Machine learning techniques applied to the coronavirus pandemic: a systematic and bibliometric analysis from January 2020 to June 2021

Latest revision as of 11:33, 15 December 2022

Abstract

During the pandemic caused by the Coronavirus (Covid-19), Machine Learning (ML) techniques can be used, among other alternatives, to detect the virus in its early stages, which would aid a fast recovery and help to ease the pressure on healthcare systems. In this study, we present a Systematic Literature Review (SLR) and a Bibliometric Analysis of ML technique applications in the Covid-19 pandemic, from January 2020 to June 2021, identifying possible unexplored gaps. In the SLR, the 117 most cited papers published during the period were analyzed and divided into four categories: 22 articles that analyzed the problem of the disease using ML techniques in an X-Ray (XR) analysis and Computed Tomography (CT) of the lungs of infected patients; 13 articles that studied the problem by addressing social network tools using ML techniques; 44 articles directly used ML techniques in forecasting problems; and 38 articles that applied ML techniques for general issues regarding the disease. The gap identified in the literature had to do with the use of ML techniques when analyzing the relationship between the human genotype and susceptibility to Covid-19 or the severity of the infection, a subject that has begun to be explored in the scientific community.

Keywords: Machine learning, coronavirus pandemic, systematic literature review, bibliometric analysis, genetic predisposition

1. Introduction

According to the World Health Organization [1], pandemic is a term used for a determined disease that rapidly spreads through diverse regions (at the continental or world level) through sustained contamination. In this respect, the gravity of the disease is not a determining factor, but rather its contagiousness and geographical proliferation.

In the last 30 years, the number of virus outbreaks has grown, proliferating diseases that affect the world. However, historical reports of pandemics stretch back to long before the twenty-first century and have been a concern of the human race for over two thousand years [2]. The main concerns include the Plague of Justinian, which occurred around 541 A.D., caused by the bacterium responsible for the Black Death of 1343, which reached its peak in 1353, also called the bubonic plague. Russian Flu, which was first reported in 1580, was the first to be documented beginning in 1889. Spanish Flu, first recorded in 1918, caused the death of 20 to 50 million people around the world [3].

Even with their biological, social, temporal, and geographical differences, pandemics usually share similar consequences, such as social chaos, changes in behavior and the spread of false information. Looking back to the past, there is an increasingly clear need to invest in and appreciate scientific research, studies, and healthcare professionals. After all, even with such a long history of pandemics, we still have to make considerable advances to ensure that this type of phenomenon does not once again have such a terribly fatal impact on humanity.

On 31 December 2019, representatives from the WHO in China were formally informed of the outbreak of a new Coronavirus disease (Covid-19), caused by the virus SARS-CoV-2, in the city of Wuhan, China [4]. The following months were marked around the world by this event that had been hitherto deemed as being of little importance. The aforementioned date marked the official beginning of the chronology of the disease that, a few weeks later, would be declared a pandemic by the WHO. Coronavirus disease is an inflammation disease, which causes respiratory ailments with reactions like a cold, fever and cough, and in progressively serious cases can result in the patient’s death. National health authorities are constantly striving to stem the spread of the virus by emphasizing the importance of wearing masks, social distancing, and hygiene.

By June 2021, over four million people had died of coronavirus, businesses had declared bankruptcy and life as it had been known changed radically. The three most affected countries so far (June 30th) were the United States (599,089 deaths), Brazil (514,092 deaths) and India (398,454 deaths) [5]. Amid so much terrible news, since late 2020, another form of counting has emerged to share attention with the victims of Covid-19, this time more optimistic: the number of people who have been given one of the vaccines now available.

With the advances in computer algorithms and Artificial Intelligence (AI), more specifically, Machine Learning (ML) techniques, the detection of this type of virus in the early stages will aid a fast recovery and help to ease the pressure on healthcare systems [6]. Covid-19 is highly contagious and spreads rapidly worldwide. Therefore, early detection is very important. Any technological tool that can provide rapid detection of a Covid-19 infection with high accuracy can be very useful to medical professionals. Covid-19 images, using techniques such as computed tomography (CT) and X-rays (XR), are very similar to other lung infections, making it difficult for medical professionals to distinguish Covid-19. Therefore, computer aided diagnostic solutions are being developed to facilitate the identification of positive Covid-19 cases [7].

The aim of this paper is to present a systematic review and a bibliometric analysis of the literature on the applications of ML techniques to a broader scope of problems related to the Covid-19. More specifically, this paper aims to: (i) analyze the number of papers with the greatest impact published from January 2020 to June 2021 due to the growing interest of the researchers in this theme; (ii) identify the journals with the highest number of papers; (iii) determine the focus of the papers; (iv) identify which ML techniques are used most by researchers; (v) identify which countries and databases were targeted by these studies; (vi) analyze which Covid-19 problems are more frequently addressed; and finally, (vii) identify the existing gaps that could yet be explored to gain a better understanding of why certain patients are so severely affected by the virus, while others are asymptomatic.

The remainder of this paper is organized as follows. Section 2 presents the theoretical background of ML applied to the Covid-19 disease. Section 3 discusses the methodological procedures, including the research terms and the flowchart used in the systematic literature review. Section 4 presents and discusses the results, i.e., the survey conducted with the researched articles. Finally, Section 5 concludes the paper and suggests directions for future research in this field.

2. Theoretical background: ML techniques for the Covid-19 disease

The Covid-19 pandemic has become the most devastating disease of the twenty-first century and has spread to all the 216 countries in the world. Despite the availability of modern and sophisticated medical treatment, the disease is spreading through more outbreaks.

According to the WHO, the number of confirmed Covid-19 cases up to 26 July 2021 was 194,080,019, with this number including 4,162,304 deaths. Up to this date, 3,694,984,437 doses of vaccines had been administered [5]. There have been many studies seeking solutions to a wide range of problems and monitoring these numbers [8]. Among these studies, those that involve the use of ML techniques should be highlighted [9].

ML techniques have been extensively used by many researchers to address health problems, of which the work of dos Santos et al. [10] should be highlighted. These researchers conducted a bibliometric analysis from 2009 to 2018 on this issue. Among the other researchers were Carroll et al. [11], who conducted a systematic review of the tools used by public health professionals with emphasis on social network analysis and geographical information systems from 1980 to 2013. Dallora et al. [12], on the other hand, conducted a systematic review regarding the application of ML techniques to the prognosis of dementia. Furthermore, Bellinger et al. [13] conducted a systematic review of the application of ML techniques to the epidemiology of air pollution.

The analysis of patients’ lung images using ML techniques, conducted by Somasekar et al. [14], has seen great progress in many directions in the field of health to provide support for subsequent medical diagnoses. The authors proposed three directions for research in the struggle against the pandemic using ML techniques: classification of X-ray images of the thorax (CXR); predicting the patient’s risk based on his characteristics (including comorbidities, initial symptoms, and vital signs for the prognosis of the disease); and forecasting the propagation of the disease and the fatality rate.

On the other hand, Shahid et al. [6], presented an analysis of the role that ML has played so far in combating the virus, mainly the aspects of triage, prediction and vaccines. The authors presented a wide-ranging study of ML techniques that can be used for this purpose. Doanvo et al. [15] reflected on the fact that since August 2020, thousands of publications involving Covid-19 have been produced. The authors commented, up to the time of their research, that these works were mainly clinical in nature, from modeling or based in the field, contrasting with studies conducted in laboratories. Furthermore, the modeling of topics indicates that publications on Covid-19 have focused on public health, notifications of an outbreak, clinical treatment, and coronavirus tests.

In their analysis, M. Li et al. [16] concluded that economic inequality increases the risk of Covid-19 transmission, considering, for instance, the per capita availability of hospital beds. Increased intake of vegetables, edible oil, protein, vitamin D and vitamin K may be associated with lower risks, while a greater alcohol intake may increase the risk of Covid-19. They also commented that age, gender, temperature, humidity, social distancing, smoking, investments in health, level of urbanization and race can influence the severity of the disease.

The present study differs from other works in that it does not focus on a single type of application, but to all the situations widely cited in the literature and seeking answers to why certain patients are so severely affected by the virus, while others are asymptomatic when affected by it.

3. Methodology

This study was based on the methodology proposed by Snyder [17] and Xiao and Watson [18] to delineate the flow of information and procedures necessary to conduct this literature review. The review was guided by the research questions presented in Section 1. The initial search criteria adopted are (“Data Mining” OR “Machine Learning”) AND (“Covid” OR “Coronavirus”).

The search was limited to original articles published in peer-reviewed journals form January 2020 to June 2021, and only in English. Three scientific databases were used: ScienceDirect; Scopus; and Web of Science. Figure 1 shows the systematic literature review flowchart.

Figure 1. Systematic literature review flowchart

Based on the initial search criteria, 1,912 articles were identified in the three scientific databases. The most frequently cited articles were then selected, resulting in a total of 130 articles, of which 10 were duplicates and three did not fit the initial search profile (the first lay outside the predefined time interval, the second was an article from the field of pharmaceutics, and the third was not an original article, but a report). These 13 articles were removed in the exclusion of duplicates and search refinement stages, leaving a total of 117 original articles whose contents were analyzed.

4. Results and discussion

The results of the study conducted in accordance with the methodology presented in Section 3 are presented here, with the 117 most cited articles from the ScienceDirect, Scopus and Web of Science databases. The systematic review of the articles is presented in Section 4.1 and the bibliometric review in Section 4.2.

4.1. Systematic literature review

Of the 117 articles analyzed, 22 are studies that used X-rays (XR) and Computed Tomography (CT) of the lungs of patients affected by Covid-19 so that, through ML techniques, they could be differentiated from other lung ailments, predicting their level of severity, and determining which measures should be taken, among other alternatives, as shown in Table 1.

Researchers Ardakani et al. [19], for example, suggested a rapid and valid method for Covid-19 diagnosis based on AI techniques. They used 1,020 CT slices from 108 patients with laboratory proven Covid-19 (Covid-19 group) and 86 patients with other atypical and viral pneumonia diseases (non-Covid-19 group). The authors used 10 known Convolutional Neural Networks (CNN) and concluded that a computer-aided diagnosis (CAD) approach based on CT images has promising potential to distinguish Covid-19 infections from other atypical and viral pneumonia diseases. Their study showed that ResNet-101 can be considered a promising model to characterize and diagnose Covid-19 infections. This model does not involve substantial costs and can be used as an adjuvant method during CT imaging in radiology departments.

Another example is the work of Cai et al. [20], who analyzed the CT quantification of Covid-19 pneumonia and how are the impacts on the assessment of disease severity through the prediction, using Random Forest Regression (RFR), of clinical outcomes in the management of Covid-19 patients. Meanwhile, the work of Chowdhury et al. [21], proposed a robust technique for automatic detection of Covid-19 pneumonia from digital chest X-ray images applying pre-trained Deep Learning (DL) algorithms while maximizing the detection accuracy. A public database was created by the authors combining several public databases and also by collecting images from recently published papers.

Table 1 provides an overview of this research niche involving ML and XR/CT. The first column contains the authors, while the second presents the focus of the study. The third column identifies the ML technique that was employed, and the fourth column shows the databases that were used.

Table 1. Papers (22) that analyzed lung XR/CT of Covid-19 patients using ML techniques

Authors (year)	Focus of the paper	ML Techniques	Databases
Anastasopoulos et al. (2020) [22]	Implement an automated software to solve the substantial increase in chest CT admissions	DL	GitHub platform with Covid-19 chest CT dataset
Ardakani et al. (2020) [19]	Develop a rapid and valid method for Covid-19 diagnosis	10 Convolutional NN	108 patients
Bharati et al. (2020) [23]	Detect lung diseases from X-ray images through VGG Data STN with CNN (VDSNet)	DL; VGG; STN; CNN	Kaggle repository
Brunese et al. (2020) [24]	Detect Covid-19 from chest X-rays	Supervised ML techniques	85 chest X-rays
Cai et al. (2020) [20]	Analyze CT quantification	RFR	99 patients from Zhejiang
Chakraborty & Mali (2021) [25]	Efficiently Interpret and segment Covid-19 radiological images.	SUFMACS	250 CT images; 250 X-Ray images
Chowdhury et al. (2020) [21]	Detect Covid-19 pneumonia from digital X-ray images	DL	Kaggle databases
Elaziz et al. (2020) [26]	Classify chest x-ray images into 2 classes: Covid-19 patient or non-Covid-19 person	New FrMEMs; modified Manta-Ray Foraging Optimization based on DE	GitHub; Qatar University; University of Dhaka
Vijay kumar et al. (2020) [27]	Use the analytics of key points from images of Covid-19 for diagnosis and predictions	GANs; DL	16 benchmark datasets
Loey et al. (2020) [28]	Detect coronavirus in chest X-ray images	GAN; DL	307 images
Saha et al. (2021) [29]	Identify Covid-19 patients by evaluating chest X-ray images through an automated detection scheme (EMCNet)	DL; RF; SVM; DT; AdaBoost	Github repository (400 chest X-ray images)
Saygılı (2021) [30]	Achieve rapid and accurate detection of Covid-19 from CT and X-ray images	k-NN; SVM; Bag of Tree; K-ELM	3 public Covid-19 data sets
Sedik et al. (2020) [31]	Present two data-augmentation models to enhance learnability of Covid-19 detection	CNN: ConvLSTM-based on DL	2 datasets consisting of X-ray and CT images
Sethy et al. (2020) [32]	Detect coronavirus infected patients using X-ray images	Deep feature; SVM	GitHub repositor (University of Montreal; 381 images)
Shiri et al. (2021) [33]	Predict Covid-19 patients using clinical data and lung/lesion radiomic features extracted from chest CT images	XGBoost	152 patients
D. Singh et al. (2020) [34]	Classify Covid-19 patients from chest CT images	MODE; ANN; ANFIS; CNN	---
Somasekar et al. (2020) [14]	Open 3 research directions in the fight against the pandemic: CXR image classification; patient risk prediction; and forecasting of disease	DCNN	---
Tamal et al. (2021) [35]	Detect Covid-19 early and rapidly from CXR	SVM; k-NN; EBM Trees	378 images
Tartaglione et al. (2020) [36]	Provide which information to expect through CXR images	DL to Covid classification of CXR images	Hospitals in Northern Italy
X. Wang et al. (2020) [37]	Develop a DL using 3D CT for Covid-19 classification and lesion localization	DL	540 patients
Waheed et al. (2020) [38]	Generate synthetic chest X-ray images	DL: CNNs	3 publicly accessible datasets
Wu et al. (2021) [39]	Improve Covid-19 diagnosis using CT	RF	Youan Hospital, Beijing.

Acronyms (alphabetical): AI (Artificial Intelligence); AF (Atrial Fibrillation); AL (Active Learning); ANFIS (adaptive neuro-fuzzy inference system); API (Application Programming Interface); AR (Auto-Regressive Process); ARIMA (Auto-Regressive Integrated Moving Average); BTM (Biterm Topic Model); CDCP (Center for Disease Control and Prevention); CART (Classification And Regression Trees); CXR (Chest X-Ray); CMC (composite Monte-Carlo); CMM (Chinese Materia Medica); CNN (Convolutional Neural Networks); CT (computed tomography); ConvLSTM (Convolutional Long Short-Term Memory); CTree (Conditional Inference Tree); CUBIST (Cubist Regression); DCNN (Deep Convolution Neural Networks); DE (Differential Evolution); DL (deep learning); DT (Decision Trees); EA (Ensemble Algorithm); EBM (Ensemble Bagged Model) Trees); ELM (Extreme Learning Machine); FrMEMs (Fractional Multichannel Exponent Moments); FRI (Fuzzy Rule Induction); GA (Genetic Algorithm); GAN (Generative Adversarial Network); GBA (Gradient Boosting Algorithm); GPR(Gaussian Process Regression); GBM (Gradient Boosted Tree Models); GIWD (Generalized Inverse Weibull distribution); GHOST (Globally Harmonized Observational Surface Treatment); HCA (Hierarchical Clustering Algorithm); IoT (Internet of Things); k-NN (k-Nearest Neighbor); K-ELM (Kernel Extreme Learning Machine); LASSO (least absolute shrinkage and selection operator); LDA (Latent Dirichlet Allocation); LSTM (Long Short-Term Memory); LR (Linear Regression); LoR (Logistic Regression); LOS (Length of Stay); LSTM (Long /Short Term Memory); LR (Linear Regression); ML (Machine Learning); MLDSP (Machine Learning with Digital Signal Processing); MLP (Multilayer perceptron); MLP-ICA (MLP-imperialist competitive algorithm); MODE (Multi-objective Differential Evolution); MNB (Multinomial Naïve Bayes); NLP (Natural Language Processing); NCBI (National Center for Biotechnology Information); NN (Neural Network); PAC (Passive Aggressive Classifier); PCR (Principal Components Regression); PDR-NML (Partial Derivative Regression and Nonlinear Machine Learning); PLS-DA (Partial Least Squares Discriminant Analysis); PLSR (Partial Least Squares Regression); PNN+cf (Polynomial Neural Network with Corrective Feedback); PR (Polynomial Regression); RF (Random Forest); RFR (Random Forest Regression); RIDGE (Ridge Regression); RT (Regression Tree); SARIMA (Seasonal Auto-Regressive Integrated Moving Average); SEIRD (Susceptible, Exposed, Infected, Recovered, and Dead); SIR (Susceptible(P-Infected-Recovered epidemiological model); SLR (Simple Linear Regression); SMOM (Social Mimic Optimization Method); SNA (social network analysis); SUFMACS (SUperpixel based Fuzzy Memetic Advanced Cuckoo Search); SVM (Support Vector Machine); SVR (Support Vector Regression); STN (Spatial Transformer Network); SVC (Support Vector Classifier); SVR (Support Vector Regression); TClustVID (Clustered Based Proposed Classification and Topics modeling Approach); TCM (traditional Chinese medicine); TWC (Topological Weighted Centroid); USDA ERS (United States Department of Agriculture, Economic Research Service); VGG (Visual Geometry Group based Neural Network); WHO (World Health Organization); VAR (Vector Autoregression); WSIDEA (Weighted Stochastic Data Envelopment Analysis); WSCC (Web of Science Core Collection); XGBoost (Extreme Gradient Boosting).

On the other hand, many works used ML techniques to analyze people’s feelings and emotions regarding the pandemic, even their impressions concerning the climate. Sentiment Analysis is an field of study that seeks useful information through the sentiments that people share on social media, such as Facebook and Twitter [40]. Sentiments can be classified as neutral, positive or negative.

Gulati et al. [41], for example, presented a comparative analysis of seven ML classifiers, such as Linear Support Vector Classifier (SVC), Perceptron, Passive Aggressive Classifier (PAC), and Logistic Regression (LoR). They used more than 72,000 tweet datasets related to Covid-19 pandemic and achieved an accuracy score higher than 98%. Haupt et al. [42] used interdisciplinary approaches to big data, ML, content analysis, and social network analysis (SNA) to characterize the communicative behavior, conversation themes, and network structures of “Liberate protest” supporters and non-supporters. For this purpose, the authors used unsupervised ML techniques and social network analysis. Praveen et al. [43] conducted their study to analyze Indian citizens’ perceptions of what causes stress, anxiety, and trauma during Covid-19. For this purpose, the authors used ML techniques, more specifically, Natural Language Processing (NLP) in 840,000 tweets. Of the 117 articles analyzed, 13 were in this line of research and are listed in Table 2.

Table 2. Works (13) related to Covid-19 that used ML techniques and social network tools

Authors (year)	Focus of the paper	ML Techniques	Databases
Abd-Alrazaq et al. (2020) [44]	Identify the topics related to Covid-19 posted by Twitter users	API; Tweepy Python library	February 2, 2020, to March 15, 2020 in public English language tweets
Gulati et al. (2021) [41]	Classify sentiment based on tweets related to Covid-19	Linear SVC; Perceptron; PAC; LoR	72,000 tweets
Gupta et al. (2021) [45]	Quantify twitter users’ perceptions regarding the effect of weather and analyze how they evolved with respect to real-world events and time.	API	166,005 English tweets; from January 23 to June 22, 2020
Haupt et al. (2021) [42]	Characterize communicative (tweets) behavior	ML techniques and SNA	API from Twitter
Hou et al. (2021) [46]	Explore public attention on social media	Text analysis; LDA	Weibo (popular microblogging site in China) from December 27, 2019 to May 31, 2020
Kabir & Madria (2021) [47]	Present tweets dataset on Covid-19 emotional responses (EMOCOV)	DL	Data of 5,000 tweets
Kyriazos et al. (2021) [48]	Model that differentiated the top 25% well-being scorers in early Covid-19 quarantine	CART; RF; CTREE	Data (1,518) were collected in a web-link posted on webpages and Facebook accounts
S. Li et al. (2020) [49]	Explore Covid-19’s impacts on mental health	DL	17,865 active Weibo users
Mackey et al. (2020) [50]	Characterize users’ conversations (tweets) associated with Covid-19 symptoms and experiences	BTM	4,492,954 tweets
Praveen et al. (2021) [43]	Analyze Indian citizens’ perception of anxiety, stress and trauma during Covid-19	Natural language	840,000 tweets
Samuel et al. (2020) [51]	Identify public sentiment (tweets) associated with the pandemic	Naïve Bayes; LR; LoR; k-NN	900,000 tweets
Satu et al. (2021) [52]	Analyze Covid-19 public tweets to extract significant sentiments	TClustVID	IEEE data portal developed by Rabindra Lamsal
Shah et al. (2021) [53]	Analyze online physician rating (OPR) to identify emerging and fading topics and sentiment trends on physician websites	NLP	55,612 OPRs of 3,430 doctors

Acronyms (alphabetical): See Table 1.

In turn, 44 of the 117 selected articles involved ML methods to predict a wide range of aspects, such as the number of patients who will be infected or intubated, the trends of the pandemic, the production of a real-time Covid-19 SEIRD (Susceptible, Exposed, Infected, Recovered, and Dead) model, and student performance.

Amar et al. [54], for example, attempted to investigate the disease to eliminate its effects and, to this end, the authors examined a real database from Egypt, from February 15, 2020, to June 15, 2020. They predicted the number of patients that would be infected and estimated the final size of the pandemic. For this purpose, they applied several regression analysis models. Burdick et al. [55] attempted to predict patients’ need for ventilation to determine a better allocation of resources and prevent emergency intubations and their associated risks. The authors analyzed 197 patients, from five USA health systems between March 24 and May 4, 2020. The patients were enrolled in the REspirAtory Decompensation for the triage of the disease: a prospective studY (READY) clinical trial. Of the 117 articles analyzed, 44, including the two already mentioned above, were in this line of research and are listed in Table 3.

