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

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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==

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

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

==1. Introduction==

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

==3. Methodology==

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

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

<div id='img-1'></div>
{| style="text-align: center; border: 1px solid #BBB; margin: 1em auto; width: auto;max-width: auto;"
|-
|style="padding:10px;"|  [[File:Review_936792395077_4578_Figure 1.svg|600px]]
|- style="text-align: center; font-size: 75%;"
| colspan="1" style="padding:10px;"| '''Figure 1'''. Systematic literature review flowchart 
|}


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

==4. Results and discussion==

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

===4.1. Systematic literature review===

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

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

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.

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

<div class="center" style="font-size: 75%;">'''Table 1'''. Papers (22) that analyzed lung XR/CT of Covid-19 patients using ML techniques</div>

<div id='tab-1'></div>
{| class="wikitable" style="margin: 1em auto 0.1em auto;border-collapse: collapse;font-size:85%;width:auto;" 
|-style="text-align:center"
! style="background-color: #d1e0df;"| Authors (year) !!  style="background-color:#d1e0df;text-align:left;"| Focus of the paper !!  style="background-color: #d1e0df;text-align:left;"| ML Techniques !! style="background-color: #d1e0df;text-align:left;"| Databases
|-
|  style="text-align: center;"|Anastasopoulos et al. (2020) [22]
|  style="text-align: left;"|Implement an automated software to solve the substantial increase in chest CT admissions
|  style="text-align: left;"|DL
|  style="text-align: center;"|GitHub platform with Covid-19 chest CT dataset
|-
|  style="text-align: center;"|Ardakani et al. (2020) [19]
|  style="text-align: left;"|Develop a rapid and valid method for Covid-19 diagnosis
|  style="text-align: left;"|10 Convolutional NN
|  style="text-align: center;"|108 patients
|-
|  style="text-align: center;"|Bharati et al. (2020) [23]
|  style="text-align: left;"|Detect lung diseases from X-ray images through ''VGG Data STN ''with ''CNN ''(VDSNet)
|  style="text-align: left;"|DL; VGG; STN; CNN
|  style="text-align: center;"|Kaggle repository
|-
|  style="text-align: center;"|Brunese et al. (2020) [24]
|  style="text-align: left;"|Detect Covid-19 from chest X-rays
|  style="text-align: left;"|Supervised ML techniques
|  style="text-align: center;"|85 chest X-rays
|-
|  style="text-align: center;"|Cai et al. (2020) [20]
|  style="text-align: left;"|Analyze CT quantification
|  style="text-align: left;"|RFR
|  style="text-align: center;"|99 patients from Zhejiang
|-
|  style="text-align: center;"|Chakraborty & Mali (2021) [25]
|  style="text-align: left;"|Efficiently Interpret and segment Covid-19 radiological images.
|  style="text-align: left;"|SUFMACS
|  style="text-align: center;"|250 CT images; 250 X-Ray images
|-
|  style="text-align: center;"|Chowdhury et al. (2020) [21]
|  style="text-align: left;"|Detect Covid-19 pneumonia from digital X-ray images
|  style="text-align: left;"|DL
|  style="text-align: center;"|Kaggle databases
|-
|  style="text-align: center;"|Elaziz et al. (2020) [26]
|  style="text-align: left;"|Classify chest x-ray images into 2 classes: Covid-19 patient or non-Covid-19 person
|  style="text-align: left;"|New FrMEMs; modified Manta-Ray Foraging Optimization based on DE
|  style="text-align: center;"|GitHub; Qatar University; University of Dhaka
|-
|  style="text-align: center;"|Vijay kumar et al. (2020) [27]
|  style="text-align: left;"|Use the analytics of key points from images of Covid-19 for diagnosis and predictions
|  style="text-align: left;"|GANs; DL
|  style="text-align: center;"|16 benchmark datasets
|-
|  style="text-align: center;"|Loey et al. (2020) [28]
|  style="text-align: left;"|Detect coronavirus in chest X-ray images
|  style="text-align: left;"|GAN; DL
|  style="text-align: center;"|307 images
|-
|  style="text-align: center;"|Saha et al. (2021) [29]
|  style="text-align: left;"|Identify Covid-19 patients by evaluating chest X-ray images through an automated detection scheme (EMCNet)
|  style="text-align: left;"|DL; RF; SVM; DT; AdaBoost
|  style="text-align: center;"|Github repository (400 chest X-ray images)
|-
|  style="text-align: center;"|Saygılı (2021) [30]
|  style="text-align: left;"|Achieve rapid and accurate detection of Covid-19 from CT and X-ray images
|  style="text-align: left;"|k-NN; SVM; Bag of Tree; K-ELM
|  style="text-align: center;"|3 public Covid-19 data sets
|-
|  style="text-align: center;"|Sedik et al. (2020) [31]
|  style="text-align: left;"|Present two data-augmentation models to enhance learnability of Covid-19 detection
|  style="text-align: left;"|CNN: ConvLSTM-based on DL
|  style="text-align: center;"|2 datasets consisting of X-ray and CT images
|-
|  style="text-align: center;"|Sethy et al. (2020) [32]
|  style="text-align: left;"|Detect coronavirus infected patients using X-ray images
|  style="text-align: left;"|Deep feature; SVM
|  style="text-align: center;"|GitHub repositor (University of Montreal; 381 images)
|-
|  style="text-align: center;"|Shiri et al. (2021) [33]
|  style="text-align: left;"|Predict Covid-19 patients using clinical data and lung/lesion radiomic features extracted from chest CT images
|  style="text-align: left;"|XGBoost
|  style="text-align: center;"|152 patients
|-
|  style="text-align: center;"|D. Singh et al. (2020) [34]
|  style="text-align: left;"|Classify Covid-19 patients from chest CT images
|  style="text-align: left;"|MODE; ANN; ANFIS; CNN
|  style="text-align: center;"|---
|-
|  style="text-align: center;"|Somasekar et al. (2020) [14]
|  style="text-align: left;"|Open 3 research directions in the fight against the pandemic: CXR image classification; patient risk prediction; and forecasting of disease
|  style="text-align: left;"|DCNN
|  style="text-align: center;"|---
|-
|  style="text-align: center;"|Tamal et al. (2021) [35]
|  style="text-align: left;"|Detect Covid-19 early and rapidly from CXR
|  style="text-align: left;"|SVM; k-NN; EBM Trees
|  style="text-align: center;"|378 images
|-
|  style="text-align: center;"|Tartaglione et al. (2020) [36]
|  style="text-align: left;"|Provide which information to expect through CXR images
|  style="text-align: left;"|DL to Covid classification of CXR images
|  style="text-align: center;"|Hospitals in Northern Italy
|-
|  style="text-align: center;"|X. Wang et al. (2020) [37]
|  style="text-align: left;"|Develop a DL using 3D CT for Covid-19 classification and lesion localization
|  style="text-align: left;"|DL
|  style="text-align: center;"|540 patients
|-
|  style="text-align: center;"|Waheed et al. (2020) [38]
|  style="text-align: left;"|Generate synthetic chest X-ray images
|  style="text-align: left;"|DL: CNNs
|  style="text-align: center;"|3 publicly accessible datasets
|-
|  style="text-align: center;"|Wu et al. (2021) [39]
|  style="text-align: left;"|Improve Covid-19 diagnosis using CT
|  style="text-align: left;"|RF
|  style="text-align: center;"|Youan Hospital, Beijing.
|}


