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'''Machine Learning Techniques applied to the Coronavirus pandemic: a systematic and bibliometric analysis from January 2020 to June 2021'''</div>
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Maria Teresinha Arns Steiner<sup>a</sup>; David Gabriel de Barros Franco<sup>b*</sup>; Pedro José Steiner Neto<sup>c</sup></div>
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<sup>a</sup> Graduate Program in Industrial Engineering and Systems (PPGEPS)</div>
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Pontifical Catholic University of Paraná (PUCPR), Curitiba, PR, Brazil</div>
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[https://orcid.org/0000-0002-8712-7957 https://orcid.org/0000-0002-8712-7957]</div>
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[mailto:maria.steiner@pucpr.br maria.steiner@pucpr.br]</div>
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<sup>b </sup>Graduate Program in Digital Agroenergy (PPGADIGITAL)</div>
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Federal University of Tocantins (UFT), Palmas, TO, Brazil</div>
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[https://orcid.org/0000-0002-4313-6798 https://orcid.org/0000-0002-4313-6798]</div>
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[mailto:david.franco@uft.edu.br david.franco@uft.edu.br] (*''corresponding author'')</div>
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<sup>c </sup>Federal University of Paraná (UFPR), Curitiba, PR, Brazil</div>
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[https://orcid.org/0000-0001-6403-6860 https://orcid.org/0000-0001-6403-6860]</div>
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[mailto:pedrosteiner@ufpr.br pedrosteiner@ufpr.br]</div>
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'''Machine Learning Techniques applied to the Coronavirus pandemic: a systematic and bibliometric analysis from January 2020 to June 2021'''</div>
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==Abstract==
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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.
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'''Keywords''': Machine learning, coronavirus pandemic, systematic literature review, bibliometric analysis, genetic predisposition
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==1. Introduction==
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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.
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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].
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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.
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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.
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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.
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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].
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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.
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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.
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==2. Theoretical background: ML techniques for the Covid-19 disease==
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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.
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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].
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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.
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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.
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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.
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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.
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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.
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==3. Methodology==
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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”).
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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.
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<div id='img-1'></div>
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{| style="text-align: center; border: 1px solid #BBB; margin: 1em auto; width: auto;max-width: auto;"
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|style="padding:10px;"|  [[File:Review_936792395077_4578_Figure 1.svg|600px]]
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|- style="text-align: center; font-size: 75%;"
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| colspan="1" style="padding:10px;"| '''Figure 1'''. Systematic literature review flowchart 
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|}
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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.
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==4. Results and discussion==
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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.
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===4.1. Systematic literature review===
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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]].
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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.
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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.
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[[#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.
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<div class="center" style="font-size: 75%;">'''Table 1'''. Papers (22) that analyzed lung XR/CT of Covid-19 patients using ML techniques</div>
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<div id='tab-1'></div>
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{| class="wikitable" style="margin: 1em auto 0.1em auto;border-collapse: collapse;font-size:85%;width:auto;" 
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|-style="text-align:center"
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! style="background-color: blue;"| Authors (year) !!Focus of the paper !! ML Techniques !! Databases
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|  style="text-align: center;"|Anastasopoulos et al. (2020) [22]
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|  style="text-align: left;"|Implement an automated software to solve the substantial increase in chest CT admissions
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|  style="text-align: left;"|DL
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|  style="text-align: center;"|GitHub platform with Covid-19 chest CT dataset
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|  style="text-align: center;"|Ardakani et al. (2020) [19]
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|  style="text-align: left;"|Develop a rapid and valid method for Covid-19 diagnosis
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|  style="text-align: left;"|10 Convolutional NN
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|  style="text-align: center;"|108 patients
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|  style="text-align: center;"|Bharati et al. (2020) [23]
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|  style="text-align: left;"|Detect lung diseases from X-ray images through ''VGG Data STN ''with ''CNN ''(VDSNet)
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|  style="text-align: left;"|DL; VGG; STN; CNN
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|  style="text-align: center;"|Kaggle repository
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|  style="text-align: center;"|Brunese et al. (2020) [24]
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|  style="text-align: left;"|Detect Covid-19 from chest X-rays
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|  style="text-align: left;"|Supervised ML techniques
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|  style="text-align: center;"|85 chest X-rays
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|  style="text-align: center;"|Cai et al. (2020) [20]
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|  style="text-align: left;"|Analyze CT quantification
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|  style="text-align: left;"|RFR
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|  style="text-align: center;"|99 patients from Zhejiang
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|  style="text-align: center;"|Chakraborty & Mali (2021) [25]
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|  style="text-align: left;"|Efficiently Interpret and segment Covid-19 radiological images.