Table 3. Works (44) related to Covid-19 that directly used ML techniques for prediction

Authors (year)	Focus of the paper	ML Techniques	Databases
Amar et al. (2020) [54]	Predict the number of patients that will be infected with Covid-19 in Egypt	LoR; Regression models	Egyptian Ministry of Health; February 15, 2020, to June 15, 2020
Ardabili et al. (2020) [56]	Predict the Covid-19 outbreak and the enforcement of relevant control measures	MLP; ANFIS	Worldometers website for five countries
Arvind et al. (2021) [57]	Predict future intubation among patients diagnosed with Covid-19	RF	Data from 5 hospitals within an academic healthcare system (4,087 patients)
ArunKumar et al. (2021) [58]	Forecast the epidemiological trends of the Covid-19 pandemic for top-16 countries	Time series models; ARIMA; SARIMA	John Hopkins University’s Covid-19 database
Aydin & Yurdakul (2020) [59]	Analyze the performance of countries to counter the Covid-19 outbreak	WSIDEA; k-means; HCA; RF; DT	Data from 142 countries
Ayyoubzadeh et al. (2020) [60]	Predict the incidence of Covid-19 in Iran	LR; LSTM models	Google Trends website
Ballı (2021) [61]	Identify the curve of the disease and forecast the epidemic trend	LR, MLP, RF and SVM	Data from WHO (35 weeks)
Bloise & Tancioni (2021) [62]	Exploit the provincial variability of Covid-19 cases in Italy to select the territorial predictors for the pandemic	LASSO; Elastic net model	Data from March 21, 2020 to June 3, 2020, in Italy
Burdick et al. (2020) [55]	Predict the need for ventilation for Covid-19 patients	XGBoost; DT	197 patients were enrolled in the READY (REspirAtory Decompensation study)
Buscema et al. (2020) [63]	Analyze the evolution of the Covid-19 phenomenon	TWC algorithm	Geospatial coordinates of latitude and longitude of the Italian locations where the events occurred.
Chakraborti et al. (2021) [64]	Perform the regression modelling and provide subsequent interpretation of most critical factors	RF; GBM	European Centre for Disease Prevention and Control (ECDC)
Chatterjee et al. (2020) [65]	Analyze datasets to understand the trend of Covid-19	Statistical and univariate time series	Oxford University Database
Chimmula & Zhang (2020) [66]	Forecast Covid-19 transmission	Time series; DL; LSTM networks	Johns Hopkins university; Canadian health authority
Cobre et al. (2021) [67]	Predict Covid-19 diagnosis and disease severity	ANN; DT; PLS-DA; KNN	Kaggle platform 5,643 patient samples
Ebinger et al. (2021) [68]	Predict the likelihood of prolonged LOS	3 ML models developed using DataRobot	966 patients
Fong et al. (2020) [69]	Find a forecasting model (GROOWS) from a small dataset for Covid-19 cases	PNN+cf	Archive of Chinese health authorities
Gothai et al. (2021) [70]	Predict the growth and trend of Covid-19	LR; SVM; time series	172,479 documents from Johns Hopkins University Repository
Jain et al. (2021) [71]	Predict Covid-19	SVM; Naïve Bayes; KNN; AdaBoost; GBoost; RF; ANN	B-cell dataset
Kang et al. (2021) [72]	Predict severe Covid-19 cases	ANN	151 cases of a China center
Kavadi et al. (2020) [73]	Global pandemic prediction of Covid-19	PDR-NML method	Kaggle
Khan et al. (2021) [74]	Predict the time after which the number of cases stops rising in India	DT; SVM; GPR	Ministry of Health and Family Welfare (MoHFW) on 10th June 2020
Lmater et al. (2021) [75]	Present an effective mathematical model for predicting the spread of the (Covid-19) pandemic.	SIDR model (susceptible, infected, diagnosed and recovered stages)	Epidemiological data from 4 countries: Belgium; Morocco; Netherlands; Russia
Malefors et al. (2021) [76]	Predict guest attendance during the pandemic (meal planning in Sweden)	RF; ANN	Data from 18 primary school kitchens and 16 preschool kitchens
Mojjada et al. (2020) [77]	Show the ability to predict the number of individuals who are affected by Covid-19.	LASSO; SVM; LR	Git Hub, supplied by Johns Hopkins University
Nemati et al. (2020) [78]	Predict patients’ period of stay in hospital	7 ML and statistical analysis techniques	1,182 hospitalized patients
Ong et al. (2020) [79]	Predict Covid-19 vaccine candidates	Vaxign reverse vaccinology tools	ClinicalTrials.gov database and PubMed literature
Papastefanopoulos et al. (2020) [80]	Investigate the accuracy of six time series for coronavirus to forecast active cases per population	Six time series	Kaggle; population-by-country dataset
Peng & Nagata (2020) [81]	Predict the number of Covid-19 cases for the 12 most affected countries	SVR	12 most affected countries
Pinter et al. (2020) [82]	Predict the Covid-19 pandemic for Hungary	Hybrid ML: ANFIS and MLP-ICA	Worldometer for Hungary
Pourhomayoun & Shakibi (2021) [83]	Determine the risk and predict the mortality risk of patients with Covid-19	SVM; ANN; RF; DT; LoR; KNN	2,670,000 Covid-19 patients from 146 countries
Quintero et al. (2021) [84]	Predict the SEIRD variables based on a deep dependence on them	GA; AR; ARIMA	The National Institute of Health for Colombia and the National Administrative Department of Statistics
Ribeiro et al. (2020) [85]	Develop short-term forecasting models to allow forecasting of the number of cases in the future	ARIMA; CUBIST; RF; RIDGE; SVR; SVR	Cases in Brazil up to April, 19 of 2020; 10 datasets
Santosh (2020) [86]	Develop AI-driven tools to identify Covid-19 outbreaks	AL	Multitudinal and Multimodal data
Shahid et al. (2021) [6]	Predict virus detection, spread prevention and medical assistance	survey of ML algorithms and models	---
V. Singh et al. (2020) [87]	Produce a real-time SEIR model of confirmed, deceased, and recovered Covid-19 cases.	SVM; time series	Johns Hopkins CSSE; data from January 22, 2020 to April 25, 2020
Sujath et al. (2020) [88]	Predict the spread of Covid-2019	LR; MLP; VAR	Kaggle; Indian database
Tarik et al. (2021) [89]	Predict Moroccan student performance	RF; DT; LR	Referral system
Tuli et al. (2020) [90]	Analyze and predict the growth of the epidemic	GIWD in a cloud computing platform	Our World in Data by Hannah Ritchie
Wadhwa et al. (2021) [91]	Predict the extension of lockdown in order to eradicate Covid-19 from India.	LR	Total number of cases, deaths, and recoveries all over India.
P. Wang et al. (2020) [92]	Predict epidemic trends	LoR	Johns Hopkins University, from January 22, 2020 to June 16, 2020.
Yan et al. (2020) [93]	Identify crucial predictive biomarkers of Covid-19 mortality	XGBoost	485 patients
Yadav et al. (2020) [94]	Solve 5 different tasks: I) Predict the spread of the disease; II) Analyze the growth rates; III) Predict how the pandemic will end; IV) Analyze the transmission rate; and V) Correlate the disease to the weather conditions.	SVR; SLR; PR	Data from different countries
Yeşilkanat (2020) [95]	Estimate the number of future cases for 190 countries in the world	RF	Johns Hopkins University Center for Systems Science; Engineering
Zivkovic et al. (2021) [96]	Predict the number of new coronavirus cases	ANFIS; BASSI	6 benchmark Functions

Acronyms (alphabetical): See Table 1.

Finally, the last 38 of the 117 selected articles that address general subjects involving ML techniques and Covid-19 are listed in Table 4. These include, for example, that of Di Castelnuovo et al. [97], who attempted to list those that aimed to identify the characteristics predisposing Covid-19 patients to in-hospital death. For this purpose, the authors used the data of 3,894 patients from 30 clinical centers distributed throughout Italy, who were hospitalized from February 19th to May 23rd, 2020. The authors used the RF technique to achieve their goal. They concluded that impaired renal function, elevated C-reactive protein, and advanced age were major predictors of in-hospital death.

Table 4. Works (38) related to Covid-19 that used ML techniques involving general subjects

Authors (year)	Focus of the paper	ML Techniques	Databases
Alves et al. (2021) [98]	Present understandable solutions to deal with Covid-19 screening in routine blood tests	DT Explainer and criteria graphs	608 patients; public dataset from the Albert Einstein Hospital, São Paulo
Baralić et al. (2020) [99]	Assess risks and benefits of Covid- 19 treatment with promising drug combinations: lopinavir/ritonavir and chloroquine/hydroxychloroquine+ azithromycin.	in silico toxicogenomic data-mining approach	Comparative Toxicogenomics Database
Carrillo-Larco & Castillo-Cara (2020) [100]	Clustering countries which shared profiles of the pandemic	k-means; statistical techniques	155 countries; Johns Hopkins University and others
Di Castelnuovo et al. (2020) [97]	Identify the characteristics predisposing Covid-19 patients to in-hospital death.	RF	3,894 patients hospitalized from a defined period (Italy)
Choudrie et al. (2021) [101]	Explore how ML techniques and experienced people process the online infodemic related to prevention and cure	DT; CNN	143 patients
Dandekar et al. (2020) [102]	Develop a globally applicable diagnostic Covid-19 model	SIR; NN	70 countries
Doanvo et al. (2020) [15]	Identify knowledge research Covid-19 gaps in the literature	PCA	35,281 abstracts from CORD-19
Fong et al. (2020) [103]	Gain stochastic insights into the pandemic development	CMC: DL; FRI	Empirical data from the Chinese CDCP
Godavarthi & Sowjanya (2021) [104]	Extract information from the scientific literature: text classification	KNN; MLP; XGBoost	CORD-19 dataset
Hu et al. (2021) [105]	Detect the changes in air pollutants during Covid-19 lockdown	RF models	Data from 35 sites in Beijing, from 2015 to 2020
Jamshidi et al. (2020) [106]	Present a response to combat the virus through AI	GANs; LSTM; ELM	---
Kadioglu et al. (2021) [107]	Identify compounds against three targets of Covid-19	Pharmaco strategy in silico	Chemical libraries (FDA-approved drugs; natural compound datasets; ZINC database)
Khanday et al. (2020) [108]	Detect Covid-19 through clinical text data	LoR; MNB	Data repository GitHub
Kuo & Fu (2021) [109]	Analyze demographic and environmental impact and mobility during the pandemic period	Elastic net model; PCR; PLSR; KNN; RT; RF; GBM; 2-layer ANN	New York Times; USDA ERA; gridMed; Google
Lam et al. (2021) [110]	Present a ML system capable of identifying patients who could be treated with a corticosteroid or remdesivir	GBM	893 patients
M. Li et al. (2021) [16]	Detect novel critical factors associated with Covid-19 in 154 countries and in the 50 USA states	LoR; LASSO	Johns Hopkins University
Lip et al. (2021) [111]	Identify patients with Covid-19 who are at the highest risk of developing incident AF	Inferential statistics and ML computations (LoR)	Data from April 1, 2018 to Nov 30, 2020
Loey et al. (2021) [112]	Develop a DL and classical ML for face detection	DL; DT; SVM; EA	3 datasets
Lovrić et al. (2021) [113]	Analyze improvements in air quality during the Covid-19 lockdown	RFR	Graz, Styria, Austria
Magazzino et al. (2021) [114]	Analyze the relationship between Covid-19 deaths, economic growth and air pollution	DL	--
McRae et al. (2020) [115]	Develop a decision support tool and rapid point-of-care platform to determine severity in patients with Covid-19	Statistical learning algorithm	160 patients from Wuhan, China
Malki et al. (2020) [116]	Verify the relationship between weather and Covid-19	Regressor ML models	Meteoblue website
Mele & Magazzino (2021) [117]	Explore the relationship between pollution, economic growth and Covid-19 deaths in India	Time Series approach; Stationarity and Toda-Yamamoto causality tests	Indian data from January 29 to May 18, 2020
Petetin et al. (2020) [118]	Use meteorological data to estimate the “business-as-usual” NO2 mixing ratios	GBM	GHOST
Qiang et al. (2020) [119]	Evaluate the infection risk of Covid-19 for early warning through spike protein feature	RF models	507 human origin viruses and 2,159 non-human-origin viruses
Radanliev et al. (2020) [120]	Investigate the scientific research response from the early stages of the pandemic	Statistical methods	WSCC
Randhawa et al. (2020) [121]	Use intrinsic genomic signatures to classify Covid-19 rapidly	MLDSP for genome analyses; DT	Dataset of over 5,000 unique viral genomic sequences from the NCBI
Shrock et al. (2020) [122]	Explore antiviral antibody responses across the human virome	XGBoost	232 coronavirus disease patients and 190 pre-Covid-19
X. Sun et al. (2020) [123]	Explore TCM formulae to investigate their compatibility with the CMM to understand their potential mechanisms for treatment of Covid-19	TCM; CMM	Encyclopedia of Traditional Chinese Medicine database; BATMAN-TCM database
C. L. F. Sun et al. (2020) [124]	Identify risks and vectors of infection in nursing homes	GBA	1146 NHs in Massachusetts
Swapnarekha et al. (2020) [125]	Present a state-of-the-art analysis using ML and DL methods in the diagnosis and prediction of Covid-19	ML; DL	January 23, 2020 to April, 21, 2020
S. Tiwari et al. (2020) [126]	Prepare Indian government and citizens to take control measures (SEIR)	Time Series	Kaggle (data available between January 22, 2020, and April 3, 2020, from India and China)
A. Tiwari et al. (2021) [127]	Define a Covid-19 Vulnerability Index (C19VI) for identifying and mapping counties considered vulnerable	RF	Johns Hopkins University; Centers for Disease Control and Prevention
Toğaçar et al. (2020) [128]	Detect Coronavirus	DL; SVM; SMOM	GitHub; Kaggle
Vaishya et al. (2020) [129]	Revise the effectiveness of AI techniques for Covid-19	AI techniques	PubMed, Scopus and Google Scholar datasets
W.-C. Wang et al. (2021) [130]	Develop a system for monitoring global and local community outbreaks	k-means	Johns Hopkins; data with daily infected, recovered and death cases
Yacchirema & Chura (2021) [131]	Implement a system based on IoT for saver mobility during the pandemic	SVM; DT; LoR; RF; KNN (to detect the location of people)	From portable IoT devices
Yang et al. (2020) [132]	Demonstrate control measures impact the containment of the epidemic	SEIR model	2003 SARS data

Acronyms (alphabetical): See Table 1.

4.2 Bibliometric literature review

Of the 117 articles analyzed on the theme of the use of ML techniques in the study of Covid-19, 67 (57%) are from the year 2020, and the other 50 articles (43%) are from 2021, up to the month of June.

Furthermore, of the 117 articles, 10 were published in “Chaos, Solitons and Fractals” and eight in “Computers in Biology and Medicine”. The top 54% of journals with the highest number of publications are presented in Figure 2.

Figure 2. Top 54% of journals in the selected papers

The two main ML techniques identified by the bibliometric analysis were Random Forest (RF) and Deep Learning (DL). The most used methods, employed in 61% of the publications, are presented in Figure 3.

Figure 3. Top 61% of publications with the most used methods

It is noticed the predominance of classical algorithms, such as RF, SVM, and DT, in addition to modern techniques, such as DL and CNN. This shows that classical methods still have space in current scientific research, even in new applications, as in the case of Covid-19.

Finally, considering the nationality of the authors, the USA and India drew with 30 researchers each, followed by China, with 20 researchers. Figure 4 shows 70% of the most frequent nationalities of the authors of the 117 articles.

Figure 4. Top 70% of authors' nationality

The first three positions, in relation to nationality, refer to the two countries with the highest number of cases of Covid-19 (USA and India) and the country where the virus was identified (China).

5. Concluding Remarks

The aim of the present study was to conduct a systematic and bibliometric review of the articles involving the protocol shown in Figure 2 which, being the most frequently cited in the literature, set out to answer, among other questions, why certain patients are severely affected by the virus while others are asymptomatic. M. Li et al. [16], for example, commented that age, sex, temperature, humidity, social distancing, smoking, investments in health, level of urbanization and race can influence the severity of the disease. On the other hand, Di Castelnuovo et al. [97] concluded that impaired renal function, elevated C-reactive protein and advanced age were major predictors of in-hospital death. However, there are many patients who fit these specific conditions, but who present different degrees of aggravation of the disease.

We firmly believe that the answer to this question is directly found in very recent studies that reveal a possible genetic predisposition to serious cases of Covid-19 [133,134,135]. Researchers discovered that more severe cases of the disease are associated with the low performance of molecules that identify the virus and are inherited from parents: class I human leukocyte antigen (HLA-I) molecules represent the group of molecules responsible for identifying and distinguishing everything that is in the body and what is not. Six HLA-I molecules, found on the surface of all cells, form a unique set for each individual, which is determined by the genes received from the parents [135]. In other words, it is very important to investigate whether there is a direct link between the seriousness of the disease and the performance of HLA-I in the identification of Sars-CoV-2.

Therefore, it would be very interesting and promising to analyze the genotype of patients who have suffered Covid-19 and healthy people, employing ML techniques and classifying patients, for instance, as serious, moderate, or mild cases.

Funding sources

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. It is part of a larger set of projects that have been funded by the Brazilian Coordination for the Improvement of Higher Education Personnel (CAPES) and the Brazilian National Council for Scientific and Technological Development (CNPq) for the research funding.

References

[1] WHO. Coronavirus disease (COVID-19) pandemic. Retrieved from https://www.who.int/emergencies/diseases/novel-coronavirus-2019, 2021.

[2] Piret J., Boivin G. Pandemics throughout history. Front. Microbiol., 15 January, 2021.

[3] Huremović D. Brief history of pandemics (Pandemics Throughout History). Psychiatry of Pandemics, Springer International Publishing, Cham, pp. 7–35, 2019.

[4] Carvalho T., Krammer F., Iwasaki A. The first 12 months of COVID-19: a timeline of immunological insights. Nat. Rev. Immunol., 21(4):245–256, 2021.

[5] WHO. WHO Coronavirus (COVID-19) Dashboard. Retrieved from https://covid19.who.int/, 2021.

[6] Shahid O., Nasajpour M., Pouriyeh S., et al. Machine learning research towards combating COVID-19: Virus detection, spread prevention, and medical assistance. J. Biomed. Inform., 117:103751, 2021.

[7] Prabha B., Kaur S., Singh J., Nandankar P., Kumar Jain S., Pallathadka H. Intelligent predictions of Covid disease based on lung CT images using machine learning strategy. Mater. Today Proc., Online July 2021.

[8] Haridy S., Maged A., Baker A.W., Shamsuzzaman M., Bashir H., Xie M. Monitoring scheme for early detection of coronavirus and other respiratory virus outbreaks. Comput. Ind. Eng., 156:107235, 2021.

[9] Waleed Salehi A., Baglat P., Gupta G. Review on machine and deep learning models for the detection and prediction of Coronavirus. Mater. Today Proc., 33:3896–3901, 2020.

[10] dos Santos B.S., Steiner M.T.A., Fenerich A.T., Lima R.H.P. Data mining and machine learning techniques applied to public health problems: A bibliometric analysis from 2009 to 2018. Comput. Ind. Eng., 138:106120, 2019.

[11] Carroll L.N., Au A.P., Detwiler L.T., Fu T., Painter I.S., Abernethy N.F. Visualization and analytics tools for infectious disease epidemiology: A systematic review. J. Biomed. Inform., 51:287–298, 2014.

[12] Dallora A.L., Eivazzadeh S., Mendes E., Berglund J., Anderberg P. Prognosis of dementia employing machine learning and microsimulation techniques: A systematic literature review. Procedia Comput. Sci., 100:480–488, 2016.

[13] Bellinger C., Mohomed Jabbar M.S., Zaïane O., Osornio-Vargas A. A systematic review of data mining and machine learning for air pollution epidemiology. BMC Public Health, 17(1):907, 2017.

[14] Somasekar J., Pavan Kumar P., Sharma A., Ramesh G. Machine learning and image analysis applications in the fight against COVID-19 pandemic: Datasets, research directions, challenges and opportunities. Mater. Today Proc., 3–6, 2020.

[15] Doanvo A., Qian X., Ramjee D., Piontkivska H., Desai A., Majumder M. Machine Learning Maps Research Needs in COVID-19 Literature. Patterns, 1(9):100123, 2020.

[16] Li M., Zhang Z., Cao W., et al. Identifying novel factors associated with COVID-19 transmission and fatality using the machine learning approach. Sci. Total Environ., 764(639):142810, 2021.

[17] Snyder H. Literature review as a research methodology: An overview and guidelines. J. Bus. Res., 104:333–339, 2019.

[18] Xiao Y., Watson M. Guidance on conducting a systematic literature review. J. Plan. Educ. Res., 39(1):93–112, 2019.

[19] Ardakani A.A., Kanafi A.R., Acharya U.R., Khadem N., Mohammadi A. Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks. Comput. Biol. Med., 121:103795, 2020.

[20] Cai W., Liu T., Xue X., et al. CT Quantification and machine-learning models for assessment of disease severity and prognosis of COVID-19 patients. Acad. Radiol., 27(12):1665–1678, 2020.

[21] Chowdhury M.E.H., Rahman T., Khandakar A., et al. Can AI Help in Screening Viral and COVID-19 Pneumonia? IEEE Access, 8:132665–132676, 2020.

[22] Anastasopoulos C., Weikert T., Yang S., et al. Development and clinical implementation of tailored image analysis tools for COVID-19 in the midst of the pandemic: The synergetic effect of an open, clinically embedded software development platform and machine learning. Eur. J. Radiol., 131:109233, 2020.

[23] Bharati S., Podder P., Mondal M.R.H. Hybrid deep learning for detecting lung diseases from X-ray images. Informatics Med. Unlocked, 20:100391, 2020.

[24] Brunese L., Martinelli F., Mercaldo F., Santone A. Machine learning for coronavirus covid-19 detection from chest x-rays. Procedia Comput. Sci., 176:2212–2221, 2020.

[25] Chakraborty S., Mali K. SUFMACS: A machine learning-based robust image segmentation framework for COVID-19 radiological image interpretation. Expert Syst. Appl., 178:115069, 2021.

[26] Elaziz M.A., Hosny K.M., Salah A., Darwish M.M., Lu S., Sahlol A.T. New machine learning method for image-based diagnosis of COVID-19. PLoS One, 15(6):e0235187, 2020.

[27] Vijay kumar J., Harshavardhan A., Bhukya H., Krishna Prasad A.V. Advanced machine learning-based analytics on COVID-19 data using generative adversarial networks. Mater. Today Proc., 2020.

[28] Loey M., Smarandache F., M. Khalifa N.E. Within the lack of chest COVID-19 X-ray dataset: A novel detection model based on GAN and deep transfer learning. Symmetry (Basel), 12(4):651, 2020.

[29] Saha P., Sadi M.S., Islam M.M. EMCNet: Automated COVID-19 diagnosis from X-ray images using convolutional neural network and ensemble of machine learning classifiers. Informatics Med. Unlocked, 22:100505, 2021.

[30] Saygılı A. A new approach for computer-aided detection of coronavirus (COVID-19) from CT and X-ray images using machine learning methods. Appl. Soft Comput., 105:107323, 2021.

[31] Sedik A., Iliyasu A.M., Abd El-Rahiem B., et al. Deploying machine and deep learning models for efficient data-augmented detection of COVID-19 infections. Viruses, 12(7):769, 2020.

[32] Sethy P.K., Behera S.K., Ratha P.K., Biswas P. Detection of coronavirus disease (COVID-19) based on deep features and support vector machine. Int. J. Math. Eng. Manag. Sci., 5(4):643–651, 2020.

[33] Shiri I., Sorouri M., Geramifar P., et al. Machine learning-based prognostic modeling using clinical data and quantitative radiomic features from chest CT images in COVID-19 patients. Comput. Biol. Med., 132:104304, 2021.

[34] Singh D., Kumar V., Vaishali, Kaur M. Classification of COVID-19 patients from chest CT images using multi-objective differential evolution–based convolutional neural networks. Eur. J. Clin. Microbiol. Infect. Dis., 39(7):1379–1389, 2020.

[35] Tamal M., Alshammari M., Alabdullah M., Hourani R., Alola H.A., Hegazi T.M. An integrated framework with machine learning and radiomics for accurate and rapid early diagnosis of COVID-19 from Chest X-ray. Expert Syst. Appl., 180:115152, 2021.

[36] Tartaglione E., Barbano C.A., Berzovini C., Calandri M., Grangetto M. Unveiling COVID-19 from CHEST X-Ray with deep learning: A hurdles race with small data. Int. J. Environ. Res. Public Health, 17(18):6933, 2020.

[37] Wang X., Deng X., Fu Q., et al. A weakly-supervised framework for COVID-19 classification and lesion localization from chest CT. IEEE Trans. Med. Imaging, 39(8):2615–2625, 2020.

[38] Waheed A., Goyal M., Gupta D., Khanna A., Al-Turjman F., Pinheiro P.R. CovidGAN: Data augmentation using auxiliary classifier GAN for improved Covid-19 detection. IEEE Access, 8:91916–91923, 2020.

[39] Wu Z., Li L., Jin R., et al. Texture feature-based machine learning classifier could assist in the diagnosis of COVID-19. Eur. J. Radiol., 137:109602, 2021.

[40] Çalı S., Balaman Ş.Y. Improved decisions for marketing, supply and purchasing: Mining big data through an integration of sentiment analysis and intuitionistic fuzzy multi criteria assessment. Comput. Ind. Eng., 129:315–332, 2019.

[41] Gulati K., Saravana Kumar S., Sarath Kumar Boddu R., Sarvakar K., Kumar Sharma D., Nomani M.Z.M. Comparative analysis of machine learning-based classification models using sentiment classification of tweets related to COVID-19 pandemic. Mater. Today Proc., 51(1):38-41, 2022.

[42] Haupt M.R., Jinich-Diamant A., Li J., Nali M., Mackey T.K. Characterizing twitter user topics and communication network dynamics of the “Liberate” movement during COVID-19 using unsupervised machine learning and social network analysis. Online Soc. Networks Media, 21:100114, 2021.

[43] Praveen S.V., Ittamalla R., Deepak G. Analyzing Indian general public’s perspective on anxiety, stress and trauma during Covid-19 - A machine learning study of 840,000 tweets. Diabetes Metab. Syndr. Clin. Res. Rev., 15(3):667–671, 2021.

[44] Abd-Alrazaq A., Alhuwail D., Househ M., Hamdi M., Shah Z. Top concerns of tweeters during the COVID-19 pandemic: Infoveillance study. J. Med. Internet Res., 22(4):e19016, 2020.

[45] Gupta M., Bansal A., Jain B., Rochelle J., Oak A., Jalali M.S. Whether the weather will help us weather the COVID-19 pandemic: Using machine learning to measure twitter users’ perceptions. Int. J. Med. Inform., 145:104340, 2021.

[46] Hou K., Hou T., Cai L. Public attention about COVID-19 on social media: An investigation based on data mining and text analysis. Pers. Individ. Dif., 175:110701, 2021.

[47] Kabir M.Y., Madria S. EMOCOV: Machine learning for emotion detection, analysis and visualization using COVID-19 tweets. Online Soc. Networks Media, 23:100135, 2021.

[48] Kyriazos T., Galanakis M., Karakasidou E., Stalikas A. Early COVID-19 quarantine: A machine learning approach to model what differentiated the top 25% well-being scorers. Pers. Individ. Dif., 181:110980, 2021.

[49] Li S., Wang Y., Xue J., Zhao N., Zhu T. The impact of COVID-19 epidemic declaration on psychological consequences: A study on active weibo users. Int. J. Environ. Res. Public Health, 17(6):2032, 2020.