<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>


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

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

<div class="center" style="width: auto; margin-left: auto; margin-right: auto;">
'''Table 2.''' Works (13) related to Covid-19 that used ML techniques and social network tools.</div>

{| style="width: 100%;border-collapse: collapse;" 
|-
|  style="border-top: 2pt solid black;border-bottom: 1pt solid black;text-align: center;"|'''Authors (year)'''
|  style="border-top: 2pt solid black;border-bottom: 1pt solid black;text-align: center;"|'''Focus of the paper'''
|  style="border-top: 2pt solid black;border-bottom: 1pt solid black;text-align: center;"|'''ML Techniques'''
|  style="border-top: 2pt solid black;border-bottom: 1pt solid black;text-align: center;"|'''Databases'''
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Abd-Alrazaq et al. (2020) [44]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Identify the topics related to Covid-19 posted by Twitter users
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|API; Tweepy Python library
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|February 2, 2020, to March 15, 2020 in public English

language tweets
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Gulati et al. (2021) [41]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Classify sentiment based on tweets related to Covid-19
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Linear SVC; Perceptron; PAC; LoR
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|72,000 tweets
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Gupta et al. (2021) [45]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Quantify twitter users’ perceptions regarding the effect of weather and analyze how they evolved with respect to real-world events and time.
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|API


|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|166,005 English tweets; from January 23 to June 22, 2020
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Haupt et al. (2021) [42]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Characterize communicative (tweets) behavior
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|ML techniques and SNA
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|API from Twitter
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Hou et al. (2021) [46]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Explore public attention on social media
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Text analysis; LDA
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Weibo (popular microblogging site in China) from December 27, 2019 to May 31, 2020
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Kabir & Madria (2021) [47]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Present tweets dataset on Covid-19 emotional responses (EMOCOV)
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|DL
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Data of 5,000 tweets
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Kyriazos et al. (2021) [48]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Model that differentiated the top 25% well-being scorers in early Covid-19 quarantine
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|CART; RF;