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|  style="text-align: left;"|SUFMACS
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|  style="text-align: center;"|250 CT images; 250 X-Ray images
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|  style="text-align: center;"|Chowdhury et al. (2020) [21]
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|  style="text-align: left;"|Detect Covid-19 pneumonia from digital X-ray images
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|  style="text-align: left;"|DL
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|  style="text-align: center;"|Kaggle databases
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|  style="text-align: center;"|Elaziz et al. (2020) [26]
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|  style="text-align: left;"|Classify chest x-ray images into 2 classes: Covid-19 patient or non-Covid-19 person
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|  style="text-align: left;"|New FrMEMs; modified Manta-Ray Foraging Optimization based on DE
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|  style="text-align: center;"|GitHub; Qatar University; University of Dhaka
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|  style="text-align: center;"|Vijay kumar et al. (2020) [27]
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|  style="text-align: left;"|Use the analytics of key points from images of Covid-19 for diagnosis and predictions
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|  style="text-align: left;"|GANs; DL
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|  style="text-align: center;"|16 benchmark datasets
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|  style="text-align: center;"|Loey et al. (2020) [28]
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|  style="text-align: left;"|Detect coronavirus in chest X-ray images
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|  style="text-align: left;"|GAN; DL
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|  style="text-align: center;"|307 images
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|  style="text-align: center;"|Saha et al. (2021) [29]
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|  style="text-align: left;"|Identify Covid-19 patients by evaluating chest X-ray images through an automated detection scheme (EMCNet)
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|  style="text-align: left;"|DL; RF; SVM; DT; AdaBoost
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|  style="text-align: center;"|Github repository (400 chest X-ray images)
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|  style="text-align: center;"|Saygılı (2021) [30]
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|  style="text-align: left;"|Achieve rapid and accurate detection of Covid-19 from CT and X-ray images
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|  style="text-align: left;"|k-NN; SVM; Bag of Tree; K-ELM
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|  style="text-align: center;"|3 public Covid-19 data sets
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|  style="text-align: center;"|Sedik et al. (2020) [31]
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|  style="text-align: left;"|Present two data-augmentation models to enhance learnability of Covid-19 detection
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|  style="text-align: left;"|CNN: ConvLSTM-based on DL
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|  style="text-align: center;"|2 datasets consisting of X-ray and CT images
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|  style="text-align: center;"|Sethy et al. (2020) [32]
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|  style="text-align: left;"|Detect coronavirus infected patients using X-ray images
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|  style="text-align: left;"|Deep feature; SVM
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|  style="text-align: center;"|GitHub repositor (University of Montreal; 381 images)
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|  style="text-align: center;"|Shiri et al. (2021) [33]
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|  style="text-align: left;"|Predict Covid-19 patients using clinical data and lung/lesion radiomic features extracted from chest CT images
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|  style="text-align: left;"|XGBoost
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|  style="text-align: center;"|152 patients
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|  style="text-align: center;"|D. Singh et al. (2020) [34]
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|  style="text-align: left;"|Classify Covid-19 patients from chest CT images
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|  style="text-align: left;"|MODE; ANN; ANFIS; CNN
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|  style="text-align: center;"|---
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|  style="text-align: center;"|Somasekar et al. (2020) [14]
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|  style="text-align: left;"|Open 3 research directions in the fight against the pandemic: CXR image classification; patient risk prediction; and forecasting of disease
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|  style="text-align: left;"|DCNN
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|  style="text-align: center;"|---
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|  style="text-align: center;"|Tamal et al. (2021) [35]
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|  style="text-align: left;"|Detect Covid-19 early and rapidly from CXR
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|  style="text-align: left;"|SVM; k-NN; EBM Trees
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|  style="text-align: center;"|378 images
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|  style="text-align: center;"|Tartaglione et al. (2020) [36]
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|  style="text-align: left;"|Provide which information to expect through CXR images
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|  style="text-align: left;"|DL to Covid classification of CXR images
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|  style="text-align: center;"|Hospitals in Northern Italy
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|  style="text-align: center;"|X. Wang et al. (2020) [37]
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|  style="text-align: left;"|Develop a DL using 3D CT for Covid-19 classification and lesion localization
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|  style="text-align: left;"|DL
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|  style="text-align: center;"|540 patients
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|  style="text-align: center;"|Waheed et al. (2020) [38]
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|  style="text-align: left;"|Generate synthetic chest X-ray images
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|  style="text-align: left;"|DL: CNNs
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|  style="text-align: center;"|3 publicly accessible datasets
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|  style="text-align: center;"|Wu et al. (2021) [39]
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|  style="text-align: left;"|Improve Covid-19 diagnosis using CT
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|  style="text-align: left;"|RF
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|  style="text-align: center;"|Youan Hospital, Beijing.