[50] Mackey T., Purushothaman V., Li J., et al. Machine learning to detect self-reporting of symptoms, testing access, and recovery associated with COVID-19 on twitter: Retrospective big data infoveillance study. JMIR Public Heal. Surveill., 6(2):e19509, 2020.

[51] Samuel J., Ali G.G.M.N., Rahman M.M., Esawi E., Samuel Y. COVID-19 public sentiment insights and machine learning for tweets classification. Information, 11(6):314, 2020.

[52] Satu M.S., Khan M.I., Mahmud M., et al. TClustVID: A novel machine learning classification model to investigate topics and sentiment in COVID-19 tweets. Knowledge-Based Syst., 226:107126, 2021.

[53] Shah A.M., Yan X., Qayyum A., Naqvi R.A., Shah S.J. Mining topic and sentiment dynamics in physician rating websites during the early wave of the COVID-19 pandemic: Machine learning approach. Int. J. Med. Inform., 149:104434, 2021.

[54] Amar L.A., Taha A.A., Mohamed M.Y. Prediction of the final size for COVID-19 epidemic using machine learning: A case study of Egypt. Infect. Dis. Model., 5:622–634, 2020.

[55] Burdick H., Lam C., Mataraso S., et al. Prediction of respiratory decompensation in Covid-19 patients using machine learning: The READY trial. Comput. Biol. Med., 124:103949, 2020.

[56] Ardabili S.F., Mosavi A., Ghamisi P., et al. COVID-19 outbreak prediction with machine learning. Algorithms, 13(10):249, 2020.

[57] Arvind V., Kim J.S., Cho B.H., Geng E., Cho S.K. Development of a machine learning algorithm to predict intubation among hospitalized patients with COVID-19. J. Crit. Care, 62:25–30, 2021.

[58] ArunKumar K.E., Kalaga D. V., Sai Kumar C.M., Chilkoor G., Kawaji M., Brenza T.M. Forecasting the dynamics of cumulative COVID-19 cases (confirmed, recovered and deaths) for top-16 countries using statistical machine learning models: Auto-Regressive Integrated Moving Average (ARIMA) and Seasonal Auto-Regressive Integrated Moving Averag. Appl. Soft Comput., 103:107161, 2021.

[59] Aydin N., Yurdakul G. Assessing countries’ performances against COVID-19 via WSIDEA and machine learning algorithms. Appl. Soft Comput., 97:106792, 2020.

[60] Ayyoubzadeh S.M., Ayyoubzadeh S.M., Zahedi H., Ahmadi M., R Niakan Kalhori S. Predicting COVID-19 incidence through analysis of Google trends data in Iran: Data mining and deep learning pilot study. JMIR Public Heal. Surveill., 6(2):e18828, 2020.

[61] Ballı S. Data analysis of Covid-19 pandemic and short-term cumulative case forecasting using machine learning time series methods. Chaos, Solitons & Fractals, 142:110512, 2021.

[62] Bloise F., Tancioni M. Predicting the spread of COVID-19 in Italy using machine learning: Do socio-economic factors matter? Struct. Chang. Econ. Dyn., 56:310–329, 2021.

[63] Buscema P.M., Della Torre F., Breda M., Massini G., Grossi E. COVID-19 in Italy and extreme data mining. Phys. A Stat. Mech. its Appl., 557:124991, 2020.

[64] Chakraborti S., Maiti A., Pramanik S., et al. Evaluating the plausible application of advanced machine learnings in exploring determinant factors of present pandemic: A case for continent specific COVID-19 analysis. Sci. Total Environ., 765:142723, 2021.

[65] Chatterjee A., Gerdes M.W., Martinez S.G. Statistical explorations and univariate timeseries analysis on COVID-19 datasets to understand the trend of disease spreading and death. Sensors, 20(11):3089, 2020.

[66] Chimmula V.K.R., Zhang L. Time series forecasting of COVID-19 transmission in Canada using LSTM networks. Chaos, Solitons & Fractals, 135:109864, 2020.

[67] Cobre A. de F., Stremel D.P., Noleto G.R., et al. Diagnosis and prediction of COVID-19 severity: can biochemical tests and machine learning be used as prognostic indicators? Comput. Biol. Med., 134:104531, 2021.

[68] Ebinger J., Wells M., Ouyang D., et al. A machine learning algorithm predicts duration of hospitalization in COVID-19 patients. Intell. Med., 5:100035, 2021.

[69] Fong S.J., Li G., Dey N., Crespo R.G., Herrera-Viedma E. Finding an accurate early forecasting model from small dataset: A case of 2019-nCoV novel coronavirus outbreak. Int. J. Interact. Multimed. Artif. Intell., 6(1):132, 2020.

[70] Gothai E., Thamilselvan R., Rajalaxmi R.R., Sadana R.M., Ragavi A., Sakthivel R. Prediction of COVID-19 growth and trend using machine learning approach. Mater. Today Proc., April 2021.

[71] Jain N., Jhunthra S., Garg H., et al. Prediction modelling of COVID using machine learning methods from B-cell dataset. Results Phys., 21:103813, 2021.

[72] Kang J., Chen T., Luo H., Luo Y., Du G., Jiming-Yang M. Machine learning predictive model for severe COVID-19. Infect. Genet. Evol., 90:104737, 2021.

[73] Kavadi D.P., Patan R., Ramachandran M., Gandomi A.H. Partial derivative nonlinear global pandemic machine learning prediction of COVID 19. Chaos, Solitons & Fractals, 139:110056, 2020.

[74] Khan F.M., Kumar A., Puppala H., Kumar G., Gupta R. Projecting the criticality of COVID-19 transmission in India using GIS and machine learning methods. J. Saf. Sci. Resil., 2(2):50–62, 2021.

[75] Lmater M.A., Eddabbah M., Elmoussaoui T., Boussaa S. Modelization of Covid-19 pandemic spreading: A machine learning forecasting with relaxation scenarios of countermeasures. J. Infect. Public Health, 14(4):468–473, 2021.

[76] Malefors C., Secondi L., Marchetti S., Eriksson M. Food waste reduction and economic savings in times of crisis: The potential of machine learning methods to plan guest attendance in Swedish public catering during the Covid-19 pandemic. Socioecon. Plann. Sci., 82(A):101041, 2022.

[77] Mojjada R.K., Yadav A., Prabhu A.V., Natarajan Y. Machine learning models for covid-19 future forecasting. Mater. Today Proc., December 2020.

[78] Nemati M., Ansary J., Nemati N. Machine-learning approaches in COVID-19 survival analysis and discharge-time likelihood prediction using clinical data. Patterns, 1(5):100074, 2020.

[79] Ong E., Wong M.U., Huffman A., He Y. COVID-19 coronavirus vaccine design using reverse vaccinology and machine learning. Front. Immunol., 11(July):1581, 2020.

[80] Papastefanopoulos V., Linardatos P., Kotsiantis S. COVID-19: A comparison of time series methods to forecast percentage of active cases per population. Appl. Sci., 10(11):3880, 2020.

[81] Peng Y., Nagata M.H. An empirical overview of nonlinearity and overfitting in machine learning using COVID-19 data. Chaos, Solitons & Fractals, 139:110055, 2020.

[82] Pinter G., Felde I., Mosavi A., Ghamisi P., Gloaguen R. COVID-19 pandemic prediction for Hungary; A hybrid machine learning approach. SSRN Electron. J., 8(6):890, 2020.

[83] Pourhomayoun M., Shakibi M. Predicting mortality risk in patients with COVID-19 using machine learning to help medical decision-making. Smart Heal., 20:100178, 2021.

[84] Quintero Y., Ardila D., Camargo E., Rivas F., Aguilar J. Machine learning models for the prediction of the SEIRD variables for the COVID-19 pandemic based on a deep dependence analysis of variables. Comput. Biol. Med., 134:104500, 2021.

[85] Ribeiro M.H.D.M., da Silva R.G., Mariani V.C., Coelho L. dos S. Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for Brazil. Chaos, Solitons & Fractals, 135:109853, 2020.

[86] Santosh K.C. AI-driven tools for coronavirus outbreak: Need of active learning and cross-population train/test models on multitudinal/multimodal data. J. Med. Syst., 44(5):93, 2020.

[87] Singh V., Poonia R.C., Kumar S., et al. Prediction of COVID-19 corona virus pandemic based on time series data using support vector machine. J. Discret. Math. Sci. Cryptogr., 23(8):1583–1597, 2020.

[88] Sujath R., Chatterjee J.M., Hassanien A.E. A machine learning forecasting model for COVID-19 pandemic in India. Stoch. Environ. Res. Risk Assess., 34(7):959–972, 2020.

[89] Tarik A., Aissa H., Yousef F. Artificial intelligence and machine learning to predict student performance during the COVID-19. Procedia Comput. Sci., 184:835–840, 2021.

[90] Tuli S., Tuli S., Tuli R., Gill S.S. Predicting the growth and trend of COVID-19 pandemic using machine learning and cloud computing. Internet of Things, 11:100222, 2020.

[91] Wadhwa P., Aishwarya, Tripathi A., Singh P., Diwakar M., Kumar N. Predicting the time period of extension of lockdown due to increase in rate of COVID-19 cases in India using machine learning. Mater. Today Proc., 37(Part 2):2617–2622, 2021.

[92] Wang P., Zheng X., Li J., Zhu B. Prediction of epidemic trends in COVID-19 with logistic model and machine learning technics. Chaos, Solitons & Fractals, 139:110058, 2020.

[93] Yan L., Zhang H.-T., Goncalves J., et al. An interpretable mortality prediction model for COVID-19 patients. Nat. Mach. Intell., 2(5):283–288, 2020.

[94] Yadav M., Perumal M., Srinivas M. Analysis on novel coronavirus (COVID-19) using machine learning methods. Chaos, Solitons & Fractals, 139:110050, 2020.

[95] Yeşilkanat C.M. Spatio-temporal estimation of the daily cases of COVID-19 in worldwide using random forest machine learning algorithm. Chaos, Solitons & Fractals, 140:110210, 2020.

[96] Zivkovic M., Bacanin N., Venkatachalam K., et al. COVID-19 cases prediction by using hybrid machine learning and beetle antennae search approach. Sustain. Cities Soc., 66:102669, 2021.

[97] Di Castelnuovo A., Bonaccio M., Costanzo S., et al. Common cardiovascular risk factors and in-hospital mortality in 3,894 patients with COVID-19: survival analysis and machine learning-based findings from the multicentre Italian CORIST Study. Nutr. Metab. Cardiovasc. Dis., 30(11):1899–1913, 2020.

[98] Alves M.A., Castro G.Z., Oliveira B.A.S., et al. Explaining machine learning based diagnosis of COVID-19 from routine blood tests with decision trees and criteria graphs. Comput. Biol. Med., 132:104335, 2021.

[99] Baralić K., Jorgovanović D., Živančević K., et al. Safety assessment of drug combinations used in COVID-19 treatment: in silico toxicogenomic data-mining approach. Toxicol. Appl. Pharmacol., 406:115237, 2020.

[100] Carrillo-Larco R.M., Castillo-Cara M. Using country-level variables to classify countries according to the number of confirmed COVID-19 cases: An unsupervised machine learning approach. Wellcome Open Res., 5:56, 2020.

[101] Choudrie J., Banerjee S., Kotecha K., Walambe R., Karende H., Ameta J. Machine learning techniques and older adults processing of online information and misinformation: A covid 19 study. Comput. Human Behav., 119:106716, 2021.

[102] Dandekar R., Rackauckas C., Barbastathis G. A machine learning-aided global diagnostic and comparative tool to assess effect of quarantine control in COVID-19 spread. Patterns, 1(9):100145, 2020.

[103] Fong S.J., Li G., Dey N., Crespo R.G., Herrera-Viedma E. Composite Monte Carlo decision making under high uncertainty of novel coronavirus epidemic using hybridized deep learning and fuzzy rule induction. Appl. Soft Comput., 93:106282, 2020.

[104] Godavarthi D., Sowjanya M. Classification of covid related articles using machine learning. Mater. Today Proc., February 2021.

[105] Hu J., Pan Y., He Y., et al. Changes in air pollutants during the COVID-19 lockdown in Beijing: Insights from a machine-learning technique and implications for future control policy. Atmos. Ocean. Sci. Lett., 14(4):100060, 2021.

[106] Jamshidi M., Lalbakhsh A., Talla J., et al. Artificial intelligence and COVID-19: deep learning approaches for diagnosis and treatment. IEEE Access, 8:109581–109595, 2020.

[107] Kadioglu O., Saeed M., Greten H.J., Efferth T. Identification of novel compounds against three targets of SARS CoV-2 coronavirus by combined virtual screening and supervised machine learning. Comput. Biol. Med., 133:104359, 2021.

[108] Khanday A.M.U.D., Rabani S.T., Khan Q.R., Rouf N., Mohi Ud Din M. Machine learning based approaches for detecting COVID-19 using clinical text data. Int. J. Inf. Technol., 12(3):731–739, 2020.

[109] Kuo C.-P., Fu J.S. Evaluating the impact of mobility on COVID-19 pandemic with machine learning hybrid predictions. Sci. Total Environ., 758:144151, 2021.

[110] Lam C., Siefkas A., Zelin N.S., et al. Machine learning as a precision-medicine approach to prescribing COVID-19 pharmacotherapy with remdesivir or corticosteroids. Clin. Ther., 43(5):871-885, 2021.

[111] Lip G.Y.H., Genaidy A., Tran G., Marroquin P., Estes C. Incident atrial fibrillation and its risk prediction in patients developing COVID-19: A machine learning based algorithm approach. Eur. J. Intern. Med., 91:53-58, 2021.

[112] Loey M., Manogaran G., Taha M.H.N., Khalifa N.E.M. A hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the COVID-19 pandemic. Measurement, 167:108288, 2021.

[113] Lovrić M., Pavlović K., Vuković M., Grange S.K., Haberl M., Kern R. Understanding the true effects of the COVID-19 lockdown on air pollution by means of machine learning. Environ. Pollut., 274:115900, 2021.

[114] Magazzino C., Mele M., Sarkodie S.A. The nexus between COVID-19 deaths, air pollution and economic growth in New York state: Evidence from Deep Machine Learning. J. Environ. Manage., 286:112241, 2021.

[115] McRae M.P., Simmons G.W., Christodoulides N.J., et al. Clinical decision support tool and rapid point-of-care platform for determining disease severity in patients with COVID-19. Lab Chip, 20(12):2075–2085, 2020.

[116] Malki Z., Atlam E.-S., Hassanien A.E., Dagnew G., Elhosseini M.A., Gad I. Association between weather data and COVID-19 pandemic predicting mortality rate: Machine learning approaches. Chaos, Solitons & Fractals, 138:110137, 2020.

[117] Mele M., Magazzino C. Pollution, economic growth, and COVID-19 deaths in India: a machine learning evidence. Environ. Sci. Pollut. Res., 28(3):2669–2677, 2021.

[118] Petetin H., Bowdalo D., Soret A., et al. Meteorology-normalized impact of the COVID-19 lockdown upon NO<sub>2</sub> pollution in Spain. Atmos. Chem. Phys., 20(18):11119–11141, 2020.

[119] Qiang X.-L., Xu P., Fang G., Liu W.-B., Kou Z. Using the spike protein feature to predict infection risk and monitor the evolutionary dynamic of coronavirus. Infect. Dis. Poverty, 9(1):33, 2020.

[120] Radanliev P., De Roure D., Walton R. Data mining and analysis of scientific research data records on Covid-19 mortality, immunity, and vaccine development - In the first wave of the Covid-19 pandemic. Diabetes Metab. Syndr. Clin. Res. Rev., 14(5):1121–1132, 2020.

[121] Randhawa G.S., Soltysiak M.P.M., El Roz H., de Souza C.P.E., Hill K.A., Kari L. Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study. PLoS One, 15(4):e0232391, 2020.

[122] Shrock E., Fujimura E., Kula T., et al. Viral epitope profiling of COVID-19 patients reveals cross-reactivity and correlates of severity. Science, 370(6520):eabd4250, 2020.

[123] Sun X., Jiang J., Wang Y., Liu S. Exploring the potential therapeutic effect of traditional Chinese medicine on coronavirus disease 2019 (COVID-19) through a combination of data mining and network pharmacology analysis. Eur. J. Integr. Med., 40:101242, 2020.

[124] Sun C.L.F., Zuccarelli E., Zerhouni E.G.A., et al. Predicting coronavirus disease 2019 infection risk and related risk drivers in nursing homes: A machine learning approach. J. Am. Med. Dir. Assoc., 21(11):1533-1538.e6, 2020.

[125] Swapnarekha H., Behera H.S., Nayak J., Naik B. Role of intelligent computing in COVID-19 prognosis: A state-of-the-art review. Chaos, Solitons & Fractals, 138:109947, 2020.

[126] Tiwari S., Kumar S., Guleria K. Outbreak trends of coronavirus disease–2019 in India: A prediction. Disaster Med. Public Health Prep., 14(5):e33–e38, 2020.

[127] Tiwari A., Dadhania A. V., Ragunathrao V.A.B., Oliveira E.R.A. Using machine learning to develop a novel COVID-19 Vulnerability Index (C19VI). Sci. Total Environ., 773:145650, 2021.

[128] Toğaçar M., Ergen B., Cömert Z. COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches. Comput. Biol. Med., 121:103805, 2020.

[129] Vaishya R., Javaid M., Khan I.H., Haleem A. Artificial Intelligence (AI) applications for COVID-19 pandemic. Diabetes Metab. Syndr. Clin. Res. Rev., 14(4):337–339, 2020.

[130] Wang W.-C., Lin T.-Y., Chiu S.Y.-H., et al. Classification of community-acquired outbreaks for the global transmission of COVID-19: Machine learning and statistical model analysis. J. Formos. Med. Assoc., 120:S26–S37, 2021.

[131] Yacchirema D., Chura A. SafeMobility: An IoT- based System for safer mobility using machine learning in the age of COVID-19. Procedia Comput. Sci., 184:524–531, 2021.

[132] Yang Z., Zeng Z., Wang K., et al. Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions. J. Thorac. Dis., 12(3):165–174, 2020.

[133] COVID-19 Host Genetics Initiative. Mapping the human genetic architecture of COVID-19. Nature, 600:472–477, 2021.

[134] Pairo-Castineira E., Clohisey S., Klaric L., et al. Genetic mechanisms of critical illness in COVID-19. Nature, 591(7848):92–98, 2021.

[135] Shkurnikov M., Nersisyan S., Jankevic T., et al. Association of HLA class I genotypes with severity of coronavirus disease-19. Front. Immunol., 12:641900, 2021.