CTREE
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Data (1,518) were collected in a web-link posted on webpages and Facebook accounts
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|S. Li et al. (2020) [49]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Explore Covid-19’s impacts on mental health
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|DL
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|17,865 active Weibo users
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Mackey et al. (2020) [50]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Characterize users’ conversations (tweets) associated with

Covid-19 symptoms and experiences
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|BTM


|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|4,492,954 tweets
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Praveen et al. (2021) [43]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Analyze Indian citizens’ perception of anxiety, stress and trauma during Covid-19
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Natural language
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|840,000 tweets
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Samuel et al. (2020) [51]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Identify public sentiment (tweets) associated with the pandemic
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Naïve Bayes; LR; LoR; k-NN
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|900,000 tweets
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Satu et al. (2021) [52]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Analyze Covid-19 public tweets to extract significant sentiments
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|TClustVID
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|IEEE data portal developed by Rabindra Lamsal
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Shah et al. (2021) [53]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Analyze online physician rating (OPR) to identify emerging and fading topics and sentiment trends on physician websites
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|NLP
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|55,612 OPRs of 3,430 doctors
|}

Acronyms (alphabetical): See Table 1.

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

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

<div class="center" style="width: auto; margin-left: auto; margin-right: auto;">
'''Table 3.''' Works (44) related to Covid-19 that directly used ML techniques for prediction.</div>

{| style="width: 100%;border-collapse: collapse;" 
|-
|  style="border-top: 2pt solid black;border-bottom: 1pt solid black;text-align: center;"|'''Authors (year)'''
|  style="border-top: 2pt solid black;border-bottom: 1pt solid black;text-align: center;"|'''Focus of the paper'''
|  style="border-top: 2pt solid black;border-bottom: 1pt solid black;text-align: center;"|'''ML Techniques'''
|  style="border-top: 2pt solid black;border-bottom: 1pt solid black;text-align: center;"|'''Databases'''
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Amar et al. (2020) [54]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Predict the number of patients that will be infected with Covid-19 in Egypt
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|LoR; Regression models
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Egyptian Ministry of Health; February

15, 2020, to June 15, 2020
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Ardabili et al. (2020) [56]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Predict the Covid-19 outbreak and the enforcement of relevant control measures
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|MLP; ANFIS
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Worldometers website for five countries
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Arvind et al. (2021) [57]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Predict future intubation among patients diagnosed with

Covid-19
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|RF
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Data from 5 hospitals within an academic healthcare system (4,087 patients)
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|ArunKumar et al. (2021) [58]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Forecast the epidemiological trends of the Covid-19 pandemic for top-16 countries
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Time series models;

ARIMA; SARIMA
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|John Hopkins University’s Covid-19 database
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Aydin & Yurdakul (2020) [59]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Analyze the performance of countries to counter the Covid-19 outbreak
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|WSIDEA; k-means; HCA; RF; DT
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Data from 142 countries
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Ayyoubzadeh et al. (2020) [60]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Predict the incidence of Covid-19 in Iran
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|LR; LSTM models
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Google Trends website
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Ballı (2021) [61]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Identify the curve of the disease and forecast the epidemic trend
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|LR, MLP, RF and SVM
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Data from WHO (35 weeks)
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Bloise & Tancioni (2021) [62]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Exploit the provincial variability of Covid-19 cases in Italy to select the territorial predictors for the pandemic
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|LASSO; Elastic net model
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Data from March 21, 2020 to June 3, 2020, in Italy
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Burdick et al. (2020) [55]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Predict the need for ventilation for Covid-19 patients
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|XGBoost; DT
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|197 patients were enrolled in the READY (REspirAtory Decompensation study)
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Buscema et al. (2020) [63]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Analyze the evolution of the Covid-19 phenomenon
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|TWC algorithm
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Geospatial coordinates of latitude and longitude of the Italian locations where the events occurred.
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Chakraborti et al. (2021) [64]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Perform the regression modelling and provide subsequent interpretation of most critical factors
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|RF; GBM
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|European Centre for Disease Prevention and Control

(ECDC)
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Chatterjee et al. (2020) [65]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Analyze datasets to

understand the trend of

Covid-19
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Statistical and univariate time series
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Oxford University