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|}
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<div class="justified" style="font-size: 85%;">'''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>
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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.
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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.
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<div class="center" style="width: auto; margin-left: auto; margin-right: auto;">
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'''Table 2.''' Works (13) related to Covid-19 that used ML techniques and social network tools.</div>
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{| style="width: 100%;border-collapse: collapse;" 
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|  style="border-top: 2pt solid black;border-bottom: 1pt solid black;text-align: center;"|'''Authors (year)'''
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|  style="border-top: 2pt solid black;border-bottom: 1pt solid black;text-align: center;"|'''Focus of the paper'''
252
|  style="border-top: 2pt solid black;border-bottom: 1pt solid black;text-align: center;"|'''ML Techniques'''
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|  style="border-top: 2pt solid black;border-bottom: 1pt solid black;text-align: center;"|'''Databases'''
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|-
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|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Abd-Alrazaq et al. (2020) [44]
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|  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
257
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|API; Tweepy Python library
258
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|February 2, 2020, to March 15, 2020 in public English
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260
language tweets
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|-
262
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Gulati et al. (2021) [41]
263
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Classify sentiment based on tweets related to Covid-19
264
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Linear SVC; Perceptron; PAC; LoR
265
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|72,000 tweets
266
|-
267
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Gupta et al. (2021) [45]
268
|  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.
269
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|API
270
271
272
|  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
273
|-
274
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Haupt et al. (2021) [42]
275
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Characterize communicative (tweets) behavior
276
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|ML techniques and SNA
277
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|API from Twitter
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|-
279
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Hou et al. (2021) [46]
280
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Explore public attention on social media
281
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Text analysis; LDA
282
|  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
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|-
284
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Kabir & Madria (2021) [47]
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|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Present tweets dataset on Covid-19 emotional responses (EMOCOV)
286
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|DL
287
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Data of 5,000 tweets
288
|-
289
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Kyriazos et al. (2021) [48]
290
|  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
291
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|CART; RF;
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293
CTREE
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|  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
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|-
296
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|S. Li et al. (2020) [49]
297
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Explore Covid-19’s impacts on mental health
298
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|DL
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|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|17,865 active Weibo users
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|-
301
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Mackey et al. (2020) [50]
302
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Characterize users’ conversations (tweets) associated with
303
304
Covid-19 symptoms and experiences
305
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|BTM
306
307
308
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|4,492,954 tweets
309
|-
310
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Praveen et al. (2021) [43]
311
|  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
312
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Natural language
313
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|840,000 tweets
314
|-
315
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Samuel et al. (2020) [51]
316
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Identify public sentiment (tweets) associated with the pandemic
317
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Naïve Bayes; LR; LoR; k-NN
318
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|900,000 tweets
319
|-
320
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Satu et al. (2021) [52]
321
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Analyze Covid-19 public tweets to extract significant sentiments
322
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|TClustVID
323
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|IEEE data portal developed by Rabindra Lamsal
324
|-
325
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Shah et al. (2021) [53]
326
|  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
327
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|NLP
328
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|55,612 OPRs of 3,430 doctors
329
|}
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331
Acronyms (alphabetical): See Table 1.
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333
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.
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335
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'''.