Latest revision as of 11:33, 15 December 2022

Abstract

1. Introduction

2. Theoretical background: ML techniques for the Covid-19 disease

3. Methodology

4. Results and discussion

4.1. Systematic literature review

4.2 Bibliometric literature review

5. Concluding Remarks

Funding sources

References

Document information

Document Score

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Keywords

@@ Line 46: / Line 46: @@
 '''Machine Learning Techniques applied to the Coronavirus pandemic: a systematic and bibliometric analysis from January 2020 to June 2021'''</div>
 -->
+==Abstract==
-'''Abstract: '''During the pandemic caused by the Coronavirus (Covid-19), Machine Learning (ML) techniques can be used, among other alternatives, to detect the virus in its early stages, which would aid a fast recovery and help to ease the pressure on healthcare systems. In this study, we present a Systematic Literature Review (SLR) and a Bibliometric Analysis of ML technique applications in the Covid-19 pandemic, from January 2020 to June 2021, identifying possible unexplored gaps. In the SLR, the 117 most cited papers published during the period were analyzed and divided into four categories: 22 articles that analyzed the problem of the disease using ML techniques in an X-Ray (XR) analysis and Computed Tomography (CT) of the lungs of infected patients; 13 articles that studied the problem by addressing social network tools using ML techniques; 44 articles directly used ML techniques in forecasting problems; and 38 articles that applied ML techniques for general issues regarding the disease. The gap identified in the literature had to do with the use of ML techniques when analyzing the relationship between the human genotype and susceptibility to Covid-19 or the severity of the infection, a subject that has begun to be explored in the scientific community.
+During the pandemic caused by the Coronavirus (Covid-19), Machine Learning (ML) techniques can be used, among other alternatives, to detect the virus in its early stages, which would aid a fast recovery and help to ease the pressure on healthcare systems. In this study, we present a Systematic Literature Review (SLR) and a Bibliometric Analysis of ML technique applications in the Covid-19 pandemic, from January 2020 to June 2021, identifying possible unexplored gaps. In the SLR, the 117 most cited papers published during the period were analyzed and divided into four categories: 22 articles that analyzed the problem of the disease using ML techniques in an X-Ray (XR) analysis and Computed Tomography (CT) of the lungs of infected patients; 13 articles that studied the problem by addressing social network tools using ML techniques; 44 articles directly used ML techniques in forecasting problems; and 38 articles that applied ML techniques for general issues regarding the disease. The gap identified in the literature had to do with the use of ML techniques when analyzing the relationship between the human genotype and susceptibility to Covid-19 or the severity of the infection, a subject that has begun to be explored in the scientific community.
-'''Keywords:''' Machine Learning; Coronavirus Pandemic; Systematic Literature Review; Bibliometric Analysis; Genetic Predisposition.
+'''Keywords''': Machine learning, coronavirus pandemic, systematic literature review, bibliometric analysis, genetic predisposition
-=1. Introduction=
+==1. Introduction==
 According to the World Health Organization [1], pandemic is a term used for a determined disease that rapidly spreads through diverse regions (at the continental or world level) through sustained contamination. In this respect, the gravity of the disease is not a determining factor, but rather its contagiousness and geographical proliferation.
@@ Line 69: / Line 70: @@
 The remainder of this paper is organized as follows. Section 2 presents the theoretical background of ML applied to the Covid-19 disease. Section 3 discusses the methodological procedures, including the research terms and the flowchart used in the systematic literature review. Section 4 presents and discusses the results, i.e., the survey conducted with the researched articles. Finally, Section 5 concludes the paper and suggests directions for future research in this field.
-=2. Theoretical background: ML techniques for the Covid-19 disease=
+==2. Theoretical background: ML techniques for the Covid-19 disease==
 The Covid-19 pandemic has become the most devastating disease of the twenty-first century and has spread to all the 216 countries in the world. Despite the availability of modern and sophisticated medical treatment, the disease is spreading through more outbreaks.
@@ Line 85: / Line 86: @@
 The present study differs from other works in that it does not focus on a single type of application, but to all the situations widely cited in the literature and seeking answers to why certain patients are so severely affected by the virus, while others are asymptomatic when affected by it.
-=3. Methodology=
+==3. Methodology==
 This study was based on the methodology proposed by Snyder [17] and Xiao and Watson [18] to delineate the flow of information and procedures necessary to conduct this literature review. The review was guided by the research questions presented in Section 1. The initial search criteria adopted are (“Data Mining” OR “Machine Learning”) AND (“Covid” OR “Coronavirus”).
-The search was limited to original articles published in peer-reviewed journals form January 2020 to June 2021, and only in English. Three scientific databases were used: ScienceDirect; Scopus; and Web of Science. '''Fig. 1''' shows the systematic literature review flowchart.
+The search was limited to original articles published in peer-reviewed journals form January 2020 to June 2021, and only in English. Three scientific databases were used: ScienceDirect; Scopus; and Web of Science. [[#img-1|Figure 1]] shows the systematic literature review flowchart.
-<div class="center" style="width: auto; margin-left: auto; margin-right: auto;">
-[[File:Review_936792395077_4578_Figure 1.svg|600px]] </div>
-<div class="center" style="width: auto; margin-left: auto; margin-right: auto;">
-'''Figure 1'''. Systematic literature review flowchart.</div>
+<div id='img-1'></div>
+{| style="text-align: center; border: 1px solid #BBB; margin: 1em auto; width: auto;max-width: auto;"
+|-
+|style="padding:10px;"|  [[File:Steiner_et_al_2022a_6611_Figure_1.png|600px]]
+|- style="text-align: center; font-size: 75%;"
+| colspan="1" style="padding:10px;"| '''Figure 1'''. Systematic literature review flowchart
+|}
 Based on the initial search criteria, 1,912 articles were identified in the three scientific databases. The most frequently cited articles were then selected, resulting in a total of 130 articles, of which 10 were duplicates and three did not fit the initial search profile (the first lay outside the predefined time interval, the second was an article from the field of pharmaceutics, and the third was not an original article, but a report). These 13 articles were removed in the exclusion of duplicates and search refinement stages, leaving a total of 117 original articles whose contents were analyzed.
-=4. Results and Discussion=
+==4. Results and discussion==
 The results of the study conducted in accordance with the methodology presented in Section 3 are presented here, with the 117 most cited articles from the ScienceDirect, Scopus and Web of Science databases. The systematic review of the articles is presented in Section 4.1 and the bibliometric review in Section 4.2.
-==4.1. Systematic Literature Review==
+===4.1. Systematic literature review===
-Of the 117 articles analyzed, 22 are studies that used X-rays (XR) and Computed Tomography (CT) of the lungs of patients affected by Covid-19 so that, through ML techniques, they could be differentiated from other lung ailments, predicting their level of severity, and determining which measures should be taken, among other alternatives, as shown in '''Table 1'''.
+Of the 117 articles analyzed, 22 are studies that used X-rays (XR) and Computed Tomography (CT) of the lungs of patients affected by Covid-19 so that, through ML techniques, they could be differentiated from other lung ailments, predicting their level of severity, and determining which measures should be taken, among other alternatives, as shown in [[#tab-1|Table 1]].
 Researchers Ardakani et al. [19], for example, suggested a rapid and valid method for Covid-19 diagnosis based on AI techniques. They used 1,020 CT slices from 108 patients with laboratory proven Covid-19 (Covid-19 group) and 86 patients with other atypical and viral pneumonia diseases (non-Covid-19 group). The authors used 10 known Convolutional Neural Networks (CNN) and concluded that a computer-aided diagnosis (CAD) approach based on CT images has promising potential to distinguish Covid-19 infections from other atypical and viral pneumonia diseases. Their study showed that ResNet-101 can be considered a promising model to characterize and diagnose Covid-19 infections. This model does not involve substantial costs and can be used as an adjuvant method during CT imaging in radiology departments.
@@ Line 112: / Line 114: @@
 Another example is the work of Cai et al. [20], who analyzed the CT quantification of Covid-19 pneumonia and how are the impacts on the assessment of disease severity through the prediction, using Random Forest Regression (RFR), of clinical outcomes in the management of Covid-19 patients. Meanwhile, the work of Chowdhury et al. [21], proposed a robust technique for automatic detection of Covid-19 pneumonia from digital chest X-ray images applying pre-trained Deep Learning (DL) algorithms while maximizing the detection accuracy. A public database was created by the authors combining several public databases and also by collecting images from recently published papers.
-'''Table 1''' provides an overview of this research niche involving ML and XR/CT. The first column contains the authors, while the second presents the focus of the study. The third column identifies the ML technique that was employed, and the fourth column shows the databases that were used.
+[[#tab-1|Table 1]] provides an overview of this research niche involving ML and XR/CT. The first column contains the authors, while the second presents the focus of the study. The third column identifies the ML technique that was employed, and the fourth column shows the databases that were used.
-<div class="center" style="width: auto; margin-left: auto; margin-right: auto;">
+<div class="center" style="font-size: 75%;">'''Table 1'''. Papers (22) that analyzed lung XR/CT of Covid-19 patients using ML techniques</div>
-'''Table 1.''' Papers (22) that analyzed lung XR/CT of Covid-19 patients using ML techniques.</div>
-{| style="width: 100%;border-collapse: collapse;"
+<div id='tab-1'></div>
+{| class="wikitable" style="margin: 1em auto 0.1em auto;border-collapse: collapse;font-size:85%;width:auto;"
+|-style="text-align:center"
+! style="background-color: #d1e0df;text-align:left;"| Authors (year) !!  style="background-color:#d1e0df;text-align:left;"| Focus of the paper !!  style="background-color: #d1e0df;text-align:left;"| ML Techniques !! style="background-color: #d1e0df;text-align:left;"| Databases
 |-
-|  style="border-top: 2pt solid black;border-bottom: 1pt solid black;text-align: center;"|'''Authors (year)'''
+|  style="text-align: left;"|Anastasopoulos et al. (2020) [22]
-|  style="border-top: 2pt solid black;border-bottom: 1pt solid black;text-align: center;"|'''Focus of the paper'''
+|  style="text-align: left;"|Implement an automated software to solve the substantial increase in chest CT admissions
-|  style="border-top: 2pt solid black;border-bottom: 1pt solid black;text-align: center;"|'''ML Techniques'''
+|  style="text-align: left;"|DL
-|  style="border-top: 2pt solid black;border-bottom: 1pt solid black;text-align: center;"|'''Databases'''
+|  style="text-align: left;"|GitHub platform with Covid-19 chest CT dataset
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Anastasopoulos et al. (2020) [22]
+|  style="text-align: left;"|Ardakani et al. (2020) [19]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Implement an automated software to solve the substantial increase in chest CT admissions
+|  style="text-align: left;"|Develop a rapid and valid method for Covid-19 diagnosis
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|DL
+|  style="text-align: left;"|10 Convolutional NN
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|GitHub platform with Covid-19 chest CT dataset
+|  style="text-align: left;"|108 patients
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Ardakani et al. (2020) [19]
+|  style="text-align: left;"|Bharati et al. (2020) [23]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Develop a rapid and valid method for Covid-19 diagnosis
+|  style="text-align: left;"|Detect lung diseases from X-ray images through ''VGG Data STN ''with ''CNN ''(VDSNet)
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|10 Convolutional NN
+|  style="text-align: left;"|DL; VGG; STN; CNN
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|108 patients
+|  style="text-align: left;"|Kaggle repository
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Bharati et al. (2020) [23]
+|  style="text-align: left;"|Brunese et al. (2020) [24]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Detect lung diseases from X-ray images through ''VGG Data STN ''with ''CNN ''(VDSNet)
+|  style="text-align: left;"|Detect Covid-19 from chest X-rays
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|DL; VGG;
+|  style="text-align: left;"|Supervised ML techniques
+|  style="text-align: left;"|85 chest X-rays
-STN; CNN
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Kaggle repository
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Brunese et al. (2020) [24]
+|  style="text-align: left;"|Cai et al. (2020) [20]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Detect Covid-19 from chest X-rays
+|  style="text-align: left;"|Analyze CT quantification
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Supervised ML techniques
+|  style="text-align: left;"|RFR
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|85 chest X-rays.
+|  style="text-align: left;"|99 patients from Zhejiang
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Cai et al. (2020) [20]
+|  style="text-align: left;"|Chakraborty & Mali (2021) [25]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Analyze CT quantification
+|  style="text-align: left;"|Efficiently Interpret and segment Covid-19 radiological images.
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|RFR
+|  style="text-align: left;"|SUFMACS
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|99 patients from Zhejiang
+|  style="text-align: left;"|250 CT images; 250 X-Ray images
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Chakraborty & Mali (2021) [25]
+|  style="text-align: left;"|Chowdhury et al. (2020) [21]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Efficiently Interpret and segment Covid-19 radiological images.
+|  style="text-align: left;"|Detect Covid-19 pneumonia from digital X-ray images
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|SUFMACS
+|  style="text-align: left;"|DL
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|250 CT images;
+|  style="text-align: left;"|Kaggle databases
-X-Ray images
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Chowdhury et al. (2020) [21]
+|  style="text-align: left;"|Elaziz et al. (2020) [26]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Detect Covid-19 pneumonia from digital X-ray images
+|  style="text-align: left;"|Classify chest x-ray images into 2 classes: Covid-19 patient or non-Covid-19 person
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|DL
+|  style="text-align: left;"|New FrMEMs; modified Manta-Ray Foraging Optimization based on DE
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Kaggle databases
+|  style="text-align: left;"|GitHub; Qatar University; University of Dhaka
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Elaziz et al. (2020) [26]
+|  style="text-align: left;"|Vijay kumar et al. (2020) [27]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Classify chest x-ray images into 2 classes: Covid-19 patient or
+|  style="text-align: left;"|Use the analytics of key points from images of Covid-19 for diagnosis and predictions
+|  style="text-align: left;"|GANs; DL
-non-Covid-19 person.
+|  style="text-align: left;"|16 benchmark datasets
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|New FrMEMs;
-modified Manta-Ray Foraging Optimization based on DE
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|GitHub; Qatar University; University of Dhaka
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Vijay kumar et al. (2020) [27]
+|  style="text-align: left;"|Loey et al. (2020) [28]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Use the analytics of key points from images of Covid-19 for diagnosis and predictions
+|  style="text-align: left;"|Detect coronavirus in chest X-ray images
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|GANs; DL
+|  style="text-align: left;"|GAN; DL
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|16 benchmark datasets
+|  style="text-align: left;"|307 images
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Loey et al. (2020) [28]
+|  style="text-align: left;"|Saha et al. (2021) [29]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Detect coronavirus in chest X-ray images
+|  style="text-align: left;"|Identify Covid-19 patients by evaluating chest X-ray images through an automated detection scheme (EMCNet)
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|GAN; DL
+|  style="text-align: left;"|DL; RF; SVM; DT; AdaBoost
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|307 images
+|  style="text-align: left;"|Github repository (400 chest X-ray images)
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Saha et al. (2021) [29]
+|  style="text-align: left;"|Saygılı (2021) [30]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Identify Covid-19 patients by evaluating chest X-ray images through an automated detection scheme (EMCNet)
+|  style="text-align: left;"|Achieve rapid and accurate detection of Covid-19 from CT and X-ray images
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|DL; RF; SVM; DT; AdaBoost
+|  style="text-align: left;"|k-NN; SVM; Bag of Tree; K-ELM
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Github repository
+|  style="text-align: left;"|3 public Covid-19 data sets
-(400 chest X-ray images)
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Saygılı (2021) [30]
+|  style="text-align: left;"|Sedik et al. (2020) [31]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Achieve rapid and accurate detection of Covid-19 from CT and X-ray images
+|  style="text-align: left;"|Present two data-augmentation models to enhance learnability of Covid-19 detection
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|k-NN; SVM; Bag of Tree; K-ELM
+|  style="text-align: left;"|CNN: ConvLSTM-based on DL
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|3 public Covid-19 data sets
+|  style="text-align: left;"|2 datasets consisting of X-ray and CT images
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Sedik et al. (2020) [31]
+|  style="text-align: left;"|Sethy et al. (2020) [32]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Present two data-augmentation models to enhance learnability
+|  style="text-align: left;"|Detect coronavirus infected patients using X-ray images
+|  style="text-align: left;"|Deep feature; SVM
-of Covid-19 detection
+|  style="text-align: left;"|GitHub repositor (University of Montreal; 381 images)
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|CNN: ConvLSTM-based on DL
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|2 datasets consisting of X-ray and CT images
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Sethy et al. (2020) [32]
+|  stylestyle="background-color:#f5faf9;"="text-align: left;"|Shiri et al. (2021) [33]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Detect coronavirus infected patients using X-ray images
+|  style="text-align: left;"|Predict Covid-19 patients using clinical data and lung/lesion radiomic features extracted from chest CT images
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Deep feature; SVM
+|  style="text-align: left;"|XGBoost
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|GitHub repositor (University of Montreal; 381 images)
+|  style="text-align: left;"|152 patients
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Shiri et al. (2021) [33]
+|  style="text-align: left;"|D. Singh et al. (2020) [34]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Predict Covid-19 patients using clinical data and lung/lesion radiomic features extracted from chest CT images
+|  style="text-align: left;"|Classify Covid-19 patients from chest CT images
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|XGBoost
+|  style="text-align: left;"|MODE; ANN; ANFIS; CNN
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|152 patients
+|  style="text-align: left;"|---
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|D. Singh et al. (2020) [34]
+|  style="text-align: left;"|Somasekar et al. (2020) [14]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Classify Covid-19 patients from chest CT images
+|  style="text-align: left;"|Open 3 research directions in the fight against the pandemic: CXR image classification; patient risk prediction; and forecasting of disease
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|MODE; ANN; ANFIS; CNN
+|  style="text-align: left;"|DCNN
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|---
+|  style="text-align: left;"|---
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Somasekar et al. (2020) [14]
+|  style="text-align: left;"|Tamal et al. (2021) [35]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Open 3 research directions in the fight against the pandemic: CXR image classification; patient risk prediction;
+|  style="text-align: left;"|Detect Covid-19 early and rapidly from CXR
+|  style="text-align: left;"|SVM; k-NN; EBM Trees
-and forecasting of disease
+|  style="text-align: left;"|378 images
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|DCNN
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|---
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Tamal et al. (2021) [35]
+|  style="text-align: left;"|Tartaglione et al. (2020) [36]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Detect Covid-19 early and rapidly from CXR
+|  style="text-align: left;"|Provide which information to expect through CXR images
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|SVM; k-NN; EBM Trees
+|  style="text-align: left;"|DL to Covid classification of CXR images
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|378 images
+|  style="text-align: left;"|Hospitals in Northern Italy
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Tartaglione et al. (2020) [36]
+|  style="text-align: left;"|X. Wang et al. (2020) [37]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Provide which information to expect through CXR images
+|  style="text-align: left;"|Develop a DL using 3D CT for Covid-19 classification and lesion localization
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|DL to Covid classification of CXR images
+|  style="text-align: left;"|DL
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Hospitals in Northern Italy
+|  style="text-align: left;"|540 patients
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|X. Wang et al. (2020) [37]
+|  style="text-align: left;"|Waheed et al. (2020) [38]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Develop a DL using 3D CT for Covid-19 classification and lesion localization
+|  style="text-align: left;"|Generate synthetic chest X-ray images
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|DL
+|  style="text-align: left;"|DL: CNNs
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|540 patients
+|  style="text-align: left;"|3 publicly accessible datasets
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Waheed et al. (2020) [38]
+|  style="text-align: left;"|Wu et al. (2021) [39]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Generate synthetic chest X-ray images
+|  style="text-align: left;"|Improve Covid-19 diagnosis using CT
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|DL: CNNs
+|  style="text-align: left;"|RF
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|3 publicly accessible datasets
+|  style="text-align: left;"|Youan Hospital, Beijing.
-|-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Wu et al. (2021) [39]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Improve Covid-19 diagnosis using CT
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|RF
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Youan Hospital, Beijing.
 |}
-'''Acronyms (alphabetical): '''AI (Artificial Intelligence); AF (Atrial Fibrillation); AL (Active Learning); ANFIS (adaptive neuro-fuzzy inference system); API (Application Programming Interface); AR (Auto-Regressive Process); ARIMA (Auto-Regressive Integrated Moving Average); BTM (Biterm Topic Model); CDCP (Center for Disease Control and Prevention); CART (Classification And Regression Trees); CXR (Chest X-Ray); CMC (composite Monte-Carlo); CMM (Chinese Materia Medica); CNN (Convolutional Neural Networks); CT (computed tomography); ConvLSTM (Convolutional Long Short-Term Memory); CTree (Conditional Inference Tree); CUBIST (Cubist Regression); DCNN (Deep Convolution Neural Networks); DE (Differential Evolution); DL (deep learning); DT (Decision Trees); EA (Ensemble Algorithm); EBM (Ensemble Bagged Model) Trees); ELM (Extreme Learning Machine); FrMEMs (Fractional Multichannel Exponent Moments); FRI (Fuzzy Rule Induction); GA (Genetic Algorithm); GAN (Generative Adversarial Network); GBA (Gradient Boosting Algorithm); GPR(Gaussian Process Regression); GBM (Gradient Boosted Tree Models); GIWD (Generalized Inverse Weibull distribution); GHOST (Globally Harmonized Observational Surface Treatment); HCA (Hierarchical Clustering Algorithm); IoT (Internet of Things); k-NN (k-Nearest Neighbor); K-ELM (Kernel Extreme Learning Machine); LASSO (least absolute shrinkage and selection operator); LDA (Latent Dirichlet Allocation); LSTM (Long Short-Term Memory); LR (Linear Regression); LoR (Logistic Regression); LOS (Length of Stay); LSTM (Long /Short Term Memory); LR (Linear Regression); ML (Machine Learning); MLDSP (Machine Learning with Digital Signal Processing); MLP (Multilayer perceptron); MLP-ICA (MLP-imperialist competitive algorithm); MODE (Multi-objective Differential Evolution); MNB (Multinomial Naïve Bayes); NLP (Natural Language Processing); NCBI (National Center for Biotechnology Information); NN (Neural Network); PAC (Passive Aggressive Classifier); PCR (Principal Components Regression); PDR-NML (Partial Derivative Regression and Nonlinear Machine Learning); PLS-DA (Partial Least Squares Discriminant Analysis); PLSR (Partial Least Squares Regression); PNN+cf (Polynomial Neural Network with Corrective Feedback); PR (Polynomial Regression); RF (Random Forest); RFR (Random Forest Regression); RIDGE (Ridge Regression); RT (Regression Tree); SARIMA (Seasonal Auto-Regressive Integrated Moving Average); SEIRD (Susceptible, Exposed, Infected, Recovered, and Dead); SIR (Susceptible(P-Infected-Recovered epidemiological model); SLR (Simple Linear Regression); SMOM (Social Mimic Optimization Method); SNA (social network analysis); SUFMACS (SUperpixel based Fuzzy Memetic Advanced Cuckoo Search); SVM (Support Vector Machine); SVR (Support Vector Regression); STN (Spatial Transformer Network); SVC (Support Vector Classifier);  SVR (Support Vector Regression); TClustVID (Clustered Based Proposed Classification and Topics modeling Approach); TCM (traditional Chinese medicine); TWC (Topological Weighted Centroid); USDA ERS (United States Department of Agriculture, Economic Research Service); VGG (Visual Geometry Group based Neural Network); WHO (World Health Organization); VAR (Vector Autoregression); WSIDEA (Weighted Stochastic Data Envelopment Analysis); WSCC (Web of Science Core Collection); XGBoost (Extreme Gradient Boosting).