Database
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Chimmula & Zhang (2020) [66]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Forecast Covid-19 transmission
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Time series; DL; LSTM networks
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Johns Hopkins university; Canadian health authority
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Cobre et al. (2021) [67]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Predict Covid-19 diagnosis and disease severity
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|ANN; DT;

PLS-DA; KNN
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Kaggle platform

5,643 patient samples
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Ebinger et al. (2021) [68]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Predict the likelihood of prolonged LOS
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|3 ML models developed using DataRobot
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|966 patients
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Fong et al. (2020) [69]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Find a forecasting model (GROOWS) from a small dataset for Covid-19 cases
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|PNN+cf
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Archive

of Chinese health authorities
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Gothai et al. (2021) [70]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Predict the growth and trend of Covid-19
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|LR; SVM; time series
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|172,479 documents from Johns Hopkins University

Repository
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Jain et al. (2021) [71]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Predict Covid-19
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|SVM; Naïve Bayes; KNN; AdaBoost; GBoost; RF; ANN
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|B-cell dataset
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Kang et al. (2021) [72]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Predict severe Covid-19 cases
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|ANN
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|151 cases of a China center
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Kavadi et al. (2020) [73]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Global pandemic prediction of Covid-19
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|PDR-NML

method
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Kaggle
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Khan et al. (2021) [74]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Predict the time after which the number of cases stops rising in India
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|DT; SVM;

GPR
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Ministry of Health and Family Welfare (MoHFW) on

10th June 2020
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Lmater et al. (2021) [75]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Present an effective mathematical model for predicting the spread of the (Covid-19) pandemic.
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|SIDR model (susceptible, infected, diagnosed and recovered stages)
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Epidemiological data from 4 countries: Belgium; Morocco; Netherlands; Russia
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Malefors et al. (2021) [76]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Predict guest attendance during the pandemic (meal planning in Sweden)
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|RF; ANN
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Data from 18 primary school kitchens and 16 preschool kitchens
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Mojjada et al. (2020) [77]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Show the ability to predict the number of individuals who are affected by Covid-19.
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|LASSO; SVM;

LR
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Git Hub, supplied

by Johns Hopkins University
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Nemati et al. (2020) [78]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Predict patients’

period of stay in hospital
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|7 ML and statistical analysis

techniques
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|1,182 hospitalized patients
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Ong et al. (2020) [79]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Predict Covid-19 vaccine candidates
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Vaxign reverse vaccinology tools
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|ClinicalTrials.gov database

and PubMed literature.
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Papastefanopoulos et al. (2020) [80]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Investigate the accuracy of six time series for coronavirus to forecast active cases per population
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Six time series
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Kaggle; population-by-country dataset
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Peng & Nagata (2020) [81]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Predict the number of Covid-19 cases for the 12 most affected countries
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|SVR
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|12 most affected countries
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Pinter et al. (2020) [82]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Predict the Covid-19 pandemic for Hungary
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Hybrid ML: ANFIS and MLP-ICA
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Worldometer for Hungary
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Pourhomayoun & Shakibi (2021) [83]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Determine the risk and predict the mortality risk of patients with Covid-19
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|SVM; ANN; RF; DT; LoR; KNN
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|2,670,000 Covid-19 patients from 146 countries
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Quintero et al. (2021) [84]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Predict the SEIRD variables based on a deep dependence on them
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|GA; AR;

ARIMA
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|The National Institute of Health for Colombia and the National Administrative Department of Statistics
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Ribeiro et al. (2020) [85]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Develop short-term forecasting models to allow forecasting of the number of cases in the future
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|ARIMA;

CUBIST; RF; RIDGE; SVR; SVR
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Cases in Brazil up to April, 19 of 2020; 10 datasets
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Santosh (2020) [86]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Develop AI-driven tools to identify Covid-19 outbreaks
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|AL
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Multitudinal and Multimodal data
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Shahid et al. (2021) [6]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Predict virus detection, spread prevention and medical assistance
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|survey of ML algorithms and models
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|---
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|V. Singh et al. (2020) [87]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Produce a real-time SEIR model of confirmed, deceased, and recovered Covid-19 cases.
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|SVM; time series
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Johns Hopkins CSSE; data from January 22, 2020 to April 25, 2020
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Sujath et al. (2020) [88]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Predict the spread of Covid-2019
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|LR; MLP; VAR
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Kaggle; Indian database
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Tarik et al. (2021) [89]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Predict Moroccan student performance
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|RF; DT; LR
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Referral system
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Tuli et al. (2020) [90]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Analyze and predict the growth of the epidemic
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|GIWD in a

cloud computing platform
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Our World in Data by Hannah Ritchie
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Wadhwa et al. (2021) [91]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Predict the extension of lockdown in order to eradicate