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337
<div class="center" style="width: auto; margin-left: auto; margin-right: auto;">
338
'''Table 3.''' Works (44) related to Covid-19 that directly used ML techniques for prediction.</div>
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340
{| style="width: 100%;border-collapse: collapse;" 
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|-
342
|  style="border-top: 2pt solid black;border-bottom: 1pt solid black;text-align: center;"|'''Authors (year)'''
343
|  style="border-top: 2pt solid black;border-bottom: 1pt solid black;text-align: center;"|'''Focus of the paper'''
344
|  style="border-top: 2pt solid black;border-bottom: 1pt solid black;text-align: center;"|'''ML Techniques'''
345
|  style="border-top: 2pt solid black;border-bottom: 1pt solid black;text-align: center;"|'''Databases'''
346
|-
347
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Amar et al. (2020) [54]
348
|  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
349
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|LoR; Regression models
350
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Egyptian Ministry of Health; February
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352
15, 2020, to June 15, 2020
353
|-
354
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Ardabili et al. (2020) [56]
355
|  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
356
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|MLP; ANFIS
357
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Worldometers website for five countries
358
|-
359
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Arvind et al. (2021) [57]
360
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Predict future intubation among patients diagnosed with
361
362
Covid-19
363
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|RF
364
|  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)
365
|-
366
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|ArunKumar et al. (2021) [58]
367
|  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
368
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Time series models;
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370
ARIMA; SARIMA
371
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|John Hopkins University’s Covid-19 database
372
|-
373
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Aydin & Yurdakul (2020) [59]
374
|  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
375
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|WSIDEA; k-means; HCA; RF; DT
376
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Data from 142 countries
377
|-
378
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Ayyoubzadeh et al. (2020) [60]
379
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Predict the incidence of Covid-19 in Iran
380
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|LR; LSTM models
381
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Google Trends website
382
|-
383
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Ballı (2021) [61]
384
|  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
385
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|LR, MLP, RF and SVM
386
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Data from WHO (35 weeks)
387
|-
388
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Bloise & Tancioni (2021) [62]
389
|  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
390
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|LASSO; Elastic net model
391
|  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
392
|-
393
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Burdick et al. (2020) [55]
394
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Predict the need for ventilation for Covid-19 patients
395
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|XGBoost; DT
396
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|197 patients were enrolled in the READY (REspirAtory Decompensation study)
397
|-
398
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Buscema et al. (2020) [63]
399
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Analyze the evolution of the Covid-19 phenomenon
400
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|TWC algorithm
401
|  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.
402
|-
403
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Chakraborti et al. (2021) [64]
404
|  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
405
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|RF; GBM
406
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|European Centre for Disease Prevention and Control
407
408
(ECDC)
409
|-
410
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Chatterjee et al. (2020) [65]
411
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Analyze datasets to
412
413
understand the trend of
414
415
Covid-19
416
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Statistical and univariate time series
417
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Oxford University
418
419
Database
420
|-
421
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Chimmula & Zhang (2020) [66]
422
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Forecast Covid-19 transmission
423
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Time series; DL; LSTM networks
424
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Johns Hopkins university; Canadian health authority
425
|-
426
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Cobre et al. (2021) [67]
427
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Predict Covid-19 diagnosis and disease severity
428
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|ANN; DT;
429
430
PLS-DA; KNN
431
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Kaggle platform
432
433
5,643 patient samples
434
|-
435
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Ebinger et al. (2021) [68]
436
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Predict the likelihood of prolonged LOS
437
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|3 ML models developed using DataRobot
438
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|966 patients
439
|-
440
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Fong et al. (2020) [69]
441
|  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
442
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|PNN+cf
443
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Archive
444
445
of Chinese health authorities
446
|-
447
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Gothai et al. (2021) [70]
448
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Predict the growth and trend of Covid-19
449
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|LR; SVM; time series
450
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|172,479 documents from Johns Hopkins University
451
452
Repository
453
|-
454
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Jain et al. (2021) [71]
455
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Predict Covid-19
456
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|SVM; Naïve Bayes; KNN; AdaBoost; GBoost; RF; ANN
457
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|B-cell dataset
458
|-
459
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Kang et al. (2021) [72]
460
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Predict severe Covid-19 cases
461
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|ANN
462
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|151 cases of a China center
463
|-
464
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Kavadi et al. (2020) [73]
465
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Global pandemic prediction of Covid-19
466
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|PDR-NML
467
468
method
469
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Kaggle
470
|-
471
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Khan et al. (2021) [74]
472
|  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
473
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|DT; SVM;
474
475
GPR
476
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Ministry of Health and Family Welfare (MoHFW) on
477
478
10th June 2020
479
|-
480
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Lmater et al. (2021) [75]
481
|  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.
482
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|SIDR model (susceptible, infected, diagnosed and recovered stages)
483
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Epidemiological data from 4 countries: Belgium; Morocco; Netherlands; Russia
484
|-
485
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Malefors et al. (2021) [76]
486
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Predict guest attendance during the pandemic (meal planning in Sweden)
487
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|RF; ANN
488
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Data from 18 primary school kitchens and 16 preschool kitchens
489
|-
490
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Mojjada et al. (2020) [77]
491
|  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.
492
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|LASSO; SVM;
493
494
LR
495
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Git Hub, supplied
496
497
by Johns Hopkins University
498
|-
499
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Nemati et al. (2020) [78]
500
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Predict patients’
501
502
period of stay in hospital
503
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|7 ML and statistical analysis
504
505
techniques
506
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|1,182 hospitalized patients
507
|-
508
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Ong et al. (2020) [79]
509
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Predict Covid-19 vaccine candidates
510
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Vaxign reverse vaccinology tools
511
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|ClinicalTrials.gov database
512
513
and PubMed literature.
