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.

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-|style="padding:10px;"| [[File:Review_936792395077_5667_Figure 2.svg|500px]]
+|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
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-|style="padding:10px;"| [[File:Review_936792395077_1718_Figure 3.svg|500px]]
+|style="padding:10px;"| [[File:Steiner_et_al_2022a_9166_Figure_3.png|500px]]
 |- style="text-align: center; font-size: 75%;"
 | colspan="1" style="padding:10px;"| '''Figure 3'''. Top 61% of publications with the most used methods
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-|style="padding:10px;"| [[File:Review_936792395077_2204_Figure 4.svg|500px]]
+|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).

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

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