+<div class="justified" style="font-size: 75%;">'''Acronyms (alphabetical): '''AI (Artificial Intelligence); AF (Atrial Fibrillation); AL (Active Learning); ANFIS (adaptive neuro-fuzzy inference system); API (Application Programming Interface); AR (Auto-Regressive Process); ARIMA (Auto-Regressive Integrated Moving Average); BTM (Biterm Topic Model); CDCP (Center for Disease Control and Prevention); CART (Classification And Regression Trees); CXR (Chest X-Ray); CMC (composite Monte-Carlo); CMM (Chinese Materia Medica); CNN (Convolutional Neural Networks); CT (computed tomography); ConvLSTM (Convolutional Long Short-Term Memory); CTree (Conditional Inference Tree); CUBIST (Cubist Regression); DCNN (Deep Convolution Neural Networks); DE (Differential Evolution); DL (deep learning); DT (Decision Trees); EA (Ensemble Algorithm); EBM (Ensemble Bagged Model) Trees); ELM (Extreme Learning Machine); FrMEMs (Fractional Multichannel Exponent Moments); FRI (Fuzzy Rule Induction); GA (Genetic Algorithm); GAN (Generative Adversarial Network); GBA (Gradient Boosting Algorithm); GPR(Gaussian Process Regression); GBM (Gradient Boosted Tree Models); GIWD (Generalized Inverse Weibull distribution); GHOST (Globally Harmonized Observational Surface Treatment); HCA (Hierarchical Clustering Algorithm); IoT (Internet of Things); k-NN (k-Nearest Neighbor); K-ELM (Kernel Extreme Learning Machine); LASSO (least absolute shrinkage and selection operator); LDA (Latent Dirichlet Allocation); LSTM (Long Short-Term Memory); LR (Linear Regression); LoR (Logistic Regression); LOS (Length of Stay); LSTM (Long /Short Term Memory); LR (Linear Regression); ML (Machine Learning); MLDSP (Machine Learning with Digital Signal Processing); MLP (Multilayer perceptron); MLP-ICA (MLP-imperialist competitive algorithm); MODE (Multi-objective Differential Evolution); MNB (Multinomial Naïve Bayes); NLP (Natural Language Processing); NCBI (National Center for Biotechnology Information); NN (Neural Network); PAC (Passive Aggressive Classifier); PCR (Principal Components Regression); PDR-NML (Partial Derivative Regression and Nonlinear Machine Learning); PLS-DA (Partial Least Squares Discriminant Analysis); PLSR (Partial Least Squares Regression); PNN+cf (Polynomial Neural Network with Corrective Feedback); PR (Polynomial Regression); RF (Random Forest); RFR (Random Forest Regression); RIDGE (Ridge Regression); RT (Regression Tree); SARIMA (Seasonal Auto-Regressive Integrated Moving Average); SEIRD (Susceptible, Exposed, Infected, Recovered, and Dead); SIR (Susceptible(P-Infected-Recovered epidemiological model); SLR (Simple Linear Regression); SMOM (Social Mimic Optimization Method); SNA (social network analysis); SUFMACS (SUperpixel based Fuzzy Memetic Advanced Cuckoo Search); SVM (Support Vector Machine); SVR (Support Vector Regression); STN (Spatial Transformer Network); SVC (Support Vector Classifier);  SVR (Support Vector Regression); TClustVID (Clustered Based Proposed Classification and Topics modeling Approach); TCM (traditional Chinese medicine); TWC (Topological Weighted Centroid); USDA ERS (United States Department of Agriculture, Economic Research Service); VGG (Visual Geometry Group based Neural Network); WHO (World Health Organization); VAR (Vector Autoregression); WSIDEA (Weighted Stochastic Data Envelopment Analysis); WSCC (Web of Science Core Collection); XGBoost (Extreme Gradient Boosting).</div>
 On the other hand, many works used ML techniques to analyze people’s feelings and emotions regarding the pandemic, even their impressions concerning the climate. Sentiment Analysis is an field of study that seeks useful information through the sentiments that people share on social media, such as Facebook and Twitter [40]. Sentiments can be classified as neutral, positive or negative.
-Gulati et al. [41], for example, presented a comparative analysis of seven ML classifiers, such as Linear Support Vector Classifier (SVC), Perceptron, Passive Aggressive Classifier (PAC), and Logistic Regression (LoR). They used more than 72,000 tweet datasets related to Covid-19 pandemic and achieved an accuracy score higher than 98%. Haupt et al. [42] used interdisciplinary approaches to big data, ML, content analysis, and social network analysis (SNA) to characterize the communicative behavior, conversation themes, and network structures of “Liberate protest” supporters and non-supporters. For this purpose, the authors used unsupervised ML techniques and social network analysis. Praveen et al. [43], conducted their study to analyze Indian citizens’ perceptions of what causes stress, anxiety, and trauma during Covid-19. For this purpose, the authors used ML techniques, more specifically, Natural Language Processing (NLP) in 840,000 tweets. Of the 117 articles analyzed, 13 were in this line of research and are listed in '''Table 2''', below.
+Gulati et al. [41], for example, presented a comparative analysis of seven ML classifiers, such as Linear Support Vector Classifier (SVC), Perceptron, Passive Aggressive Classifier (PAC), and Logistic Regression (LoR). They used more than 72,000 tweet datasets related to Covid-19 pandemic and achieved an accuracy score higher than 98%. Haupt et al. [42] used interdisciplinary approaches to big data, ML, content analysis, and social network analysis (SNA) to characterize the communicative behavior, conversation themes, and network structures of “Liberate protest” supporters and non-supporters. For this purpose, the authors used unsupervised ML techniques and social network analysis. Praveen et al. [43] conducted their study to analyze Indian citizens’ perceptions of what causes stress, anxiety, and trauma during Covid-19. For this purpose, the authors used ML techniques, more specifically, Natural Language Processing (NLP) in 840,000 tweets. Of the 117 articles analyzed, 13 were in this line of research and are listed in [[#tab-2|Table 2]].
-<div class="center" style="width: auto; margin-left: auto; margin-right: auto;">
-'''Table 2.''' Works (13) related to Covid-19 that used ML techniques and social network tools.</div>
-{| style="width: 100%;border-collapse: collapse;"
+<div class="center" style="font-size: 75%;">'''Table 2'''. Works (13) related to Covid-19 that used ML techniques and social network tools</div>
+<div id='tab-2'></div>
+{| class="wikitable" style="margin: 1em auto 0.1em auto;border-collapse: collapse;font-size:85%;width:auto;"
+|-style="text-align:center"
+! style="background-color: #d1e0df;text-align:left;"| Authors (year) !!  style="background-color:#d1e0df;text-align:left;"| Focus of the paper !!  style="background-color: #d1e0df;text-align:left;"| ML Techniques !! style="background-color: #d1e0df;text-align:left;"| Databases
 |-
-|  style="border-top: 2pt solid black;border-bottom: 1pt solid black;text-align: center;"|'''Authors (year)'''
+|  style="text-align: left;"|Abd-Alrazaq et al. (2020) [44]
-|  style="border-top: 2pt solid black;border-bottom: 1pt solid black;text-align: center;"|'''Focus of the paper'''
+|  style="text-align: left;"|Identify the topics related to Covid-19 posted by Twitter users
-|  style="border-top: 2pt solid black;border-bottom: 1pt solid black;text-align: center;"|'''ML Techniques'''
+|  style="text-align: left;"|API; Tweepy Python library
-|  style="border-top: 2pt solid black;border-bottom: 1pt solid black;text-align: center;"|'''Databases'''
+|  style="text-align: left;"|February 2, 2020, to March 15, 2020 in public English language tweets
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Abd-Alrazaq et al. (2020) [44]
+|  style="text-align: left;"|Gulati et al. (2021) [41]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Identify the topics related to Covid-19 posted by Twitter users
+|  style="text-align: left;"|Classify sentiment based on tweets related to Covid-19
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|API; Tweepy Python library
+|  style="text-align: left;"|Linear SVC; Perceptron; PAC; LoR
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|February 2, 2020, to March 15, 2020 in public English
+|  style="text-align: left;"|72,000 tweets
-language tweets
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Gulati et al. (2021) [41]
+|  style="text-align: left;"|Gupta et al. (2021) [45]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Classify sentiment based on tweets related to Covid-19
+|  style="text-align: left;"|Quantify twitter users’ perceptions regarding the effect of weather and analyze how they evolved with respect to real-world events and time.
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Linear SVC; Perceptron; PAC; LoR
+|  style="text-align: left;"|API
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|72,000 tweets
+|  style="text-align: left;"|166,005 English tweets; from January 23 to June 22, 2020
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Gupta et al. (2021) [45]
+|  style="text-align: left;"|Haupt et al. (2021) [42]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Quantify twitter users’ perceptions regarding the effect of weather and analyze how they evolved with respect to real-world events and time.
+|  style="text-align: left;"|Characterize communicative (tweets) behavior
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|API
+|  style="text-align: left;"|ML techniques and SNA
+|  style="text-align: left;"|API from Twitter
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|166,005 English tweets; from January 23 to June 22, 2020
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Haupt et al. (2021) [42]
+|  style="text-align: left;"|Hou et al. (2021) [46]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Characterize communicative (tweets) behavior
+|  style="text-align: left;"|Explore public attention on social media
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|ML techniques and SNA
+|  style="text-align: left;"|Text analysis; LDA
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|API from Twitter
+|  style="text-align: left;"|Weibo (popular microblogging site in China) from December 27, 2019 to May 31, 2020
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Hou et al. (2021) [46]
+|  style="text-align: left;"|Kabir & Madria (2021) [47]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Explore public attention on social media
+|  style="text-align: left;"|Present tweets dataset on Covid-19 emotional responses (EMOCOV)
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Text analysis; LDA
+|  style="text-align: left;"|DL
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Weibo (popular microblogging site in China) from December 27, 2019 to May 31, 2020
+|  style="text-align: left;"|Data of 5,000 tweets
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Kabir & Madria (2021) [47]
+|  style="text-align: left;"|Kyriazos et al. (2021) [48]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Present tweets dataset on Covid-19 emotional responses (EMOCOV)
+|  style="text-align: left;"|Model that differentiated the top 25% well-being scorers in early Covid-19 quarantine
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|DL
+|  style="text-align: left;"|CART; RF; CTREE
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Data of 5,000 tweets
+|  style="text-align: left;"|Data (1,518) were collected in a web-link posted on webpages and Facebook accounts
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Kyriazos et al. (2021) [48]
+|  style="text-align: left;"|S. Li et al. (2020) [49]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Model that differentiated the top 25% well-being scorers in early Covid-19 quarantine
+|  style="text-align: left;"|Explore Covid-19’s impacts on mental health
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|CART; RF;
+|  style="text-align: left;"|DL
+|  style="text-align: left;"|17,865 active Weibo users
-CTREE
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Data (1,518) were collected in a web-link posted on webpages and Facebook accounts
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|S. Li et al. (2020) [49]
+|  style="text-align: left;"|Mackey et al. (2020) [50]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Explore Covid-19’s impacts on mental health
+|  style="text-align: left;"|Characterize users’ conversations (tweets) associated with Covid-19 symptoms and experiences
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|DL
+|  style="text-align: left;"|BTM
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|17,865 active Weibo users
+|  style="text-align: left;"|4,492,954 tweets
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Mackey et al. (2020) [50]
+|  style="text-align: left;"|Praveen et al. (2021) [43]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Characterize users’ conversations (tweets) associated with
+|  style="text-align: left;"|Analyze Indian citizens’ perception of anxiety, stress and trauma during Covid-19
+|  style="text-align: left;"|Natural language
-Covid-19 symptoms and experiences
+|  style="text-align: left;"|840,000 tweets
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|BTM
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|4,492,954 tweets
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Praveen et al. (2021) [43]
+|  style="text-align: left;"|Samuel et al. (2020) [51]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Analyze Indian citizens’ perception of anxiety, stress and trauma during Covid-19
+|  style="text-align: left;"|Identify public sentiment (tweets) associated with the pandemic
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Natural language
+|  style="text-align: left;"|Naïve Bayes; LR; LoR; k-NN
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|840,000 tweets
+|  style="text-align: left;"|900,000 tweets
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Samuel et al. (2020) [51]
+|  style="text-align: left;"|Satu et al. (2021) [52]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Identify public sentiment (tweets) associated with the pandemic
+|  style="text-align: left;"|Analyze Covid-19 public tweets to extract significant sentiments
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Naïve Bayes; LR; LoR; k-NN
+|  style="text-align: left;"|TClustVID
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|900,000 tweets
+|  style="text-align: left;"|IEEE data portal developed by Rabindra Lamsal
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Satu et al. (2021) [52]
+|  style="text-align: left;"|Shah et al. (2021) [53]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Analyze Covid-19 public tweets to extract significant sentiments
+|  style="text-align: left;"|Analyze online physician rating (OPR) to identify emerging and fading topics and sentiment trends on physician websites
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|TClustVID
+|  style="text-align: left;"|NLP
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|IEEE data portal developed by Rabindra Lamsal
+|  style="text-align: left;"|55,612 OPRs of 3,430 doctors
-|-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Shah et al. (2021) [53]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Analyze online physician rating (OPR) to identify emerging and fading topics and sentiment trends on physician websites
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|NLP
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|55,612 OPRs of 3,430 doctors
 |}
-Acronyms (alphabetical): See Table 1.
+Acronyms (alphabetical): See [[#tab-1|Table 1]].
 In turn, 44 of the 117 selected articles involved ML methods to predict a wide range of aspects, such as the number of patients who will be infected or intubated, the trends of the pandemic, the production of a real-time Covid-19 SEIRD (Susceptible, Exposed, Infected, Recovered, and Dead) model, and student performance.
-Amar et al. [54], for example, attempted to investigate the disease to eliminate its effects and, to this end, the authors examined a real database from Egypt, from February 15, 2020, to June 15, 2020. They predicted the number of patients that would be infected and estimated the final size of the pandemic. For this purpose, they applied several regression analysis models. Burdick et al. [55] attempted to predict patients’ need for ventilation to determine a better allocation of resources and prevent emergency intubations and their associated risks. The authors analyzed 197 patients, from five USA health systems between March 24 and May 4, 2020. The patients were enrolled in the REspirAtory Decompensation for the triage of the disease: a prospective studY (READY) clinical trial. Of the 117 articles analyzed, 44, including the two already mentioned above, were in this line of research and are listed in '''Table 3'''.
+Amar et al. [54], for example, attempted to investigate the disease to eliminate its effects and, to this end, the authors examined a real database from Egypt, from February 15, 2020, to June 15, 2020. They predicted the number of patients that would be infected and estimated the final size of the pandemic. For this purpose, they applied several regression analysis models. Burdick et al. [55] attempted to predict patients’ need for ventilation to determine a better allocation of resources and prevent emergency intubations and their associated risks. The authors analyzed 197 patients, from five USA health systems between March 24 and May 4, 2020. The patients were enrolled in the REspirAtory Decompensation for the triage of the disease: a prospective studY (READY) clinical trial. Of the 117 articles analyzed, 44, including the two already mentioned above, were in this line of research and are listed in [[#tab-3|Table 3]].
-<div class="center" style="width: auto; margin-left: auto; margin-right: auto;">
+<div class="center" style="font-size: 75%;">'''Table 3'''. Works (44) related to Covid-19 that directly used ML techniques for prediction</div>
-'''Table 3.''' Works (44) related to Covid-19 that directly used ML techniques for prediction.</div>
-{| style="width: 100%;border-collapse: collapse;"
+<div id='tab-3'></div>
+{| class="wikitable" style="margin: 1em auto 0.1em auto;border-collapse: collapse;font-size:85%;width:auto;"
+|-style="text-align:center"
+! style="background-color: #d1e0df;text-align:left;"| Authors (year) !!  style="background-color:#d1e0df;text-align:left;"| Focus of the paper !!  style="background-color: #d1e0df;text-align:left;"| ML Techniques !! style="background-color: #d1e0df;text-align:left;"| Databases
 |-
-|  style="border-top: 2pt solid black;border-bottom: 1pt solid black;text-align: center;"|'''Authors (year)'''
+|  style="text-align: left;"|Amar et al. (2020) [54]
-|  style="border-top: 2pt solid black;border-bottom: 1pt solid black;text-align: center;"|'''Focus of the paper'''
+|  style="text-align: left;"|Predict the number of patients that will be infected with Covid-19 in Egypt
-|  style="border-top: 2pt solid black;border-bottom: 1pt solid black;text-align: center;"|'''ML Techniques'''
+|  style="text-align: left;"|LoR; Regression models
-|  style="border-top: 2pt solid black;border-bottom: 1pt solid black;text-align: center;"|'''Databases'''
+|  style="text-align: left;"|Egyptian Ministry of Health; February 15, 2020, to June 15, 2020
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Amar et al. (2020) [54]
+|  style="text-align: left;"|Ardabili et al. (2020) [56]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Predict the number of patients that will be infected with Covid-19 in Egypt
+|  style="text-align: left;"|Predict the Covid-19 outbreak and the enforcement of relevant control measures
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|LoR; Regression models
+|  style="text-align: left;"|MLP; ANFIS
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Egyptian Ministry of Health; February
+|  style="text-align: left;"|Worldometers website for five countries
-, 2020, to June 15, 2020
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Ardabili et al. (2020) [56]
+|  style="text-align: left;"|Arvind et al. (2021) [57]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Predict the Covid-19 outbreak and the enforcement of relevant control measures
+|  style="text-align: left;"|Predict future intubation among patients diagnosed with Covid-19
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|MLP; ANFIS
+|  style="text-align: left;"|RF
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Worldometers website for five countries
+|  style="text-align: left;"|Data from 5 hospitals within an academic healthcare system (4,087 patients)
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Arvind et al. (2021) [57]
+|  style="text-align: left;"|ArunKumar et al. (2021) [58]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Predict future intubation among patients diagnosed with
+|  style="text-align: left;"|Forecast the epidemiological trends of the Covid-19 pandemic for top-16 countries
+|  style="text-align: left;"|Time series models; ARIMA; SARIMA
-Covid-19
+|  style="text-align: left;"|John Hopkins University’s Covid-19 database
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|RF
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Data from 5 hospitals within an academic healthcare system (4,087 patients)
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|ArunKumar et al. (2021) [58]
+|  style="text-align: left;"|Aydin & Yurdakul (2020) [59]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Forecast the epidemiological trends of the Covid-19 pandemic for top-16 countries
+|  style="text-align: left;"|Analyze the performance of countries to counter the Covid-19 outbreak
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Time series models;
+|  style="text-align: left;"|WSIDEA; k-means; HCA; RF; DT
+|  style="text-align: left;"|Data from 142 countries
-ARIMA; SARIMA
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|John Hopkins University’s Covid-19 database
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Aydin & Yurdakul (2020) [59]
+|  style="text-align: left;"|Ayyoubzadeh et al. (2020) [60]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Analyze the performance of countries to counter the Covid-19 outbreak
+|  style="text-align: left;"|Predict the incidence of Covid-19 in Iran
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|WSIDEA; k-means; HCA; RF; DT
+|  style="text-align: left;"|LR; LSTM models
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Data from 142 countries
+|  style="text-align: left;"|Google Trends website
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Ayyoubzadeh et al. (2020) [60]
+|  style="text-align: left;"|Ballı (2021) [61]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Predict the incidence of Covid-19 in Iran
+|  style="text-align: left;"|Identify the curve of the disease and forecast the epidemic trend
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|LR; LSTM models
+|  style="text-align: left;"|LR, MLP, RF and SVM
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Google Trends website
+|  style="text-align: left;"|Data from WHO (35 weeks)
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Ballı (2021) [61]
+|  style="text-align: left;"|Bloise & Tancioni (2021) [62]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Identify the curve of the disease and forecast the epidemic trend
+|  style="text-align: left;"|Exploit the provincial variability of Covid-19 cases in Italy to select the territorial predictors for the pandemic
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|LR, MLP, RF and SVM
+|  style="text-align: left;"|LASSO; Elastic net model
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Data from WHO (35 weeks)
+|  style="text-align: left;"|Data from March 21, 2020 to June 3, 2020, in Italy
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Bloise & Tancioni (2021) [62]
+|  style="text-align: left;"|Burdick et al. (2020) [55]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Exploit the provincial variability of Covid-19 cases in Italy to select the territorial predictors for the pandemic
+|  style="text-align: left;"|Predict the need for ventilation for Covid-19 patients
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|LASSO; Elastic net model
+|  style="text-align: left;"|XGBoost; DT
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Data from March 21, 2020 to June 3, 2020, in Italy
+|  style="text-align: left;"|197 patients were enrolled in the READY (REspirAtory Decompensation study)
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Burdick et al. (2020) [55]
+|  style="text-align: left;"|Buscema et al. (2020) [63]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Predict the need for ventilation for Covid-19 patients
+|  style="text-align: left;"|Analyze the evolution of the Covid-19 phenomenon
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|XGBoost; DT
+|  style="text-align: left;"|TWC algorithm
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|197 patients were enrolled in the READY (REspirAtory Decompensation study)
+|  style="text-align: left;"|Geospatial coordinates of latitude and longitude of the Italian locations where the events occurred.
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Buscema et al. (2020) [63]
+|  style="text-align: left;"|Chakraborti et al. (2021) [64]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Analyze the evolution of the Covid-19 phenomenon
+|  style="text-align: left;"|Perform the regression modelling and provide subsequent interpretation of most critical factors
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|TWC algorithm
+|  style="text-align: left;"|RF; GBM
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Geospatial coordinates of latitude and longitude of the Italian locations where the events occurred.
+|  style="text-align: left;"|European Centre for Disease Prevention and Control (ECDC)
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Chakraborti et al. (2021) [64]
+|  style="text-align: left;"|Chatterjee et al. (2020) [65]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Perform the regression modelling and provide subsequent interpretation of most critical factors
+|  style="text-align: left;"|Analyze datasets to understand the trend of Covid-19
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|RF; GBM
+|  style="text-align: left;"|Statistical and univariate time series
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|European Centre for Disease Prevention and Control
+|  style="text-align: left;"|Oxford University Database
-(ECDC)
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Chatterjee et al. (2020) [65]
+|  style="text-align: left;"|Chimmula & Zhang (2020) [66]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Analyze datasets to
+|  style="text-align: left;"|Forecast Covid-19 transmission
+|  style="text-align: left;"|Time series; DL; LSTM networks
-understand the trend of
+|  style="text-align: left;"|Johns Hopkins university; Canadian health authority
-Covid-19
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Statistical and univariate time series
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Oxford University
-Database
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Chimmula & Zhang (2020) [66]
+|  style="text-align: left;"|Cobre et al. (2021) [67]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Forecast Covid-19 transmission
+|  style="text-align: left;"|Predict Covid-19 diagnosis and disease severity
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Time series; DL; LSTM networks
+|  style="text-align: left;"|ANN; DT; PLS-DA; KNN
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Johns Hopkins university; Canadian health authority
+|  style="text-align: left;"|Kaggle platform 5,643 patient samples
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Cobre et al. (2021) [67]
+|  style="text-align: left;"|Ebinger et al. (2021) [68]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Predict Covid-19 diagnosis and disease severity
+|  style="text-align: left;"|Predict the likelihood of prolonged LOS
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|ANN; DT;
+|  style="text-align: left;"|3 ML models developed using DataRobot
+|  style="text-align: left;"|966 patients
-PLS-DA; KNN
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Kaggle platform
-,643 patient samples
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Ebinger et al. (2021) [68]
+|  style="text-align: left;"|Fong et al. (2020) [69]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Predict the likelihood of prolonged LOS
+|  style="text-align: left;"|Find a forecasting model (GROOWS) from a small dataset for Covid-19 cases
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|3 ML models developed using DataRobot
+|  style="text-align: left;"|PNN+cf
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|966 patients
+|  style="text-align: left;"|Archive of Chinese health authorities
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Fong et al. (2020) [69]
+|  style="text-align: left;"|Gothai et al. (2021) [70]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Find a forecasting model (GROOWS) from a small dataset for Covid-19 cases
+|  style="text-align: left;"|Predict the growth and trend of Covid-19
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|PNN+cf
+|  style="text-align: left;"|LR; SVM; time series
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Archive
+|  style="text-align: left;"|172,479 documents from Johns Hopkins University Repository
-of Chinese health authorities
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Gothai et al. (2021) [70]
+|  style="text-align: left;"|Jain et al. (2021) [71]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Predict the growth and trend of Covid-19
+|  style="text-align: left;"|Predict Covid-19
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|LR; SVM; time series
+|  style="text-align: left;"|SVM; Naïve Bayes; KNN; AdaBoost; GBoost; RF; ANN
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|172,479 documents from Johns Hopkins University
+|  style="text-align: left;"|B-cell dataset
-Repository
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Jain et al. (2021) [71]
+|  style="text-align: left;"|Kang et al. (2021) [72]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Predict Covid-19
+|  style="text-align: left;"|Predict severe Covid-19 cases
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|SVM; Naïve Bayes; KNN; AdaBoost; GBoost; RF; ANN
+|  style="text-align: left;"|ANN
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|B-cell dataset
+|  style="text-align: left;"|151 cases of a China center
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Kang et al. (2021) [72]
+|  style="text-align: left;"|Kavadi et al. (2020) [73]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Predict severe Covid-19 cases
+|  style="text-align: left;"|Global pandemic prediction of Covid-19
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|ANN
+|  style="text-align: left;"|PDR-NML method
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|151 cases of a China center
+|  style="text-align: left;"|Kaggle
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Kavadi et al. (2020) [73]
+|  style="text-align: left;"|Khan et al. (2021) [74]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Global pandemic prediction of Covid-19
+|  style="text-align: left;"|Predict the time after which the number of cases stops rising in India
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|PDR-NML