514
|-
515
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Papastefanopoulos et al. (2020) [80]
516
|  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
517
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Six time series
518
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Kaggle; population-by-country dataset
519
|-
520
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Peng & Nagata (2020) [81]
521
|  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
522
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|SVR
523
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|12 most affected countries
524
|-
525
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Pinter et al. (2020) [82]
526
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Predict the Covid-19 pandemic for Hungary
527
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Hybrid ML: ANFIS and MLP-ICA
528
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Worldometer for Hungary
529
|-
530
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Pourhomayoun & Shakibi (2021) [83]
531
|  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
532
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|SVM; ANN; RF; DT; LoR; KNN
533
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|2,670,000 Covid-19 patients from 146 countries
534
|-
535
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Quintero et al. (2021) [84]
536
|  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
537
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|GA; AR;
538
539
ARIMA
540
|  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
541
|-
542
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Ribeiro et al. (2020) [85]
543
|  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
544
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|ARIMA;
545
546
CUBIST; RF; RIDGE; SVR; SVR
547
|  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
548
|-
549
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Santosh (2020) [86]
550
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Develop AI-driven tools to identify Covid-19 outbreaks
551
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|AL
552
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Multitudinal and Multimodal data
553
|-
554
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Shahid et al. (2021) [6]
555
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Predict virus detection, spread prevention and medical assistance
556
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|survey of ML algorithms and models
557
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|---
558
|-
559
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|V. Singh et al. (2020) [87]
560
|  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.
561
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|SVM; time series
562
|  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
563
|-
564
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Sujath et al. (2020) [88]
565
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Predict the spread of Covid-2019
566
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|LR; MLP; VAR
567
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Kaggle; Indian database
568
|-
569
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Tarik et al. (2021) [89]
570
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Predict Moroccan student performance
571
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|RF; DT; LR
572
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Referral system
573
|-
574
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Tuli et al. (2020) [90]
575
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Analyze and predict the growth of the epidemic
576
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|GIWD in a
577
578
cloud computing platform
579
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Our World in Data by Hannah Ritchie
580
|-
581
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Wadhwa et al. (2021) [91]
582
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Predict the extension of lockdown in order to eradicate
583
584
Covid-19 from India.
585
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|LR
586
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Total number of
587
588
cases, deaths, and recoveries all over India.
589
|-
590
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|P. Wang et al. (2020) [92]
591
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Predict epidemic trends
592
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|LoR
593
|  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.
594
|-
595
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Yan et al. (2020) [93]
596
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Identify crucial predictive biomarkers of Covid-19 mortality
597
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|XGBoost
598
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|485 patients
599
|-
600
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Yadav et al. (2020) [94]
601
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Solve 5 different tasks:
602
603
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.
604
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|SVR; SLR; PR
605
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Data from different countries
606
|-
607
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Yeşilkanat (2020) [95]
608
|  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
609
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|RF
610
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Johns Hopkins University Center for Systems Science; Engineering
611
|-
612
|  style="border-top: 1pt solid black;border-bottom: 2pt solid black;text-align: center;"|Zivkovic et al. (2021) [96]
613
|  style="border-top: 1pt solid black;border-bottom: 2pt solid black;text-align: center;"|Predict the number of new coronavirus cases
614
|  style="border-top: 1pt solid black;border-bottom: 2pt solid black;text-align: center;"|ANFIS; BASSI
615
|  style="border-top: 1pt solid black;border-bottom: 2pt solid black;text-align: center;"|6 benchmark
616
617
Functions
618
|}
619
620
Acronyms (alphabetical): See Table 1.
621
622
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.
623
624
<div class="center" style="width: auto; margin-left: auto; margin-right: auto;">
625
'''Table 4.''' Works (38) related to Covid-19 that used ML techniques involving general subjects.</div>
626
627
{| style="width: 100%;border-collapse: collapse;" 
628
|-
629
|  style="border-top: 2pt solid black;border-bottom: 1pt solid black;text-align: center;"|'''Authors (year)'''
630
|  style="border-top: 2pt solid black;border-bottom: 1pt solid black;text-align: center;"|'''Focus of the paper'''
631
|  style="border-top: 2pt solid black;border-bottom: 1pt solid black;text-align: center;"|'''ML Techniques'''
632
|  style="border-top: 2pt solid black;border-bottom: 1pt solid black;text-align: center;"|'''Databases'''
633
|-
634
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Alves et al. (2021) [98]
635
|  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
636
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|DT
637
638
Explainer and criteria graphs
639
|  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
640
|-
641
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Baralić et al. (2020) [99]
642
|  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.
643
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|''in silico ''toxicogenomic data-mining approach
644
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Comparative Toxicogenomics Database
645
|-
646
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Carrillo-Larco & Castillo-Cara (2020) [100]
647
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Clustering countries which shared profiles of the pandemic
648
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|k-means; statistical techniques
649
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|155 countries; Johns Hopkins University and others
650
|-
651
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Di Castelnuovo et al. (2020) [97]
652
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Identify the characteristics predisposing Covid-19 patients
653
654
to in-hospital death.
655
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|RF
656
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|3,894 patients
657
658
hospitalized from a defined period (Italy)
659
|-
660
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Choudrie et al. (2021) [101]
661
|  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
662
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|DT; CNN
663
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|143 patients
664
|-
665
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Dandekar et al. (2020) [102]
666
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Develop a globally applicable diagnostic Covid-19 model
667
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|SIR; NN
668
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|70 countries
669
|-
670
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Doanvo et al. (2020) [15]
671
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Identify knowledge research Covid-19 gaps in the literature
672
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|PCA
673
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|35,281 abstracts from CORD-19
674
|-
675
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Fong et al. (2020) [103]
676
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Gain stochastic insights into the pandemic development
677
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|CMC: DL; FRI
678
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Empirical data from the Chinese CDCP
679
|-
680
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Godavarthi & Sowjanya (2021) [104]
681
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Extract information from the scientific literature: text classification
682
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|KNN; MLP; XGBoost
683
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|CORD-19 dataset
684
|-
685
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Hu et al. (2021) [105]
686
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Detect the changes in air pollutants during Covid-19 lockdown
687
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|RF models
688
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Data from 35 sites in Beijing, from 2015 to 2020
689
|-
690
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Jamshidi et al. (2020) [106]
691
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Present a response to combat the virus through AI
692
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|GANs; LSTM; ELM
693
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|---
694
|-
695
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Kadioglu et al. (2021) [107]
696
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Identify compounds against three targets of Covid-19
697
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Pharmaco strategy'' in silico''
698
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Chemical libraries (FDA-approved drugs; natural compound datasets; ZINC database)
699
|-
700
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Khanday et al. (2020) [108]
701
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Detect Covid-19 through clinical text data
702
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|LoR; MNB
703
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Data repository GitHub
704
|-
705
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Kuo & Fu (2021) [109]
706
|  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
707
|  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
708
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|New York Times; USDA ERA; gridMed; Google
709
|-
710
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Lam et al. (2021) [110]
711
|  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
712
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|GBM
713
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|893 patients
714
|-
715
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|M. Li et al. (2021) [16]
716
|  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
717
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|LoR; LASSO
718
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Johns Hopkins University
719
|-
720
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Lip et al. (2021) [111]
721
|  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
722
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Inferential statistics and ML computations (LoR)
723
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Data from April 1, 2018 to Nov 30, 2020
724
|-
725
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Loey et al. (2021) [112]
726
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Develop a DL and classical ML for face detection
727
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|DL; DT; SVM; EA
728
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|3 datasets
729
|-
730
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Lovrić et al. (2021) [113]
731
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Analyze improvements in air quality during the Covid-19 lockdown
732
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|RFR
733
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Graz, Styria, Austria
734
|-
735
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Magazzino et al. (2021) [114]
736
|  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
737
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|DL
738
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|--
739
|-
740
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|McRae et al. (2020) [115]
741
|  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
742
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Statistical learning algorithm
743
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|160 patients from Wuhan, China
744
|-
745
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Malki et al. (2020) [116]
746
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Verify the relationship between weather and Covid-19
747
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Regressor ML models
748
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Meteoblue website
749
|-
750
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Mele & Magazzino (2021) [117]
751
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Explore the relationship between pollution, economic growth and
752
753
Covid-19 deaths in India
754
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Time Series approach; Stationarity
755
756
and Toda-Yamamoto causality tests
757
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Indian data from January 29 to May 18, 2020
758
|-
759
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Petetin et al. (2020) [118]
760
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Use meteorological
761
762
data to estimate the
763
764
“business-as-usual” NO2 mixing ratios
765
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|GBM
766
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|GHOST
767
|-
768
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Qiang et al. (2020) [119]