+|  style="text-align: left;"|DT; SVM; GPR
+|  style="text-align: left;"|Ministry of Health and Family Welfare (MoHFW) on 10th June 2020
-method
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Kaggle
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Khan et al. (2021) [74]
+|  style="text-align: left;"|Lmater et al. (2021) [75]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Predict the time after which the number of cases stops rising in India
+|  style="text-align: left;"|Present an effective mathematical model for predicting the spread of the (Covid-19) pandemic.
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|DT; SVM;
+|  style="text-align: left;"|SIDR model (susceptible, infected, diagnosed and recovered stages)
+|  style="text-align: left;"|Epidemiological data from 4 countries: Belgium; Morocco; Netherlands; Russia
-GPR
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Ministry of Health and Family Welfare (MoHFW) on
-th June 2020
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Lmater et al. (2021) [75]
+|  style="text-align: left;"|Malefors et al. (2021) [76]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Present an effective mathematical model for predicting the spread of the (Covid-19) pandemic.
+|  style="text-align: left;"|Predict guest attendance during the pandemic (meal planning in Sweden)
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|SIDR model (susceptible, infected, diagnosed and recovered stages)
+|  style="text-align: left;"|RF; ANN
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Epidemiological data from 4 countries: Belgium; Morocco; Netherlands; Russia
+|  style="text-align: left;"|Data from 18 primary school kitchens and 16 preschool kitchens
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Malefors et al. (2021) [76]
+|  style="text-align: left;"|Mojjada et al. (2020) [77]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Predict guest attendance during the pandemic (meal planning in Sweden)
+|  style="text-align: left;"|Show the ability to predict the number of individuals who are affected by Covid-19.
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|RF; ANN
+|  style="text-align: left;"|LASSO; SVM; LR
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Data from 18 primary school kitchens and 16 preschool kitchens
+|  style="text-align: left;"|Git Hub, supplied by Johns Hopkins University
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Mojjada et al. (2020) [77]
+|  style="text-align: left;"|Nemati et al. (2020) [78]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Show the ability to predict the number of individuals who are affected by Covid-19.
+|  style="text-align: left;"|Predict patients’ period of stay in hospital
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|LASSO; SVM;
+|  style="text-align: left;"|7 ML and statistical analysis techniques
+|  style="text-align: left;"|1,182 hospitalized patients
-LR
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Git Hub, supplied
-by Johns Hopkins University
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Nemati et al. (2020) [78]
+|  style="text-align: left;"|Ong et al. (2020) [79]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Predict patients’
+|  style="text-align: left;"|Predict Covid-19 vaccine candidates
+|  style="text-align: left;"|Vaxign reverse vaccinology tools
-period of stay in hospital
+|  style="text-align: left;"|ClinicalTrials.gov database and PubMed literature
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|7 ML and statistical analysis
-techniques
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|1,182 hospitalized patients
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Ong et al. (2020) [79]
+|  style="text-align: left;"|Papastefanopoulos et al. (2020) [80]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Predict Covid-19 vaccine candidates
+|  style="text-align: left;"|Investigate the accuracy of six time series for coronavirus to forecast active cases per population
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Vaxign reverse vaccinology tools
+|  style="text-align: left;"|Six time series
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|ClinicalTrials.gov database
+|  style="text-align: left;"|Kaggle; population-by-country dataset
-and PubMed literature.
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Papastefanopoulos et al. (2020) [80]
+|  style="text-align: left;"|Peng & Nagata (2020) [81]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Investigate the accuracy of six time series for coronavirus to forecast active cases per population
+|  style="text-align: left;"|Predict the number of Covid-19 cases for the 12 most affected countries
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Six time series
+|  style="text-align: left;"|SVR
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Kaggle; population-by-country dataset
+|  style="text-align: left;"|12 most affected countries
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Peng & Nagata (2020) [81]
+|  style="text-align: left;"|Pinter et al. (2020) [82]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Predict the number of Covid-19 cases for the 12 most affected countries
+|  style="text-align: left;"|Predict the Covid-19 pandemic for Hungary
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|SVR
+|  style="text-align: left;"|Hybrid ML: ANFIS and MLP-ICA
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|12 most affected countries
+|  style="text-align: left;"|Worldometer for Hungary
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Pinter et al. (2020) [82]
+|  style="text-align: left;"|Pourhomayoun & Shakibi (2021) [83]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Predict the Covid-19 pandemic for Hungary
+|  style="text-align: left;"|Determine the risk and predict the mortality risk of patients with Covid-19
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Hybrid ML: ANFIS and MLP-ICA
+|  style="text-align: left;"|SVM; ANN; RF; DT; LoR; KNN
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Worldometer for Hungary
+|  style="text-align: left;"|2,670,000 Covid-19 patients from 146 countries
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Pourhomayoun & Shakibi (2021) [83]
+|  style="text-align: left;"|Quintero et al. (2021) [84]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Determine the risk and predict the mortality risk of patients with Covid-19
+|  style="text-align: left;"|Predict the SEIRD variables based on a deep dependence on them
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|SVM; ANN; RF; DT; LoR; KNN
+|  style="text-align: left;"|GA; AR; ARIMA
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|2,670,000 Covid-19 patients from 146 countries
+|  style="text-align: left;"|The National Institute of Health for Colombia and the National Administrative Department of Statistics
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Quintero et al. (2021) [84]
+|  style="text-align: left;"|Ribeiro et al. (2020) [85]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Predict the SEIRD variables based on a deep dependence on them
+|  style="text-align: left;"|Develop short-term forecasting models to allow forecasting of the number of cases in the future
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|GA; AR;
+|  style="text-align: left;"|ARIMA; CUBIST; RF; RIDGE; SVR; SVR
+|  style="text-align: left;"|Cases in Brazil up to April, 19 of 2020; 10 datasets
-ARIMA
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|The National Institute of Health for Colombia and the National Administrative Department of Statistics
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Ribeiro et al. (2020) [85]
+|  style="text-align: left;"|Santosh (2020) [86]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Develop short-term forecasting models to allow forecasting of the number of cases in the future
+|  style="text-align: left;"|Develop AI-driven tools to identify Covid-19 outbreaks
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|ARIMA;
+|  style="text-align: left;"|AL
+|  style="text-align: left;"|Multitudinal and Multimodal data
-CUBIST; RF; RIDGE; SVR; SVR
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Cases in Brazil up to April, 19 of 2020; 10 datasets
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Santosh (2020) [86]
+|  style="text-align: left;"|Shahid et al. (2021) [6]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Develop AI-driven tools to identify Covid-19 outbreaks
+|  style="text-align: left;"|Predict virus detection, spread prevention and medical assistance
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|AL
+|  style="text-align: left;"|survey of ML algorithms and models
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Multitudinal and Multimodal data
+|  style="text-align: left;"|---
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Shahid et al. (2021) [6]
+|  style="text-align: left;"|V. Singh et al. (2020) [87]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Predict virus detection, spread prevention and medical assistance
+|  style="text-align: left;"|Produce a real-time SEIR model of confirmed, deceased, and recovered Covid-19 cases.
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|survey of ML algorithms and models
+|  style="text-align: left;"|SVM; time series
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|---
+|  style="text-align: left;"|Johns Hopkins CSSE; data from January 22, 2020 to April 25, 2020
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|V. Singh et al. (2020) [87]
+|  style="text-align: left;"|Sujath et al. (2020) [88]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Produce a real-time SEIR model of confirmed, deceased, and recovered Covid-19 cases.
+|  style="text-align: left;"|Predict the spread of Covid-2019
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|SVM; time series
+|  style="text-align: left;"|LR; MLP; VAR
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Johns Hopkins CSSE; data from January 22, 2020 to April 25, 2020
+|  style="text-align: left;"|Kaggle; Indian database
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Sujath et al. (2020) [88]
+|  style="text-align: left;"|Tarik et al. (2021) [89]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Predict the spread of Covid-2019
+|  style="text-align: left;"|Predict Moroccan student performance
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|LR; MLP; VAR
+|  style="text-align: left;"|RF; DT; LR
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Kaggle; Indian database
+|  style="text-align: left;"|Referral system
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Tarik et al. (2021) [89]
+|  style="text-align: left;"|Tuli et al. (2020) [90]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Predict Moroccan student performance
+|  style="text-align: left;"|Analyze and predict the growth of the epidemic
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|RF; DT; LR
+|  style="text-align: left;"|GIWD in a cloud computing platform
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Referral system
+|  style="text-align: left;"|Our World in Data by Hannah Ritchie
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Tuli et al. (2020) [90]
+|  style="text-align: left;"|Wadhwa et al. (2021) [91]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Analyze and predict the growth of the epidemic
+|  style="text-align: left;"|Predict the extension of lockdown in order to eradicate Covid-19 from India.
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|GIWD in a
+|  style="text-align: left;"|LR
+|  style="text-align: left;"|Total number of cases, deaths, and recoveries all over India.
-cloud computing platform
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Our World in Data by Hannah Ritchie
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Wadhwa et al. (2021) [91]
+|  style="text-align: left;"|P. Wang et al. (2020) [92]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Predict the extension of lockdown in order to eradicate
+|  style="text-align: left;"|Predict epidemic trends
+|  style="text-align: left;"|LoR
-Covid-19 from India.
+|  style="text-align: left;"|Johns Hopkins University, from January 22, 2020 to June 16, 2020.
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|LR
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Total number of
-cases, deaths, and recoveries all over India.
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|P. Wang et al. (2020) [92]
+|  style="text-align: left;"|Yan et al. (2020) [93]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Predict epidemic trends
+|  style="text-align: left;"|Identify crucial predictive biomarkers of Covid-19 mortality
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|LoR
+|  style="text-align: left;"|XGBoost
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Johns Hopkins University, from January 22, 2020 to June 16, 2020.
+|  style="text-align: left;"|485 patients
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Yan et al. (2020) [93]
+|  style="text-align: left;"|Yadav et al. (2020) [94]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Identify crucial predictive biomarkers of Covid-19 mortality
+|  style="text-align: left;"|Solve 5 different tasks: I) Predict the spread of the disease; II) Analyze the growth rates; III) Predict how the pandemic will end; IV) Analyze the transmission rate; and V) Correlate the disease to the weather conditions.
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|XGBoost
+|  style="text-align: left;"|SVR; SLR; PR
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|485 patients
+|  style="text-align: left;"|Data from different countries
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Yadav et al. (2020) [94]
+|  style="text-align: left;"|Yeşilkanat (2020) [95]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Solve 5 different tasks:
+|  style="text-align: left;"|Estimate the number of future cases for 190 countries in the world
+|  style="text-align: left;"|RF
-I) Predict the spread of the disease; II) Analyze the growth rates; III) Predict how the pandemic will end; IV) Analyze the transmission rate; and V) Correlate the disease to the weather conditions.
+|  style="text-align: left;"|Johns Hopkins University Center for Systems Science; Engineering
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|SVR; SLR; PR
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Data from different countries
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Yeşilkanat (2020) [95]
+|  style=" text-align: left;"|Zivkovic et al. (2021) [96]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Estimate the number of future cases for 190 countries in the world
+|  style=" text-align: left;"|Predict the number of new coronavirus cases
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|RF
+|  style=" text-align: left;"|ANFIS; BASSI
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Johns Hopkins University Center for Systems Science; Engineering
+|  style=" text-align: left;"|6 benchmark Functions
-|-
-|  style="border-top: 1pt solid black;border-bottom: 2pt solid black;text-align: center;"|Zivkovic et al. (2021) [96]
-|  style="border-top: 1pt solid black;border-bottom: 2pt solid black;text-align: center;"|Predict the number of new coronavirus cases
-|  style="border-top: 1pt solid black;border-bottom: 2pt solid black;text-align: center;"|ANFIS; BASSI
-|  style="border-top: 1pt solid black;border-bottom: 2pt solid black;text-align: center;"|6 benchmark
-Functions
 |}
-Acronyms (alphabetical): See Table 1.
+Acronyms (alphabetical): See [[#tab-1|Table 1]].
-Finally, the last 38 of the 117 selected articles that address general subjects involving ML techniques and Covid-19 are listed in '''Table 4'''. These include, for example, that of Di Castelnuovo et al. [97], who attempted to list those that aimed to identify the characteristics predisposing Covid-19 patients to in-hospital death. For this purpose, the authors used the data of 3,894 patients from 30 clinical centers distributed throughout Italy, who were hospitalized from February 19th to May 23rd, 2020. The authors used the RF technique to achieve their goal. They concluded that impaired renal function, elevated C-reactive protein, and advanced age were major predictors of in-hospital death.
-<div class="center" style="width: auto; margin-left: auto; margin-right: auto;">
+Finally, the last 38 of the 117 selected articles that address general subjects involving ML techniques and Covid-19 are listed in [[#tab-4|Table 4]]. These include, for example, that of Di Castelnuovo et al. [97], who attempted to list those that aimed to identify the characteristics predisposing Covid-19 patients to in-hospital death. For this purpose, the authors used the data of 3,894 patients from 30 clinical centers distributed throughout Italy, who were hospitalized from February 19th to May 23rd, 2020. The authors used the RF technique to achieve their goal. They concluded that impaired renal function, elevated C-reactive protein, and advanced age were major predictors of in-hospital death.
-'''Table 4.''' Works (38) related to Covid-19 that used ML techniques involving general subjects.</div>
-{| style="width: 100%;border-collapse: collapse;"
+<div class="center" style="font-size: 75%;">'''Table 4'''. Works (38) related to Covid-19 that used ML techniques involving general subjects</div>
+<div id='tab-4'></div>
+{| class="wikitable" style="margin: 1em auto 0.1em auto;border-collapse: collapse;font-size:85%;width:auto;"
+|-style="text-align:center"
+! style="background-color: #d1e0df;text-align:left;"| Authors (year) !!  style="background-color:#d1e0df;text-align:left;"| Focus of the paper !!  style="background-color: #d1e0df;text-align:left;"| ML Techniques !! style="background-color: #d1e0df;text-align:left;"| Databases
 |-
-|  style="border-top: 2pt solid black;border-bottom: 1pt solid black;text-align: center;"|'''Authors (year)'''
+|  style="text-align: left;"|Alves et al. (2021) [98]
-|  style="border-top: 2pt solid black;border-bottom: 1pt solid black;text-align: center;"|'''Focus of the paper'''
+|  style="text-align: left;"|Present understandable solutions to deal with Covid-19 screening in routine blood tests
-|  style="border-top: 2pt solid black;border-bottom: 1pt solid black;text-align: center;"|'''ML Techniques'''
+|  style="text-align: left;"|DT Explainer and criteria graphs
-|  style="border-top: 2pt solid black;border-bottom: 1pt solid black;text-align: center;"|'''Databases'''
+|  style="text-align: left;"|608 patients; public dataset from the Albert Einstein Hospital, São Paulo
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Alves et al. (2021) [98]
+|  style="text-align: left;"|Baralić et al. (2020) [99]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Present understandable solutions to deal with Covid-19 screening in routine blood tests
+|  style="text-align: left;"|Assess risks and benefits of Covid- 19 treatment with promising drug combinations: lopinavir/ritonavir and chloroquine/hydroxychloroquine+ azithromycin.
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|DT
+|  style="text-align: left;"|''in silico ''toxicogenomic data-mining approach
+|  style="text-align: left;"|Comparative Toxicogenomics Database
-Explainer and criteria graphs
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|608 patients; public dataset from the Albert Einstein Hospital, São Paulo
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Baralić et al. (2020) [99]
+|  style="text-align: left;"|Carrillo-Larco & Castillo-Cara (2020) [100]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Assess risks and benefits of Covid- 19 treatment with promising drug combinations: lopinavir/ritonavir and chloroquine/hydroxychloroquine+ azithromycin.
+|  style="text-align: left;"|Clustering countries which shared profiles of the pandemic
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|''in silico ''toxicogenomic data-mining approach
+|  style="text-align: left;"|k-means; statistical techniques
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Comparative Toxicogenomics Database
+|  style="text-align: left;"|155 countries; Johns Hopkins University and others
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Carrillo-Larco & Castillo-Cara (2020) [100]
+|  style="text-align: left;"|Di Castelnuovo et al. (2020) [97]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Clustering countries which shared profiles of the pandemic
+|  style="text-align: left;"|Identify the characteristics predisposing Covid-19 patients to in-hospital death.
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|k-means; statistical techniques
+|  style="text-align: left;"|RF
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|155 countries; Johns Hopkins University and others
+|  style="text-align: left;"|3,894 patients hospitalized from a defined period (Italy)
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Di Castelnuovo et al. (2020) [97]
+|  style="text-align: left;"|Choudrie et al. (2021) [101]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Identify the characteristics predisposing Covid-19 patients
+|  style="text-align: left;"|Explore how ML techniques and experienced people process the online infodemic related to prevention and cure
+|  style="text-align: left;"|DT; CNN
-to in-hospital death.
+|  style="text-align: left;"|143 patients
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|RF
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|3,894 patients
-hospitalized from a defined period (Italy)
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Choudrie et al. (2021) [101]
+|  style="text-align: left;"|Dandekar et al. (2020) [102]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Explore how ML techniques and experienced people process the online infodemic related to prevention and cure
+|  style="text-align: left;"|Develop a globally applicable diagnostic Covid-19 model
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|DT; CNN
+|  style="text-align: left;"|SIR; NN
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|143 patients
+|  style="text-align: left;"|70 countries
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Dandekar et al. (2020) [102]
+|  style="text-align: left;"|Doanvo et al. (2020) [15]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Develop a globally applicable diagnostic Covid-19 model
+|  style="text-align: left;"|Identify knowledge research Covid-19 gaps in the literature
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|SIR; NN
+|  style="text-align: left;"|PCA
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|70 countries
+|  style="text-align: left;"|35,281 abstracts from CORD-19
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Doanvo et al. (2020) [15]
+|  style="text-align: left;"|Fong et al. (2020) [103]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Identify knowledge research Covid-19 gaps in the literature
+|  style="text-align: left;"|Gain stochastic insights into the pandemic development
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|PCA
+|  style="text-align: left;"|CMC: DL; FRI
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|35,281 abstracts from CORD-19
+|  style="text-align: left;"|Empirical data from the Chinese CDCP
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Fong et al. (2020) [103]
+|  style="text-align: left;"|Godavarthi & Sowjanya (2021) [104]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Gain stochastic insights into the pandemic development
+|  style="text-align: left;"|Extract information from the scientific literature: text classification
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|CMC: DL; FRI
+|  style="text-align: left;"|KNN; MLP; XGBoost
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Empirical data from the Chinese CDCP
+|  style="text-align: left;"|CORD-19 dataset
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Godavarthi & Sowjanya (2021) [104]
+|  style="text-align: left;"|Hu et al. (2021) [105]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Extract information from the scientific literature: text classification
+|  style="text-align: left;"|Detect the changes in air pollutants during Covid-19 lockdown
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|KNN; MLP; XGBoost
+|  style="text-align: left;"|RF models
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|CORD-19 dataset
+|  style="text-align: left;"|Data from 35 sites in Beijing, from 2015 to 2020
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Hu et al. (2021) [105]
+|  style="text-align: left;"|Jamshidi et al. (2020) [106]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Detect the changes in air pollutants during Covid-19 lockdown
+|  style="text-align: left;"|Present a response to combat the virus through AI
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|RF models
+|  style="text-align: left;"|GANs; LSTM; ELM
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Data from 35 sites in Beijing, from 2015 to 2020
+|  style="text-align: left;"|---
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Jamshidi et al. (2020) [106]
+|  style="text-align: left;"|Kadioglu et al. (2021) [107]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Present a response to combat the virus through AI
+|  style="text-align: left;"|Identify compounds against three targets of Covid-19
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|GANs; LSTM; ELM
+|  style="text-align: left;"|Pharmaco strategy'' in silico''
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|---
+|  style="text-align: left;"|Chemical libraries (FDA-approved drugs; natural compound datasets; ZINC database)
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Kadioglu et al. (2021) [107]
+|  style="text-align: left;"|Khanday et al. (2020) [108]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Identify compounds against three targets of Covid-19
+|  style="text-align: left;"|Detect Covid-19 through clinical text data
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Pharmaco strategy'' in silico''
+|  style="text-align: left;"|LoR; MNB
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Chemical libraries (FDA-approved drugs; natural compound datasets; ZINC database)
+|  style="text-align: left;"|Data repository GitHub
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Khanday et al. (2020) [108]
+|  style="text-align: left;"|Kuo & Fu (2021) [109]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Detect Covid-19 through clinical text data
+|  style="text-align: left;"|Analyze demographic and environmental impact and mobility during the pandemic period
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|LoR; MNB
+|  style="text-align: left;"|Elastic net model; PCR; PLSR; KNN;  RT; RF; GBM; 2-layer ANN
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Data repository GitHub
+|  style="text-align: left;"|New York Times; USDA ERA; gridMed; Google
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Kuo & Fu (2021) [109]
+|  style="text-align: left;"|Lam et al. (2021) [110]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Analyze demographic and environmental impact and mobility during the pandemic period
+|  style="text-align: left;"|Present a ML system capable of identifying patients who could be treated with a corticosteroid or remdesivir
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Elastic net model; PCR; PLSR; KNN;  RT; RF; GBM; 2-layer ANN
+|  style="text-align: left;"|GBM
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|New York Times; USDA ERA; gridMed; Google
+|  style="text-align: left;"|893 patients
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Lam et al. (2021) [110]
+|  style="text-align: left;"|M. Li et al. (2021) [16]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Present a ML system capable of identifying patients who could be treated with a corticosteroid or remdesivir
+|  style="text-align: left;"|Detect novel critical factors associated with Covid-19 in 154 countries and in the 50 USA states
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|GBM
+|  style="text-align: left;"|LoR; LASSO
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|893 patients
+|  style="text-align: left;"|Johns Hopkins University
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|M. Li et al. (2021) [16]
+|  style="text-align: left;"|Lip et al. (2021) [111]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Detect novel critical factors associated with Covid-19 in 154 countries and in the 50 USA states
+|  style="text-align: left;"|Identify patients with Covid-19 who are at the highest risk of developing incident AF
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|LoR; LASSO
+|  style="text-align: left;"|Inferential statistics and ML computations (LoR)
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Johns Hopkins University
+|  style="text-align: left;"|Data from April 1, 2018 to Nov 30, 2020
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Lip et al. (2021) [111]
+|  style="text-align: left;"|Loey et al. (2021) [112]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Identify patients with Covid-19 who are at the highest risk of developing incident AF
+|  style="text-align: left;"|Develop a DL and classical ML for face detection
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Inferential statistics and ML computations (LoR)
+|  style="text-align: left;"|DL; DT; SVM; EA
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Data from April 1, 2018 to Nov 30, 2020
+|  style="text-align: left;"|3 datasets
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Loey et al. (2021) [112]
+|  style="text-align: left;"|Lovrić et al. (2021) [113]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Develop a DL and classical ML for face detection