769
|  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
770
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|RF models
771
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|507 human origin
772
773
viruses and 2,159 non-human-origin viruses
774
|-
775
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Radanliev et al. (2020) [120]
776
|  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
777
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Statistical methods
778
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|WSCC
779
|-
780
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Randhawa et al. (2020) [121]
781
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Use intrinsic genomic signatures to classify Covid-19 rapidly
782
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|MLDSP for genome analyses; DT
783
|  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
784
|-
785
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Shrock et al. (2020) [122]
786
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Explore antiviral antibody
787
788
responses across the human virome
789
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|XGBoost
790
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|232 coronavirus disease patients and 190 pre-Covid-19
791
|-
792
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|X. Sun et al. (2020) [123]
793
|  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
794
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|TCM; CMM
795
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Encyclopedia of Traditional Chinese Medicine database; BATMAN-TCM database
796
|-
797
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|C. L. F. Sun et al. (2020) [124]
798
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Identify risks and vectors of infection in nursing homes
799
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|GBA
800
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|1146 NHs in Massachusetts
801
|-
802
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Swapnarekha et al. (2020) [125]
803
|  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
804
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|ML; DL
805
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|January 23, 2020 to April, 21, 2020
806
|-
807
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|S. Tiwari et al. (2020) [126]
808
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Prepare Indian government and citizens to take control measures (SEIR)
809
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Time Series
810
|  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)
811
|-
812
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|A. Tiwari et al. (2021) [127]
813
|  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
814
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|RF
815
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Johns Hopkins University; Centers for Disease Control and Prevention
816
|-
817
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Toğaçar et al. (2020) [128]
818
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Detect Coronavirus
819
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|DL; SVM; SMOM
820
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|GitHub; Kaggle
821
|-
822
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Vaishya et al. (2020) [129]
823
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Revise the effectiveness of AI techniques for Covid-19
824
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|AI techniques
825
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|PubMed, Scopus and Google Scholar datasets
826
|-
827
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|W.-C. Wang et al. (2021) [130]
828
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Develop a system for monitoring global and local community outbreaks
829
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|k-means
830
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Johns Hopkins; data with daily infected, recovered and death cases
831
|-
832
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Yacchirema & Chura (2021) [131]
833
|  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
834
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|SVM; DT; LoR; RF; KNN
835
836
(to detect the location of people)
837
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|From portable IoT devices
838
|-
839
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Yang et al. (2020) [132]
840
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Demonstrate control measures impact the containment of the epidemic
841
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|SEIR model
842
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|2003 SARS data
843
|}
844
845
Acronyms (alphabetical): See Table 1.
846
847
==4.2 Bibliometric Literature Review==
848
849
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.
850
851
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'''.
852
853
<div class="center" style="width: auto; margin-left: auto; margin-right: auto;">
854
'''Figure 2.''' Top 54% of journals in the selected papers.</div>
855
856
<div class="center" style="width: auto; margin-left: auto; margin-right: auto;">
857
[[File:Review_936792395077_5667_Figure 2.svg|500px]] </div>
858
859
860
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'''.
861
862
<div class="center" style="width: auto; margin-left: auto; margin-right: auto;">
863
'''Figure 3.''' Top 61% of publications with the most used methods.</div>
864
865
<div class="center" style="width: auto; margin-left: auto; margin-right: auto;">
866
 [[File:Review_936792395077_1718_Figure 3.svg|500px]] </div>
867
868
869
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.
870
871
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.
872
873
<div class="center" style="width: auto; margin-left: auto; margin-right: auto;">
874
'''Figure 4.''' Top 70% of authors' nationality.</div>
875
876
<div class="center" style="width: auto; margin-left: auto; margin-right: auto;">
877
[[File:Review_936792395077_2204_Figure 4.svg|500px]] </div>
878
879
880
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).
881
882
=5. Concluding Remarks=
883
884
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.
885
886
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.
887
888
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.
889
890
==Declaration of competing interest==
891
892
None.
893
894
==Funding sources==
895
896
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.
897
898
==References==
899
900
[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.
901
902
[2] Piret J., Boivin G. Pandemics Throughout History. Front. Microbiol., 11(January), 2021.
903
904
[3] Huremović D. Brief History of Pandemics (Pandemics Throughout History). In Psychiatry of Pandemics, Springer International Publishing, Cham, pp. 7–35, 2019.
905
906
[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.
907
908
[5] WHO. WHO Coronavirus (COVID-19) Dashboard. Retrieved from [https://covid19.who.int/ https://covid19.who.int/] 2021.
909
910
[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.
911
912
[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.
913
914
[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.
915
916
[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.
917
918
[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.
919
920
[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.
921
922
[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.
923
924
[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.
925
926
[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.
927
928
[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.
929
930
[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.
931
932
[17] Snyder H. Literature review as a research methodology: An overview and guidelines. J. Bus. Res., 104(July):333–339, 2019.
933
934
[18] Xiao Y., Watson M. Guidance on Conducting a Systematic Literature Review. J. Plan. Educ. Res., 39(1):93–112, 2019.
935
936
[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.
937
938
[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.
939
940
[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.
941
942
[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.
943
944
[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.
945
946
[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.
947
948
[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.
949
950
[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.
951
952
[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.
953
954
[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.
955
956
[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.
957
958
[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.
959
960
[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.
961
962
[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.
963
964
[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.
965
966
[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.
967
968
[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.
969
970
[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.
971
972
[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.
973
974
[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.
975
976
[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.
977
978
[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.
979
980
[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.
981
982
[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.
983
984
[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.
985
986
[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.
987
988
[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.
989
990
[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.
991
992
[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.
993
994
[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.
995
996
[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.
997
998
[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.
999
1000
[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.
1001
1002
[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.
1003
1004
[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.
1005
1006
[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.
1007
1008
[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.
1009
1010
[56] Ardabili S.F., Mosavi A., Ghamisi P., et al. COVID-19 Outbreak Prediction with Machine Learning. Algorithms, 13(10):249, 2020.
1011
1012
[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.
1013
1014
[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.
1015
1016
[59] Aydin N., Yurdakul G. Assessing countries’ performances against COVID-19 via WSIDEA and machine learning algorithms. Appl. Soft Comput., 97:106792, 2020.
1017
1018
[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.
1019
1020
[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.
1021
1022
[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.
1023
1024
[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.
1025
1026
[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.
1027
1028
[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.
1029
1030
[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.
1031
1032
[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.
1033
1034
[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.
1035
1036
[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.
1037
1038
[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.
1039
1040
[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.
1041
1042
[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.
1043
1044
[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.
1045
1046
[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.
1047
1048
[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.
1049
1050
[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.
1051
1052
[77] Mojjada R.K., Yadav A., Prabhu A.V., Natarajan Y. Machine learning models for covid-19 future forecasting. Mater. Today Proc., xxxx), 2020.
1053
1054
[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.
1055
1056
[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.
1057
1058
[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.
1059
1060
[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.
1061
1062
[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.
1063
1064
[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.
1065
1066
[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.
1067
1068
[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.
1069
1070
[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.
1071
1072
[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.
1073
1074
[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.
1075
1076
[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.
1077
1078
[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.
1079
1080
[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.
1081
1082
[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.
1083
1084
[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.
1085
1086
[94] Yadav M., Perumal M., Srinivas M. Analysis on novel coronavirus (COVID-19) using machine learning methods. Chaos, Solitons & Fractals, 139:110050, 2020.
1087
1088
[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.
1089
1090
[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.
1091
1092
[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.
1093
1094
[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.
1095
1096
[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.
1097
1098
[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.
1099
1100
[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.
1101
1102
[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.
1103
1104
[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.
1105
1106
[104] Godavarthi D., Sowjanya M. Classification of covid related articles using machine learning. Mater. Today Proc., xxxx), 2021.
1107
1108
[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.
1109
1110
[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.
1111
1112
[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.
1113
1114
[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.
1115
1116
[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.
1117
1118
[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.
1119
1120
[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.
1121
1122
[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.
1123
1124
[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.
1125
1126
[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.
1127
1128
[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.
1129
1130
[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.
1131
1132
[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.
1133
1134
[118] Petetin H., Bowdalo D., Soret A., et al. Meteorology-normalized impact of the COVID-19 lockdown upon NO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; pollution in Spain. Atmos. Chem. Phys., 20(18):11119–11141, 2020.
1135
1136
[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.
1137
1138
[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.
1139
1140
[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.
1141
1142
[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.
1143
1144
[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.
1145
1146
[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.
1147
1148
[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.
1149
1150
[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.
1151
1152
[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.
1153
1154
[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.
1155
1156
[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.
1157
1158
[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.
1159
1160
[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.
1161
1162
[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.
1163
1164
[133] The COVID-19 Host Genetics Initiative . Mapping the human genetic architecture of COVID-19. Nature,:2021.03.10.21252820, 2021.
1165
1166
[134] Pairo-Castineira E., Clohisey S., Klaric L., et al. Genetic mechanisms of critical illness in COVID-19. Nature, 591(7848):92–98, 2021.
1167
1168
[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.
1169

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Published on 09/09/22
Accepted on 26/08/22
Submitted on 20/03/22

Volume 38, Issue 3, 2022
DOI: 10.23967/j.rimni.2022.09.001
Licence: CC BY-NC-SA license

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