+|  style="text-align: left;"|Analyze improvements in air quality during the Covid-19 lockdown
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|DL; DT; SVM; EA
+|  style="text-align: left;"|RFR
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|3 datasets
+|  style="text-align: left;"|Graz, Styria, Austria
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Lovrić et al. (2021) [113]
+|  style="text-align: left;"|Magazzino et al. (2021) [114]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Analyze improvements in air quality during the Covid-19 lockdown
+|  style="text-align: left;"|Analyze the relationship between Covid-19 deaths, economic growth and air pollution
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|RFR
+|  style="text-align: left;"|DL
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Graz, Styria, Austria
+|  style="text-align: left;"|--
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Magazzino et al. (2021) [114]
+|  style="text-align: left;"|McRae et al. (2020) [115]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Analyze the relationship between Covid-19 deaths, economic growth and air pollution
+|  style="text-align: left;"|Develop a decision support tool and rapid point-of-care platform to determine severity in patients with Covid-19
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|DL
+|  style="text-align: left;"|Statistical learning algorithm
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|--
+|  style="text-align: left;"|160 patients from Wuhan, China
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|McRae et al. (2020) [115]
+|  style="text-align: left;"|Malki et al. (2020) [116]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Develop a decision support tool and rapid point-of-care platform to determine severity in patients with Covid-19
+|  style="text-align: left;"|Verify the relationship between weather and Covid-19
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Statistical learning algorithm
+|  style="text-align: left;"|Regressor ML models
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|160 patients from Wuhan, China
+|  style="text-align: left;"|Meteoblue website
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Malki et al. (2020) [116]
+|  style="text-align: left;"|Mele & Magazzino (2021) [117]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Verify the relationship between weather and Covid-19
+|  style="text-align: left;"|Explore the relationship between pollution, economic growth and Covid-19 deaths in India
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Regressor ML models
+|  style="text-align: left;"|Time Series approach; Stationarity and Toda-Yamamoto causality tests
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Meteoblue website
+|  style="text-align: left;"|Indian data from January 29 to May 18, 2020
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Mele & Magazzino (2021) [117]
+|  style="text-align: left;"|Petetin et al. (2020) [118]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Explore the relationship between pollution, economic growth and
+|  style="text-align: left;"|Use meteorological data to estimate the “business-as-usual” NO2 mixing ratios
+|  style="text-align: left;"|GBM
-Covid-19 deaths in India
+|  style="text-align: left;"|GHOST
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Time Series approach; Stationarity
-and Toda-Yamamoto causality tests
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Indian data from January 29 to May 18, 2020
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Petetin et al. (2020) [118]
+|  style="text-align: left;"|Qiang et al. (2020) [119]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Use meteorological
+|  style="text-align: left;"|Evaluate the infection risk of Covid-19 for early warning through spike protein feature
+|  style="text-align: left;"|RF models
-data to estimate the
+|  style="text-align: left;"|507 human origin viruses and 2,159 non-human-origin viruses
-“business-as-usual” NO2 mixing ratios
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|GBM
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|GHOST
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Qiang et al. (2020) [119]
+|  style="text-align: left;"|Radanliev et al. (2020) [120]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Evaluate the infection risk of Covid-19 for early warning through spike protein feature
+|  style="text-align: left;"|Investigate the scientific research response from the early stages of the pandemic
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|RF models
+|  style="text-align: left;"|Statistical methods
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|507 human origin
+|  style="text-align: left;"|WSCC
-viruses and 2,159 non-human-origin viruses
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Radanliev et al. (2020) [120]
+|  style="text-align: left;"|Randhawa et al. (2020) [121]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Investigate the scientific research response from the early stages of the pandemic
+|  style="text-align: left;"|Use intrinsic genomic signatures to classify Covid-19 rapidly
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Statistical methods
+|  style="text-align: left;"|MLDSP for genome analyses; DT
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|WSCC
+|  style="text-align: left;"|Dataset of over 5,000 unique viral genomic sequences from the NCBI
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Randhawa et al. (2020) [121]
+|  style="text-align: left;"|Shrock et al. (2020) [122]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Use intrinsic genomic signatures to classify Covid-19 rapidly
+|  style="text-align: left;"|Explore antiviral antibody responses across the human virome
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|MLDSP for genome analyses; DT
+|  style="text-align: left;"|XGBoost
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Dataset of over 5,000 unique viral genomic sequences from the NCBI
+|  style="text-align: left;"|232 coronavirus disease patients and 190 pre-Covid-19
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Shrock et al. (2020) [122]
+|  style="text-align: left;"|X. Sun et al. (2020) [123]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Explore antiviral antibody
+|  style="text-align: left;"|Explore TCM formulae to investigate their compatibility with the CMM to understand their potential mechanisms for treatment of Covid-19
+|  style="text-align: left;"|TCM; CMM
-responses across the human virome
+|  style="text-align: left;"|Encyclopedia of Traditional Chinese Medicine database; BATMAN-TCM database
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|XGBoost
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|232 coronavirus disease patients and 190 pre-Covid-19
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|X. Sun et al. (2020) [123]
+|  style="text-align: left;"|C. L. F. Sun et al. (2020) [124]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Explore TCM formulae to investigate their compatibility with the CMM to understand their potential mechanisms for treatment of Covid-19
+|  style="text-align: left;"|Identify risks and vectors of infection in nursing homes
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|TCM; CMM
+|  style="text-align: left;"|GBA
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Encyclopedia of Traditional Chinese Medicine database; BATMAN-TCM database
+|  style="text-align: left;"|1146 NHs in Massachusetts
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|C. L. F. Sun et al. (2020) [124]
+|  style="text-align: left;"|Swapnarekha et al. (2020) [125]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Identify risks and vectors of infection in nursing homes
+|  style="text-align: left;"|Present a state-of-the-art analysis using ML and DL methods in the diagnosis and prediction of Covid-19
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|GBA
+|  style="text-align: left;"|ML; DL
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|1146 NHs in Massachusetts
+|  style="text-align: left;"|January 23, 2020 to April, 21, 2020
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Swapnarekha et al. (2020) [125]
+|  style="text-align: left;"|S. Tiwari et al. (2020) [126]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Present a state-of-the-art analysis using ML and DL methods in the diagnosis and prediction of Covid-19
+|  style="text-align: left;"|Prepare Indian government and citizens to take control measures (SEIR)
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|ML; DL
+|  style="text-align: left;"|Time Series
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|January 23, 2020 to April, 21, 2020
+|  style="text-align: left;"|Kaggle (data available between January 22, 2020, and April 3, 2020, from India and China)
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|S. Tiwari et al. (2020) [126]
+|  style="text-align: left;"|A. Tiwari et al. (2021) [127]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Prepare Indian government and citizens to take control measures (SEIR)
+|  style="text-align: left;"|Define a Covid-19 Vulnerability Index (C19VI) for identifying and mapping counties considered vulnerable
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Time Series
+|  style="text-align: left;"|RF
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Kaggle (data available between January 22, 2020, and April 3, 2020, from India and China)
+|  style="text-align: left;"|Johns Hopkins University; Centers for Disease Control and Prevention
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|A. Tiwari et al. (2021) [127]
+|  style="text-align: left;"|Toğaçar et al. (2020) [128]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Define a Covid-19 Vulnerability Index (C19VI) for identifying and mapping counties considered vulnerable
+|  style="text-align: left;"|Detect Coronavirus
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|RF
+|  style="text-align: left;"|DL; SVM; SMOM
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Johns Hopkins University; Centers for Disease Control and Prevention
+|  style="text-align: left;"|GitHub; Kaggle
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Toğaçar et al. (2020) [128]
+|  style="text-align: left;"|Vaishya et al. (2020) [129]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Detect Coronavirus
+|  style="text-align: left;"|Revise the effectiveness of AI techniques for Covid-19
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|DL; SVM; SMOM
+|  style="text-align: left;"|AI techniques
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|GitHub; Kaggle
+|  style="text-align: left;"|PubMed, Scopus and Google Scholar datasets
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Vaishya et al. (2020) [129]
+|  style="text-align: left;"|W.-C. Wang et al. (2021) [130]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Revise the effectiveness of AI techniques for Covid-19
+|  style="text-align: left;"|Develop a system for monitoring global and local community outbreaks
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|AI techniques
+|  style="text-align: left;"|k-means
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|PubMed, Scopus and Google Scholar datasets
+|  style="text-align: left;"|Johns Hopkins; data with daily infected, recovered and death cases
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|W.-C. Wang et al. (2021) [130]
+|  style="text-align: left;"|Yacchirema & Chura (2021) [131]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Develop a system for monitoring global and local community outbreaks
+|  style="text-align: left;"|Implement a system based on IoT for saver mobility during the pandemic
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|k-means
+|  style="text-align: left;"|SVM; DT; LoR; RF; KNN (to detect the location of people)
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Johns Hopkins; data with daily infected, recovered and death cases
+|  style="text-align: left;"|From portable IoT devices
-|-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Yacchirema & Chura (2021) [131]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Implement a system based on IoT for saver mobility during the pandemic
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|SVM; DT; LoR; RF; KNN
-(to detect the location of people)
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|From portable IoT devices
 |-
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Yang et al. (2020) [132]
+|  style="text-align: left;"|Yang et al. (2020) [132]
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Demonstrate control measures impact the containment of the epidemic
+|  style="text-align: left;"|Demonstrate control measures impact the containment of the epidemic
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|SEIR model
+|  style="text-align: left;"|SEIR model
-|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|2003 SARS data
+|  style="text-align: left;"|2003 SARS data
 |}
-Acronyms (alphabetical): See Table 1.
+Acronyms (alphabetical): See [[#tab-1|Table 1]].
-==4.2 Bibliometric Literature Review==
+===4.2 Bibliometric literature review===
 Of the 117 articles analyzed on the theme of the use of ML techniques in the study of Covid-19, 67 (57%) are from the year 2020, and the other 50 articles (43%) are from 2021, up to the month of June.
-Furthermore, of the 117 articles, 10 were published in “Chaos, Solitons and Fractals” and eight in “Computers in Biology and Medicine”. The top 54% of journals with the highest number of publications are presented in '''Fig. 2'''.
+Furthermore, of the 117 articles, 10 were published in “Chaos, Solitons and Fractals” and eight in “Computers in Biology and Medicine”. The top 54% of journals with the highest number of publications are presented in [[#img-2|Figure 2]].
-<div class="center" style="width: auto; margin-left: auto; margin-right: auto;">
+<div id='img-2'></div>
-'''Figure 2.''' Top 54% of journals in the selected papers.</div>
+{| style="text-align: center; border: 1px solid #BBB; margin: 1em auto; width: 70%;"
+|-
+|style="padding:10px;"| [[File:Steiner_et_al_2022a_2680_Figure_2.png|500px]]
+|- style="text-align: center; font-size: 75%;"
+| colspan="1" style="padding:10px;"| '''Figure 2'''. Top 54% of journals in the selected papers
+|}
-<div class="center" style="width: auto; margin-left: auto; margin-right: auto;">
-[[File:Review_936792395077_5667_Figure 2.svg|600px]] </div>
+The two main ML techniques identified by the bibliometric analysis were Random Forest (RF) and Deep Learning (DL). The most used methods, employed in 61% of the publications, are presented in [[#img-3|Figure 3]].
-The two main ML techniques identified by the bibliometric analysis were Random Forest (RF) and Deep Learning (DL). The most used methods, employed in 61% of the publications, are presented in '''Fig. 3'''.
+<div id='img-3'></div>
+{| style="text-align: center; border: 1px solid #BBB; margin: 1em auto; width: 60%;"
-<div class="center" style="width: auto; margin-left: auto; margin-right: auto;">
+|-
-'''Figure 3.''' Top 61% of publications with the most used methods.</div>
+|style="padding:10px;"| [[File:Steiner_et_al_2022a_9166_Figure_3.png|500px]]
+|- style="text-align: center; font-size: 75%;"
-<div class="center" style="width: auto; margin-left: auto; margin-right: auto;">
+| colspan="1" style="padding:10px;"| '''Figure 3'''. Top 61% of publications with the most used methods
- [[File:Review_936792395077_1718_Figure 3.svg|600px]] </div>
+|}
 It is noticed the predominance of classical algorithms, such as RF, SVM, and DT, in addition to modern techniques, such as DL and CNN. This shows that classical methods still have space in current scientific research, even in new applications, as in the case of Covid-19.
-Finally, considering the nationality of the authors, the USA and India drew with 30 researchers each, followed by China, with 20 researchers. '''Fig. 4''' shows 70% of the most frequent nationalities of the authors of the 117 articles.
+Finally, considering the nationality of the authors, the USA and India drew with 30 researchers each, followed by China, with 20 researchers. [[#img-4|Figure 4]] shows 70% of the most frequent nationalities of the authors of the 117 articles.
-<div class="center" style="width: auto; margin-left: auto; margin-right: auto;">
-'''Figure 4.''' Top 70% of authors' nationality.</div>
-<div class="center" style="width: auto; margin-left: auto; margin-right: auto;">
-[[File:Review_936792395077_2204_Figure 4.svg|600px]] </div>
+<div id='img-4'></div>
+{| style="text-align: center; border: 1px solid #BBB; margin: 1em auto; width: 60%;"
+|-
+|style="padding:10px;"| [[File:Steiner_et_al_2022a_3203_Figure_4.png|500px]]
+|- style="text-align: center; font-size: 75%;"
+| colspan="1" style="padding:10px;"| '''Figure 4'''. Top 70% of authors' nationality
 The first three positions, in relation to nationality, refer to the two countries with the highest number of cases of Covid-19 (USA and India) and the country where the virus was identified (China).
-=5. Concluding Remarks=
+==5. Concluding Remarks==
-The aim of the present study was to conduct a systematic and bibliometric review of the articles involving the protocol shown in '''Fig. 2''' which, being the most frequently cited in the literature, set out to answer, among other questions, why certain patients are severely affected by the virus while others are asymptomatic. M. Li ''et al.'' [16], for example, commented that age, sex, temperature, humidity, social distancing, smoking, investments in health, level of urbanization and race can influence the severity of the disease. On the other hand, Di Castelnuovo ''et al.'' [97] concluded that impaired renal function, elevated C-reactive protein and advanced age were major predictors of in-hospital death. However, there are many patients who fit these specific conditions, but who present different degrees of aggravation of the disease.
+The aim of the present study was to conduct a systematic and bibliometric review of the articles involving the protocol shown in [[#img-2|Figure 2]] which, being the most frequently cited in the literature, set out to answer, among other questions, why certain patients are severely affected by the virus while others are asymptomatic. M. Li et al. [16], for example, commented that age, sex, temperature, humidity, social distancing, smoking, investments in health, level of urbanization and race can influence the severity of the disease. On the other hand, Di Castelnuovo et al. [97] concluded that impaired renal function, elevated C-reactive protein and advanced age were major predictors of in-hospital death. However, there are many patients who fit these specific conditions, but who present different degrees of aggravation of the disease.
 We firmly believe that the answer to this question is directly found in very recent studies that reveal a possible genetic predisposition to serious cases of Covid-19 [133,134,135]. Researchers discovered that more severe cases of the disease are associated with the low performance of molecules that identify the virus and are inherited from parents: class I human leukocyte antigen (HLA-I) molecules represent the group of molecules responsible for identifying and distinguishing everything that is in the body and what is not. Six HLA-I molecules, found on the surface of all cells, form a unique set for each individual, which is determined by the genes received from the parents [135]. In other words, it is very important to investigate whether there is a direct link between the seriousness of the disease and the performance of HLA-I in the identification of Sars-CoV-2.
 Therefore, it would be very interesting and promising to analyze the genotype of patients who have suffered Covid-19 and healthy people, employing ML techniques and classifying patients, for instance, as serious, moderate, or mild cases.
+<!--==Declaration of competing interest==
-==Declaration of competing interest==
 None.
+-->
 ==Funding sources==
@@ Line 912: / Line 815: @@
 ==References==
+<div class="auto" style="text-align: left;width: auto; margin-left: auto; margin-right: auto;font-size: 85%;">
-[1] WHO. Coronavirus disease (COVID-19) pandemic. Retrieved from [https://www.who.int/emergencies/diseases/novel-coronavirus-2019 https://www.who.int/emergencies/diseases/novel-coronavirus-2019] 2021.
+[1] WHO. Coronavirus disease (COVID-19) pandemic. Retrieved from [https://www.who.int/emergencies/diseases/novel-coronavirus-2019 https://www.who.int/emergencies/diseases/novel-coronavirus-2019], 2021.
-[2] Piret J., Boivin G. Pandemics Throughout History. Front. Microbiol., 11(January), 2021.
+[2] Piret J., Boivin G. Pandemics throughout history. Front. Microbiol., 15 January, 2021.
-[3] Huremović D. Brief History of Pandemics (Pandemics Throughout History). In Psychiatry of Pandemics, Springer International Publishing, Cham, pp. 7–35, 2019.
+[3] Huremović D. Brief history of pandemics (Pandemics Throughout History). Psychiatry of Pandemics, Springer International Publishing, Cham, pp. 7–35, 2019.
 [4] Carvalho T., Krammer F., Iwasaki A. The first 12 months of COVID-19: a timeline of immunological insights. Nat. Rev. Immunol., 21(4):245–256, 2021.
-[5] WHO. WHO Coronavirus (COVID-19) Dashboard. Retrieved from [https://covid19.who.int/ https://covid19.who.int/] 2021.
+[5] WHO. WHO Coronavirus (COVID-19) Dashboard. Retrieved from [https://covid19.who.int/ https://covid19.who.int/], 2021.
-[6] Shahid O., Nasajpour M., Pouriyeh S., et al. Machine learning research towards combating COVID-19: Virus detection, spread prevention, and medical assistance. J. Biomed. Inform., 117(October 2020):103751, 2021.
+[6] Shahid O., Nasajpour M., Pouriyeh S., et al. Machine learning research towards combating COVID-19: Virus detection, spread prevention, and medical assistance. J. Biomed. Inform., 117:103751, 2021.
-[7] Prabha B., Kaur S., Singh J., Nandankar P., Kumar Jain S., Pallathadka H. Intelligent predictions of Covid disease based on lung CT images using machine learning strategy. Mater. Today Proc., xxxx), 2021.
+[7] Prabha B., Kaur S., Singh J., Nandankar P., Kumar Jain S., Pallathadka H. Intelligent predictions of Covid disease based on lung CT images using machine learning strategy. Mater. Today Proc., Online July 2021.
-[8] Haridy S., Maged A., Baker A.W., Shamsuzzaman M., Bashir H., Xie M. Monitoring scheme for early detection of coronavirus and other respiratory virus outbreaks. Comput. Ind. Eng., 156(June 2020):107235, 2021.
+[8] Haridy S., Maged A., Baker A.W., Shamsuzzaman M., Bashir H., Xie M. Monitoring scheme for early detection of coronavirus and other respiratory virus outbreaks. Comput. Ind. Eng., 156:107235, 2021.
 [9] Waleed Salehi A., Baglat P., Gupta G. Review on machine and deep learning models for the detection and prediction of Coronavirus. Mater. Today Proc., 33:3896–3901, 2020.
-[10] dos Santos B.S., Steiner M.T.A., Fenerich A.T., Lima R.H.P. Data mining and machine learning techniques applied to public health problems: A bibliometric analysis from 2009 to 2018. Comput. Ind. Eng., 138(April):106120, 2019.
+[10] dos Santos B.S., Steiner M.T.A., Fenerich A.T., Lima R.H.P. Data mining and machine learning techniques applied to public health problems: A bibliometric analysis from 2009 to 2018. Comput. Ind. Eng., 138:106120, 2019.
 [11] Carroll L.N., Au A.P., Detwiler L.T., Fu T., Painter I.S., Abernethy N.F. Visualization and analytics tools for infectious disease epidemiology: A systematic review. J. Biomed. Inform., 51:287–298, 2014.
-[12] Dallora A.L., Eivazzadeh S., Mendes E., Berglund J., Anderberg P. Prognosis of Dementia Employing Machine Learning and Microsimulation Techniques: A Systematic Literature Review. Procedia Comput. Sci., 100:480–488, 2016.
+[12] Dallora A.L., Eivazzadeh S., Mendes E., Berglund J., Anderberg P. Prognosis of dementia employing machine learning and microsimulation techniques: A systematic literature review. Procedia Comput. Sci., 100:480–488, 2016.
 [13] Bellinger C., Mohomed Jabbar M.S., Zaïane O., Osornio-Vargas A. A systematic review of data mining and machine learning for air pollution epidemiology. BMC Public Health, 17(1):907, 2017.
-[14] Somasekar J., Pavan Kumar P., Sharma A., Ramesh G. Machine learning and image analysis applications in the fight against COVID-19 pandemic: Datasets, research directions, challenges and opportunities. Mater. Today Proc., xxxx):3–6, 2020.
+[14] Somasekar J., Pavan Kumar P., Sharma A., Ramesh G. Machine learning and image analysis applications in the fight against COVID-19 pandemic: Datasets, research directions, challenges and opportunities. Mater. Today Proc., 3–6, 2020.
 [15] Doanvo A., Qian X., Ramjee D., Piontkivska H., Desai A., Majumder M. Machine Learning Maps Research Needs in COVID-19 Literature. Patterns, 1(9):100123, 2020.
@@ Line 945: / Line 849: @@
 [16] Li M., Zhang Z., Cao W., et al. Identifying novel factors associated with COVID-19 transmission and fatality using the machine learning approach. Sci. Total Environ., 764(639):142810, 2021.
-[17] Snyder H. Literature review as a research methodology: An overview and guidelines. J. Bus. Res., 104(July):333–339, 2019.
+[17] Snyder H. Literature review as a research methodology: An overview and guidelines. J. Bus. Res., 104:333–339, 2019.
-[18] Xiao Y., Watson M. Guidance on Conducting a Systematic Literature Review. J. Plan. Educ. Res., 39(1):93–112, 2019.
+[18] Xiao Y., Watson M. Guidance on conducting a systematic literature review. J. Plan. Educ. Res., 39(1):93–112, 2019.
-[19] Ardakani A.A., Kanafi A.R., Acharya U.R., Khadem N., Mohammadi A. Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks. Comput. Biol. Med., 121(January):103795, 2020.
+[19] Ardakani A.A., Kanafi A.R., Acharya U.R., Khadem N., Mohammadi A. Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks. Comput. Biol. Med., 121:103795, 2020.
-[20] Cai W., Liu T., Xue X., et al. CT Quantification and Machine-learning Models for Assessment of Disease Severity and Prognosis of COVID-19 Patients. Acad. Radiol., 27(12):1665–1678, 2020.
+[20] Cai W., Liu T., Xue X., et al. CT Quantification and machine-learning models for assessment of disease severity and prognosis of COVID-19 patients. Acad. Radiol., 27(12):1665–1678, 2020.
 [21] Chowdhury M.E.H., Rahman T., Khandakar A., et al. Can AI Help in Screening Viral and COVID-19 Pneumonia? IEEE Access, 8:132665–132676, 2020.
@@ Line 961: / Line 865: @@
 [24] Brunese L., Martinelli F., Mercaldo F., Santone A. Machine learning for coronavirus covid-19 detection from chest x-rays. Procedia Comput. Sci., 176:2212–2221, 2020.
-[25] Chakraborty S., Mali K. SUFMACS: A machine learning-based robust image segmentation framework for COVID-19 radiological image interpretation. Expert Syst. Appl., 178(April):115069, 2021.
+[25] Chakraborty S., Mali K. SUFMACS: A machine learning-based robust image segmentation framework for COVID-19 radiological image interpretation. Expert Syst. Appl., 178:115069, 2021.
 [26] Elaziz M.A., Hosny K.M., Salah A., Darwish M.M., Lu S., Sahlol A.T. New machine learning method for image-based diagnosis of COVID-19. PLoS One, 15(6):e0235187, 2020.
-[27] Vijay kumar J., Harshavardhan A., Bhukya H., Krishna Prasad A.V. Advanced machine learning-based analytics on COVID-19 data using generative adversarial networks. Mater. Today Proc., xxxx), 2020.
+[27] Vijay kumar J., Harshavardhan A., Bhukya H., Krishna Prasad A.V. Advanced machine learning-based analytics on COVID-19 data using generative adversarial networks. Mater. Today Proc., 2020.
-[28] Loey M., Smarandache F., M. Khalifa N.E. Within the Lack of Chest COVID-19 X-ray Dataset: A Novel Detection Model Based on GAN and Deep Transfer Learning. Symmetry (Basel)., 12(4):651, 2020.
+[28] Loey M., Smarandache F., M. Khalifa N.E. Within the lack of chest COVID-19 X-ray dataset: A novel detection model based on GAN and deep transfer learning. Symmetry (Basel), 12(4):651, 2020.
 [29] Saha P., Sadi M.S., Islam M.M. EMCNet: Automated COVID-19 diagnosis from X-ray images using convolutional neural network and ensemble of machine learning classifiers. Informatics Med. Unlocked, 22:100505, 2021.
@@ Line 973: / Line 877: @@
 [30] Saygılı A. A new approach for computer-aided detection of coronavirus (COVID-19) from CT and X-ray images using machine learning methods. Appl. Soft Comput., 105:107323, 2021.
-[31] Sedik A., Iliyasu A.M., Abd El-Rahiem B., et al. Deploying Machine and Deep Learning Models for Efficient Data-Augmented Detection of COVID-19 Infections. Viruses, 12(7):769, 2020.
+[31] Sedik A., Iliyasu A.M., Abd El-Rahiem B., et al. Deploying machine and deep learning models for efficient data-augmented detection of COVID-19 infections. Viruses, 12(7):769, 2020.
-[32] Sethy P.K., Behera S.K., Ratha P.K., Biswas P. Detection of coronavirus Disease (COVID-19) based on Deep Features and Support Vector Machine. Int. J. Math. Eng. Manag. Sci., 5(4):643–651, 2020.
+[32] Sethy P.K., Behera S.K., Ratha P.K., Biswas P. Detection of coronavirus disease (COVID-19) based on deep features and support vector machine. Int. J. Math. Eng. Manag. Sci., 5(4):643–651, 2020.
 [33] Shiri I., Sorouri M., Geramifar P., et al. Machine learning-based prognostic modeling using clinical data and quantitative radiomic features from chest CT images in COVID-19 patients. Comput. Biol. Med., 132:104304, 2021.
@@ Line 981: / Line 885: @@
 [34] Singh D., Kumar V., Vaishali, Kaur M. Classification of COVID-19 patients from chest CT images using multi-objective differential evolution–based convolutional neural networks. Eur. J. Clin. Microbiol. Infect. Dis., 39(7):1379–1389, 2020.
-[35] Tamal M., Alshammari M., Alabdullah M., Hourani R., Alola H.A., Hegazi T.M. An integrated framework with machine learning and radiomics for accurate and rapid early diagnosis of COVID-19 from Chest X-ray. Expert Syst. Appl., 180(November 2020):115152, 2021.
+[35] Tamal M., Alshammari M., Alabdullah M., Hourani R., Alola H.A., Hegazi T.M. An integrated framework with machine learning and radiomics for accurate and rapid early diagnosis of COVID-19 from Chest X-ray. Expert Syst. Appl., 180:115152, 2021.
-[36] Tartaglione E., Barbano C.A., Berzovini C., Calandri M., Grangetto M. Unveiling COVID-19 from CHEST X-Ray with Deep Learning: A Hurdles Race with Small Data. Int. J. Environ. Res. Public Health, 17(18):6933, 2020.
+[36] Tartaglione E., Barbano C.A., Berzovini C., Calandri M., Grangetto M. Unveiling COVID-19 from CHEST X-Ray with deep learning: A hurdles race with small data. Int. J. Environ. Res. Public Health, 17(18):6933, 2020.
-[37] Wang X., Deng X., Fu Q., et al. A Weakly-Supervised Framework for COVID-19 Classification and Lesion Localization From Chest CT. IEEE Trans. Med. Imaging, 39(8):2615–2625, 2020.
+[37] Wang X., Deng X., Fu Q., et al. A weakly-supervised framework for COVID-19 classification and lesion localization from chest CT. IEEE Trans. Med. Imaging, 39(8):2615–2625, 2020.
-[38] Waheed A., Goyal M., Gupta D., Khanna A., Al-Turjman F., Pinheiro P.R. CovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved Covid-19 Detection. IEEE Access, 8:91916–91923, 2020.
+[38] Waheed A., Goyal M., Gupta D., Khanna A., Al-Turjman F., Pinheiro P.R. CovidGAN: Data augmentation using auxiliary classifier GAN for improved Covid-19 detection. IEEE Access, 8:91916–91923, 2020.
-[39] Wu Z., Li L., Jin R., et al. Texture feature-based machine learning classifier could assist in the diagnosis of COVID-19. Eur. J. Radiol., 137(August 2020):109602, 2021.
+[39] Wu Z., Li L., Jin R., et al. Texture feature-based machine learning classifier could assist in the diagnosis of COVID-19. Eur. J. Radiol., 137:109602, 2021.
-[40] Çalı S., Balaman Ş.Y. Improved decisions for marketing, supply and purchasing: Mining big data through an integration of sentiment analysis and intuitionistic fuzzy multi criteria assessment. Comput. Ind. Eng., 129(April 2018):315–332, 2019.
+[40] Çalı S., Balaman Ş.Y. Improved decisions for marketing, supply and purchasing: Mining big data through an integration of sentiment analysis and intuitionistic fuzzy multi criteria assessment. Comput. Ind. Eng., 129:315–332, 2019.
-[41] Gulati K., Saravana Kumar S., Sarath Kumar Boddu R., Sarvakar K., Kumar Sharma D., Nomani M.Z.M. Comparative analysis of machine learning-based classification models using sentiment classification of tweets related to COVID-19 pandemic. Mater. Today Proc., xxxx):1–4, 2021.
+[41] Gulati K., Saravana Kumar S., Sarath Kumar Boddu R., Sarvakar K., Kumar Sharma D., Nomani M.Z.M. Comparative analysis of machine learning-based classification models using sentiment classification of tweets related to COVID-19 pandemic. Mater. Today Proc., 51(1):38-41, 2022.
-[42] Haupt M.R., Jinich-Diamant A., Li J., Nali M., Mackey T.K. Characterizing twitter user topics and communication network dynamics of the “Liberate” movement during COVID-19 using unsupervised machine learning and social network analysis. Online Soc. Networks Media, 21(December 2020):100114, 2021.
+[42] Haupt M.R., Jinich-Diamant A., Li J., Nali M., Mackey T.K. Characterizing twitter user topics and communication network dynamics of the “Liberate” movement during COVID-19 using unsupervised machine learning and social network analysis. Online Soc. Networks Media, 21:100114, 2021.
 [43] Praveen S.V., Ittamalla R., Deepak G. Analyzing Indian general public’s perspective on anxiety, stress and trauma during Covid-19 - A machine learning study of 840,000 tweets. Diabetes Metab. Syndr. Clin. Res. Rev., 15(3):667–671, 2021.
-[44] Abd-Alrazaq A., Alhuwail D., Househ M., Hamdi M., Shah Z. Top Concerns of Tweeters During the COVID-19 Pandemic: Infoveillance Study. J. Med. Internet Res., 22(4):e19016, 2020.
+[44] Abd-Alrazaq A., Alhuwail D., Househ M., Hamdi M., Shah Z. Top concerns of tweeters during the COVID-19 pandemic: Infoveillance study. J. Med. Internet Res., 22(4):e19016, 2020.
-[45] Gupta M., Bansal A., Jain B., Rochelle J., Oak A., Jalali M.S. Whether the weather will help us weather the COVID-19 pandemic: Using machine learning to measure twitter users’ perceptions. Int. J. Med. Inform., 145(September 2020):104340, 2021.
+[45] Gupta M., Bansal A., Jain B., Rochelle J., Oak A., Jalali M.S. Whether the weather will help us weather the COVID-19 pandemic: Using machine learning to measure twitter users’ perceptions. Int. J. Med. Inform., 145:104340, 2021.
-[46] Hou K., Hou T., Cai L. Public attention about COVID-19 on social media: An investigation based on data mining and text analysis. Pers. Individ. Dif., 175(September 2020):110701, 2021.
+[46] Hou K., Hou T., Cai L. Public attention about COVID-19 on social media: An investigation based on data mining and text analysis. Pers. Individ. Dif., 175:110701, 2021.
-[47] Kabir M.Y., Madria S. EMOCOV: Machine learning for emotion detection, analysis and visualization using COVID-19 tweets. Online Soc. Networks Media, 23(September 2020):100135, 2021.
+[47] Kabir M.Y., Madria S. EMOCOV: Machine learning for emotion detection, analysis and visualization using COVID-19 tweets. Online Soc. Networks Media, 23:100135, 2021.
-[48] Kyriazos T., Galanakis M., Karakasidou E., Stalikas A. Early COVID-19 quarantine: A machine learning approach to model what differentiated the top 25% well-being scorers. Pers. Individ. Dif., 181(April):110980, 2021.
+[48] Kyriazos T., Galanakis M., Karakasidou E., Stalikas A. Early COVID-19 quarantine: A machine learning approach to model what differentiated the top 25% well-being scorers. Pers. Individ. Dif., 181:110980, 2021.
-[49] Li S., Wang Y., Xue J., Zhao N., Zhu T. The Impact of COVID-19 Epidemic Declaration on Psychological Consequences: A Study on Active Weibo Users. Int. J. Environ. Res. Public Health, 17(6):2032, 2020.
+[49] Li S., Wang Y., Xue J., Zhao N., Zhu T. The impact of COVID-19 epidemic declaration on psychological consequences: A study on active weibo users. Int. J. Environ. Res. Public Health, 17(6):2032, 2020.
-[50] Mackey T., Purushothaman V., Li J., et al. Machine Learning to Detect Self-Reporting of Symptoms, Testing Access, and Recovery Associated With COVID-19 on Twitter: Retrospective Big Data Infoveillance Study. JMIR Public Heal. Surveill., 6(2):e19509, 2020.
+[50] Mackey T., Purushothaman V., Li J., et al. Machine learning to detect self-reporting of symptoms, testing access, and recovery associated with COVID-19 on twitter: Retrospective big data infoveillance study. JMIR Public Heal. Surveill., 6(2):e19509, 2020.
-[51] Samuel J., Ali G.G.M.N., Rahman M.M., Esawi E., Samuel Y. COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification. Information, 11(6):314, 2020.
+[51] Samuel J., Ali G.G.M.N., Rahman M.M., Esawi E., Samuel Y. COVID-19 public sentiment insights and machine learning for tweets classification. Information, 11(6):314, 2020.
 [52] Satu M.S., Khan M.I., Mahmud M., et al. TClustVID: A novel machine learning classification model to investigate topics and sentiment in COVID-19 tweets. Knowledge-Based Syst., 226:107126, 2021.
-[53] Shah A.M., Yan X., Qayyum A., Naqvi R.A., Shah S.J. Mining topic and sentiment dynamics in physician rating websites during the early wave of the COVID-19 pandemic: Machine learning approach. Int. J. Med. Inform., 149(February):104434, 2021.
+[53] Shah A.M., Yan X., Qayyum A., Naqvi R.A., Shah S.J. Mining topic and sentiment dynamics in physician rating websites during the early wave of the COVID-19 pandemic: Machine learning approach. Int. J. Med. Inform., 149:104434, 2021.
 [54] Amar L.A., Taha A.A., Mohamed M.Y. Prediction of the final size for COVID-19 epidemic using machine learning: A case study of Egypt. Infect. Dis. Model., 5:622–634, 2020.
@@ Line 1,023: / Line 927: @@
 [55] Burdick H., Lam C., Mataraso S., et al. Prediction of respiratory decompensation in Covid-19 patients using machine learning: The READY trial. Comput. Biol. Med., 124:103949, 2020.
-[56] Ardabili S.F., Mosavi A., Ghamisi P., et al. COVID-19 Outbreak Prediction with Machine Learning. Algorithms, 13(10):249, 2020.
+[56] Ardabili S.F., Mosavi A., Ghamisi P., et al. COVID-19 outbreak prediction with machine learning. Algorithms, 13(10):249, 2020.
 [57] Arvind V., Kim J.S., Cho B.H., Geng E., Cho S.K. Development of a machine learning algorithm to predict intubation among hospitalized patients with COVID-19. J. Crit. Care, 62:25–30, 2021.
-[58] ArunKumar K.E., Kalaga D. V., Sai Kumar C.M., Chilkoor G., Kawaji M., Brenza T.M. Forecasting the dynamics of cumulative COVID-19 cases (confirmed, recovered and deaths) for top-16 countries using statistical machine learning models: Auto-Regressive Integrated Moving Average (ARIMA) and Seasonal Auto-Regressive Integrated Moving Averag. Appl. Soft Comput., 103(December 2019):107161, 2021.
+[58] ArunKumar K.E., Kalaga D. V., Sai Kumar C.M., Chilkoor G., Kawaji M., Brenza T.M. Forecasting the dynamics of cumulative COVID-19 cases (confirmed, recovered and deaths) for top-16 countries using statistical machine learning models: Auto-Regressive Integrated Moving Average (ARIMA) and Seasonal Auto-Regressive Integrated Moving Averag. Appl. Soft Comput., 103:107161, 2021.
 [59] Aydin N., Yurdakul G. Assessing countries’ performances against COVID-19 via WSIDEA and machine learning algorithms. Appl. Soft Comput., 97:106792, 2020.
-[60] Ayyoubzadeh S.M., Ayyoubzadeh S.M., Zahedi H., Ahmadi M., R Niakan Kalhori S. Predicting COVID-19 Incidence Through Analysis of Google Trends Data in Iran: Data Mining and Deep Learning Pilot Study. JMIR Public Heal. Surveill., 6(2):e18828, 2020.
+[60] Ayyoubzadeh S.M., Ayyoubzadeh S.M., Zahedi H., Ahmadi M., R Niakan Kalhori S. Predicting COVID-19 incidence through analysis of Google trends data in Iran: Data mining and deep learning pilot study. JMIR Public Heal. Surveill., 6(2):e18828, 2020.
 [61] Ballı S. Data analysis of Covid-19 pandemic and short-term cumulative case forecasting using machine learning time series methods. Chaos, Solitons & Fractals, 142:110512, 2021.
@@ Line 1,041: / Line 945: @@
 [64] Chakraborti S., Maiti A., Pramanik S., et al. Evaluating the plausible application of advanced machine learnings in exploring determinant factors of present pandemic: A case for continent specific COVID-19 analysis. Sci. Total Environ., 765:142723, 2021.
-[65] Chatterjee A., Gerdes M.W., Martinez S.G. Statistical Explorations and Univariate Timeseries Analysis on COVID-19 Datasets to Understand the Trend of Disease Spreading and Death. Sensors, 20(11):3089, 2020.
+[65] Chatterjee A., Gerdes M.W., Martinez S.G. Statistical explorations and univariate timeseries analysis on COVID-19 datasets to understand the trend of disease spreading and death. Sensors, 20(11):3089, 2020.
 [66] Chimmula V.K.R., Zhang L. Time series forecasting of COVID-19 transmission in Canada using LSTM networks. Chaos, Solitons & Fractals, 135:109864, 2020.
@@ Line 1,047: / Line 951: @@
 [67] Cobre A. de F., Stremel D.P., Noleto G.R., et al. Diagnosis and prediction of COVID-19 severity: can biochemical tests and machine learning be used as prognostic indicators? Comput. Biol. Med., 134:104531, 2021.
-[68] Ebinger J., Wells M., Ouyang D., et al. A Machine Learning Algorithm Predicts Duration of hospitalization in COVID-19 patients. Intell. Med., 5:100035, 2021.
+[68] Ebinger J., Wells M., Ouyang D., et al. A machine learning algorithm predicts duration of hospitalization in COVID-19 patients. Intell. Med., 5:100035, 2021.
-[69] Fong S.J., Li G., Dey N., Crespo R.G., Herrera-Viedma E. Finding an Accurate Early Forecasting Model from Small Dataset: A Case of 2019-nCoV Novel Coronavirus Outbreak. Int. J. Interact. Multimed. Artif. Intell., 6(1):132, 2020.
+[69] Fong S.J., Li G., Dey N., Crespo R.G., Herrera-Viedma E. Finding an accurate early forecasting model from small dataset: A case of 2019-nCoV novel coronavirus outbreak. Int. J. Interact. Multimed. Artif. Intell., 6(1):132, 2020.
-[70] Gothai E., Thamilselvan R., Rajalaxmi R.R., Sadana R.M., Ragavi A., Sakthivel R. Prediction of COVID-19 growth and trend using machine learning approach. Mater. Today Proc., xxxx), 2021.
+[70] Gothai E., Thamilselvan R., Rajalaxmi R.R., Sadana R.M., Ragavi A., Sakthivel R. Prediction of COVID-19 growth and trend using machine learning approach. Mater. Today Proc., April 2021.
 [71] Jain N., Jhunthra S., Garg H., et al. Prediction modelling of COVID using machine learning methods from B-cell dataset. Results Phys., 21:103813, 2021.
-[72] Kang J., Chen T., Luo H., Luo Y., Du G., Jiming-Yang M. Machine learning predictive model for severe COVID-19. Infect. Genet. Evol., 90(August 2020):104737, 2021.
+[72] Kang J., Chen T., Luo H., Luo Y., Du G., Jiming-Yang M. Machine learning predictive model for severe COVID-19. Infect. Genet. Evol., 90:104737, 2021.
-[73] Kavadi D.P., Patan R., Ramachandran M., Gandomi A.H. Partial derivative Nonlinear Global Pandemic Machine Learning prediction of COVID 19. Chaos, Solitons & Fractals, 139:110056, 2020.
+[73] Kavadi D.P., Patan R., Ramachandran M., Gandomi A.H. Partial derivative nonlinear global pandemic machine learning prediction of COVID 19. Chaos, Solitons & Fractals, 139:110056, 2020.
 [74] Khan F.M., Kumar A., Puppala H., Kumar G., Gupta R. Projecting the criticality of COVID-19 transmission in India using GIS and machine learning methods. J. Saf. Sci. Resil., 2(2):50–62, 2021.
@@ Line 1,063: / Line 967: @@
 [75] Lmater M.A., Eddabbah M., Elmoussaoui T., Boussaa S. Modelization of Covid-19 pandemic spreading: A machine learning forecasting with relaxation scenarios of countermeasures. J. Infect. Public Health, 14(4):468–473, 2021.
-[76] Malefors C., Secondi L., Marchetti S., Eriksson M. Food waste reduction and economic savings in times of crisis: The potential of machine learning methods to plan guest attendance in Swedish public catering during the Covid-19 pandemic. Socioecon. Plann. Sci., February):101041, 2021.
+[76] Malefors C., Secondi L., Marchetti S., Eriksson M. Food waste reduction and economic savings in times of crisis: The potential of machine learning methods to plan guest attendance in Swedish public catering during the Covid-19 pandemic. Socioecon. Plann. Sci., 82(A):101041, 2022.
-[77] Mojjada R.K., Yadav A., Prabhu A.V., Natarajan Y. Machine learning models for covid-19 future forecasting. Mater. Today Proc., xxxx), 2020.
+[77] Mojjada R.K., Yadav A., Prabhu A.V., Natarajan Y. Machine learning models for covid-19 future forecasting. Mater. Today Proc., December 2020.
-[78] Nemati M., Ansary J., Nemati N. Machine-Learning Approaches in COVID-19 Survival Analysis and Discharge-Time Likelihood Prediction Using Clinical Data. Patterns, 1(5):100074, 2020.
+[78] Nemati M., Ansary J., Nemati N. Machine-learning approaches in COVID-19 survival analysis and discharge-time likelihood prediction using clinical data. Patterns, 1(5):100074, 2020.
-[79] Ong E., Wong M.U., Huffman A., He Y. COVID-19 Coronavirus Vaccine Design Using Reverse Vaccinology and Machine Learning. Front. Immunol., 11(July):1581, 2020.
+[79] Ong E., Wong M.U., Huffman A., He Y. COVID-19 coronavirus vaccine design using reverse vaccinology and machine learning. Front. Immunol., 11(July):1581, 2020.
-[80] Papastefanopoulos V., Linardatos P., Kotsiantis S. COVID-19: A Comparison of Time Series Methods to Forecast Percentage of Active Cases per Population. Appl. Sci., 10(11):3880, 2020.
+[80] Papastefanopoulos V., Linardatos P., Kotsiantis S. COVID-19: A comparison of time series methods to forecast percentage of active cases per population. Appl. Sci., 10(11):3880, 2020.
 [81] Peng Y., Nagata M.H. An empirical overview of nonlinearity and overfitting in machine learning using COVID-19 data. Chaos, Solitons & Fractals, 139:110055, 2020.
-[82] Pinter G., Felde I., Mosavi A., Ghamisi P., Gloaguen R. COVID-19 Pandemic Prediction for Hungary; A Hybrid Machine Learning Approach. SSRN Electron. J., 2020.
+[82] Pinter G., Felde I., Mosavi A., Ghamisi P., Gloaguen R. COVID-19 pandemic prediction for Hungary; A hybrid machine learning approach. SSRN Electron. J., 8(6):890, 2020.
-[83] Pourhomayoun M., Shakibi M. Predicting mortality risk in patients with COVID-19 using machine learning to help medical decision-making. Smart Heal., 20(April 2020):100178, 2021.
+[83] Pourhomayoun M., Shakibi M. Predicting mortality risk in patients with COVID-19 using machine learning to help medical decision-making. Smart Heal., 20:100178, 2021.
-[84] Quintero Y., Ardila D., Camargo E., Rivas F., Aguilar J. Machine learning models for the prediction of the SEIRD variables for the COVID-19 pandemic based on a deep dependence analysis of variables. Comput. Biol. Med., 134(May):104500, 2021.
+[84] Quintero Y., Ardila D., Camargo E., Rivas F., Aguilar J. Machine learning models for the prediction of the SEIRD variables for the COVID-19 pandemic based on a deep dependence analysis of variables. Comput. Biol. Med., 134:104500, 2021.
 [85] Ribeiro M.H.D.M., da Silva R.G., Mariani V.C., Coelho L. dos S. Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for Brazil. Chaos, Solitons & Fractals, 135:109853, 2020.
-[86] Santosh K.C. AI-Driven Tools for Coronavirus Outbreak: Need of Active Learning and Cross-Population Train/Test Models on Multitudinal/Multimodal Data. J. Med. Syst., 44(5):93, 2020.
+[86] Santosh K.C. AI-driven tools for coronavirus outbreak: Need of active learning and cross-population train/test models on multitudinal/multimodal data. J. Med. Syst., 44(5):93, 2020.
 [87] Singh V., Poonia R.C., Kumar S., et al. Prediction of COVID-19 corona virus pandemic based on time series data using support vector machine. J. Discret. Math. Sci. Cryptogr., 23(8):1583–1597, 2020.
@@ Line 1,089: / Line 993: @@
 [88] Sujath R., Chatterjee J.M., Hassanien A.E. A machine learning forecasting model for COVID-19 pandemic in India. Stoch. Environ. Res. Risk Assess., 34(7):959–972, 2020.
-[89] Tarik A., Aissa H., Yousef F. Artificial Intelligence and Machine Learning to Predict Student Performance during the COVID-19. Procedia Comput. Sci., 184:835–840, 2021.
+[89] Tarik A., Aissa H., Yousef F. Artificial intelligence and machine learning to predict student performance during the COVID-19. Procedia Comput. Sci., 184:835–840, 2021.
 [90] Tuli S., Tuli S., Tuli R., Gill S.S. Predicting the growth and trend of COVID-19 pandemic using machine learning and cloud computing. Internet of Things, 11:100222, 2020.
@@ Line 1,103: / Line 1,007: @@
 [95] Yeşilkanat C.M. Spatio-temporal estimation of the daily cases of COVID-19 in worldwide using random forest machine learning algorithm. Chaos, Solitons & Fractals, 140:110210, 2020.
-[96] Zivkovic M., Bacanin N., Venkatachalam K., et al. COVID-19 cases prediction by using hybrid machine learning and beetle antennae search approach. Sustain. Cities Soc., 66(November 2020):102669, 2021.
+[96] Zivkovic M., Bacanin N., Venkatachalam K., et al. COVID-19 cases prediction by using hybrid machine learning and beetle antennae search approach. Sustain. Cities Soc., 66:102669, 2021.
 [97] Di Castelnuovo A., Bonaccio M., Costanzo S., et al. Common cardiovascular risk factors and in-hospital mortality in 3,894 patients with COVID-19: survival analysis and machine learning-based findings from the multicentre Italian CORIST Study. Nutr. Metab. Cardiovasc. Dis., 30(11):1899–1913, 2020.
-[98] Alves M.A., Castro G.Z., Oliveira B.A.S., et al. Explaining machine learning based diagnosis of COVID-19 from routine blood tests with decision trees and criteria graphs. Comput. Biol. Med., 132(February):104335, 2021.
+[98] Alves M.A., Castro G.Z., Oliveira B.A.S., et al. Explaining machine learning based diagnosis of COVID-19 from routine blood tests with decision trees and criteria graphs. Comput. Biol. Med., 132:104335, 2021.
-[99] Baralić K., Jorgovanović D., Živančević K., et al. Safety assessment of drug combinations used in COVID-19 treatment: in silico toxicogenomic data-mining approach. Toxicol. Appl. Pharmacol., 406(May):115237, 2020.
+[99] Baralić K., Jorgovanović D., Živančević K., et al. Safety assessment of drug combinations used in COVID-19 treatment: in silico toxicogenomic data-mining approach. Toxicol. Appl. Pharmacol., 406:115237, 2020.
 [100] Carrillo-Larco R.M., Castillo-Cara M. Using country-level variables to classify countries according to the number of confirmed COVID-19 cases: An unsupervised machine learning approach. Wellcome Open Res., 5:56, 2020.
-[101] Choudrie J., Banerjee S., Kotecha K., Walambe R., Karende H., Ameta J. Machine learning techniques and older adults processing of online information and misinformation: A covid 19 study. Comput. Human Behav., 119(January):106716, 2021.
+[101] Choudrie J., Banerjee S., Kotecha K., Walambe R., Karende H., Ameta J. Machine learning techniques and older adults processing of online information and misinformation: A covid 19 study. Comput. Human Behav., 119:106716, 2021.
-[102] Dandekar R., Rackauckas C., Barbastathis G. A Machine Learning-Aided Global Diagnostic and Comparative Tool to Assess Effect of Quarantine Control in COVID-19 Spread. Patterns, 1(9):100145, 2020.
+[102] Dandekar R., Rackauckas C., Barbastathis G. A machine learning-aided global diagnostic and comparative tool to assess effect of quarantine control in COVID-19 spread. Patterns, 1(9):100145, 2020.
-[103] Fong S.J., Li G., Dey N., Crespo R.G., Herrera-Viedma E. Composite Monte Carlo decision making under high uncertainty of novel coronavirus epidemic using hybridized deep learning and fuzzy rule induction. Appl. Soft Comput., 93(December 2019):106282, 2020.
+[103] Fong S.J., Li G., Dey N., Crespo R.G., Herrera-Viedma E. Composite Monte Carlo decision making under high uncertainty of novel coronavirus epidemic using hybridized deep learning and fuzzy rule induction. Appl. Soft Comput., 93:106282, 2020.
-[104] Godavarthi D., Sowjanya M. Classification of covid related articles using machine learning. Mater. Today Proc., xxxx), 2021.
+[104] Godavarthi D., Sowjanya M. Classification of covid related articles using machine learning. Mater. Today Proc., February 2021.
 [105] Hu J., Pan Y., He Y., et al. Changes in air pollutants during the COVID-19 lockdown in Beijing: Insights from a machine-learning technique and implications for future control policy. Atmos. Ocean. Sci. Lett., 14(4):100060, 2021.
-[106] Jamshidi M., Lalbakhsh A., Talla J., et al. Artificial Intelligence and COVID-19: Deep Learning Approaches for Diagnosis and Treatment. IEEE Access, 8(December 2019):109581–109595, 2020.
+[106] Jamshidi M., Lalbakhsh A., Talla J., et al. Artificial intelligence and COVID-19: deep learning approaches for diagnosis and treatment. IEEE Access, 8:109581–109595, 2020.
-[107] Kadioglu O., Saeed M., Greten H.J., Efferth T. Identification of novel compounds against three targets of SARS CoV-2 coronavirus by combined virtual screening and supervised machine learning. Comput. Biol. Med., 133(November 2020):104359, 2021.
+[107] Kadioglu O., Saeed M., Greten H.J., Efferth T. Identification of novel compounds against three targets of SARS CoV-2 coronavirus by combined virtual screening and supervised machine learning. Comput. Biol. Med., 133:104359, 2021.
 [108] Khanday A.M.U.D., Rabani S.T., Khan Q.R., Rouf N., Mohi Ud Din M. Machine learning based approaches for detecting COVID-19 using clinical text data. Int. J. Inf. Technol., 12(3):731–739, 2020.
@@ Line 1,131: / Line 1,035: @@
 [109] Kuo C.-P., Fu J.S. Evaluating the impact of mobility on COVID-19 pandemic with machine learning hybrid predictions. Sci. Total Environ., 758:144151, 2021.
-[110] Lam C., Siefkas A., Zelin N.S., et al. Machine Learning as a Precision-Medicine Approach to Prescribing COVID-19 Pharmacotherapy with Remdesivir or Corticosteroids. Clin. Ther., xxx(xxx):1–16, 2021.
+[110] Lam C., Siefkas A., Zelin N.S., et al. Machine learning as a precision-medicine approach to prescribing COVID-19 pharmacotherapy with remdesivir or corticosteroids. Clin. Ther., 43(5):871-885, 2021.
-[111] Lip G.Y.H., Genaidy A., Tran G., Marroquin P., Estes C. Incident atrial fibrillation and its risk prediction in patients developing COVID-19: A machine learning based algorithm approach. Eur. J. Intern. Med., April), 2021.
+[111] Lip G.Y.H., Genaidy A., Tran G., Marroquin P., Estes C. Incident atrial fibrillation and its risk prediction in patients developing COVID-19: A machine learning based algorithm approach. Eur. J. Intern. Med., 91:53-58, 2021.
-[112] Loey M., Manogaran G., Taha M.H.N., Khalifa N.E.M. A hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the COVID-19 pandemic. Measurement, 167(May 2020):108288, 2021.
+[112] Loey M., Manogaran G., Taha M.H.N., Khalifa N.E.M. A hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the COVID-19 pandemic. Measurement, 167:108288, 2021.
 [113] Lovrić M., Pavlović K., Vuković M., Grange S.K., Haberl M., Kern R. Understanding the true effects of the COVID-19 lockdown on air pollution by means of machine learning. Environ. Pollut., 274:115900, 2021.
-[114] Magazzino C., Mele M., Sarkodie S.A. The nexus between COVID-19 deaths, air pollution and economic growth in New York state: Evidence from Deep Machine Learning. J. Environ. Manage., 286(October 2020):112241, 2021.
+[114] Magazzino C., Mele M., Sarkodie S.A. The nexus between COVID-19 deaths, air pollution and economic growth in New York state: Evidence from Deep Machine Learning. J. Environ. Manage., 286:112241, 2021.
 [115] McRae M.P., Simmons G.W., Christodoulides N.J., et al. Clinical decision support tool and rapid point-of-care platform for determining disease severity in patients with COVID-19. Lab Chip, 20(12):2075–2085, 2020.
@@ Line 1,155: / Line 1,059: @@
 [121] Randhawa G.S., Soltysiak M.P.M., El Roz H., de Souza C.P.E., Hill K.A., Kari L. Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study. PLoS One, 15(4):e0232391, 2020.
-[122] Shrock E., Fujimura E., Kula T., et al. Viral epitope profiling of COVID-19 patients reveals cross-reactivity and correlates of severity. Science (80-. )., 370(6520):eabd4250, 2020.
+[122] Shrock E., Fujimura E., Kula T., et al. Viral epitope profiling of COVID-19 patients reveals cross-reactivity and correlates of severity. Science, 370(6520):eabd4250, 2020.
-[123] Sun X., Jiang J., Wang Y., Liu S. Exploring the potential therapeutic effect of traditional Chinese medicine on coronavirus disease 2019 (COVID-19) through a combination of data mining and network pharmacology analysis. Eur. J. Integr. Med., 40(October):101242, 2020.
+[123] Sun X., Jiang J., Wang Y., Liu S. Exploring the potential therapeutic effect of traditional Chinese medicine on coronavirus disease 2019 (COVID-19) through a combination of data mining and network pharmacology analysis. Eur. J. Integr. Med., 40:101242, 2020.
-[124] Sun C.L.F., Zuccarelli E., Zerhouni E.G.A., et al. Predicting Coronavirus Disease 2019 Infection Risk and Related Risk Drivers in Nursing Homes: A Machine Learning Approach. J. Am. Med. Dir. Assoc., 21(11):1533-1538.e6, 2020.
+[124] Sun C.L.F., Zuccarelli E., Zerhouni E.G.A., et al. Predicting coronavirus disease 2019 infection risk and related risk drivers in nursing homes: A machine learning approach. J. Am. Med. Dir. Assoc., 21(11):1533-1538.e6, 2020.
 [125] Swapnarekha H., Behera H.S., Nayak J., Naik B. Role of intelligent computing in COVID-19 prognosis: A state-of-the-art review. Chaos, Solitons & Fractals, 138:109947, 2020.
-[126] Tiwari S., Kumar S., Guleria K. Outbreak Trends of Coronavirus Disease–2019 in India: A Prediction. Disaster Med. Public Health Prep., 14(5):e33–e38, 2020.
+[126] Tiwari S., Kumar S., Guleria K. Outbreak trends of coronavirus disease–2019 in India: A prediction. Disaster Med. Public Health Prep., 14(5):e33–e38, 2020.
 [127] Tiwari A., Dadhania A. V., Ragunathrao V.A.B., Oliveira E.R.A. Using machine learning to develop a novel COVID-19 Vulnerability Index (C19VI). Sci. Total Environ., 773:145650, 2021.
-[128] Toğaçar M., Ergen B., Cömert Z. COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches. Comput. Biol. Med., 121(January):103805, 2020.
+[128] Toğaçar M., Ergen B., Cömert Z. COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches. Comput. Biol. Med., 121:103805, 2020.
 [129] Vaishya R., Javaid M., Khan I.H., Haleem A. Artificial Intelligence (AI) applications for COVID-19 pandemic. Diabetes Metab. Syndr. Clin. Res. Rev., 14(4):337–339, 2020.
-[130] Wang W.-C., Lin T.-Y., Chiu S.Y.-H., et al. Classification of community-acquired outbreaks for the global transmission of COVID-19: Machine learning and statistical model analysis. J. Formos. Med. Assoc., 120(xxxx):S26–S37, 2021.
+[130] Wang W.-C., Lin T.-Y., Chiu S.Y.-H., et al. Classification of community-acquired outbreaks for the global transmission of COVID-19: Machine learning and statistical model analysis. J. Formos. Med. Assoc., 120:S26–S37, 2021.
 [131] Yacchirema D., Chura A. SafeMobility: An IoT- based System for safer mobility using machine learning in the age of COVID-19. Procedia Comput. Sci., 184:524–531, 2021.
@@ Line 1,177: / Line 1,081: @@
 [132] Yang Z., Zeng Z., Wang K., et al. Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions. J. Thorac. Dis., 12(3):165–174, 2020.
-[133] The COVID-19 Host Genetics Initiative . Mapping the human genetic architecture of COVID-19. Nature,:2021.03.10.21252820, 2021.
+[133] COVID-19 Host Genetics Initiative. Mapping the human genetic architecture of COVID-19. Nature,  600:472–477, 2021.
 [134] Pairo-Castineira E., Clohisey S., Klaric L., et al. Genetic mechanisms of critical illness in COVID-19. Nature, 591(7848):92–98, 2021.
-[135] Shkurnikov M., Nersisyan S., Jankevic T., et al. Association of HLA Class I Genotypes With Severity of Coronavirus Disease-19. Front. Immunol., 12(February), 2021.
+[135] Shkurnikov M., Nersisyan S., Jankevic T., et al. Association of HLA class I genotypes with severity of coronavirus disease-19. Front. Immunol., 12:641900, 2021.