Covid-19 from India.
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|LR
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Total number of

cases, deaths, and recoveries all over India.
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|P. Wang et al. (2020) [92]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Predict epidemic trends
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|LoR
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Johns Hopkins University, from January 22, 2020 to June 16, 2020.
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Yan et al. (2020) [93]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Identify crucial predictive biomarkers of Covid-19 mortality
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|XGBoost
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|485 patients
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Yadav et al. (2020) [94]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Solve 5 different tasks:

I) Predict the spread of the disease; II) Analyze the growth rates; III) Predict how the pandemic will end; IV) Analyze the transmission rate; and V) Correlate the disease to the weather conditions.
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|SVR; SLR; PR
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Data from different countries
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Yeşilkanat (2020) [95]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Estimate the number of future cases for 190 countries in the world
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|RF
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Johns Hopkins University Center for Systems Science; Engineering
|-
|  style="border-top: 1pt solid black;border-bottom: 2pt solid black;text-align: center;"|Zivkovic et al. (2021) [96]
|  style="border-top: 1pt solid black;border-bottom: 2pt solid black;text-align: center;"|Predict the number of new coronavirus cases
|  style="border-top: 1pt solid black;border-bottom: 2pt solid black;text-align: center;"|ANFIS; BASSI
|  style="border-top: 1pt solid black;border-bottom: 2pt solid black;text-align: center;"|6 benchmark

Functions
|}

Acronyms (alphabetical): See Table 1.

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

<div class="center" style="width: auto; margin-left: auto; margin-right: auto;">
'''Table 4.''' Works (38) related to Covid-19 that used ML techniques involving general subjects.</div>

{| style="width: 100%;border-collapse: collapse;" 
|-
|  style="border-top: 2pt solid black;border-bottom: 1pt solid black;text-align: center;"|'''Authors (year)'''
|  style="border-top: 2pt solid black;border-bottom: 1pt solid black;text-align: center;"|'''Focus of the paper'''
|  style="border-top: 2pt solid black;border-bottom: 1pt solid black;text-align: center;"|'''ML Techniques'''
|  style="border-top: 2pt solid black;border-bottom: 1pt solid black;text-align: center;"|'''Databases'''
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Alves et al. (2021) [98]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Present understandable solutions to deal with Covid-19 screening in routine blood tests
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|DT

Explainer and criteria graphs
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|608 patients; public dataset from the Albert Einstein Hospital, São Paulo
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Baralić et al. (2020) [99]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Assess risks and benefits of Covid- 19 treatment with promising drug combinations: lopinavir/ritonavir and chloroquine/hydroxychloroquine+ azithromycin.
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|''in silico ''toxicogenomic data-mining approach
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Comparative Toxicogenomics Database
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Carrillo-Larco & Castillo-Cara (2020) [100]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Clustering countries which shared profiles of the pandemic
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|k-means; statistical techniques
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|155 countries; Johns Hopkins University and others
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Di Castelnuovo et al. (2020) [97]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Identify the characteristics predisposing Covid-19 patients

to in-hospital death.
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|RF
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|3,894 patients

hospitalized from a defined period (Italy)
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Choudrie et al. (2021) [101]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Explore how ML techniques and experienced people process the online infodemic related to prevention and cure
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|DT; CNN
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|143 patients
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Dandekar et al. (2020) [102]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Develop a globally applicable diagnostic Covid-19 model
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|SIR; NN
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|70 countries
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Doanvo et al. (2020) [15]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Identify knowledge research Covid-19 gaps in the literature
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|PCA
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|35,281 abstracts from CORD-19
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Fong et al. (2020) [103]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Gain stochastic insights into the pandemic development
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|CMC: DL; FRI
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Empirical data from the Chinese CDCP
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Godavarthi & Sowjanya (2021) [104]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Extract information from the scientific literature: text classification
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|KNN; MLP; XGBoost
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|CORD-19 dataset
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Hu et al. (2021) [105]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Detect the changes in air pollutants during Covid-19 lockdown
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|RF models
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Data from 35 sites in Beijing, from 2015 to 2020
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Jamshidi et al. (2020) [106]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Present a response to combat the virus through AI
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|GANs; LSTM; ELM
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|---
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Kadioglu et al. (2021) [107]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Identify compounds against three targets of Covid-19
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Pharmaco strategy'' in silico''
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Chemical libraries (FDA-approved drugs; natural compound datasets; ZINC database)
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Khanday et al. (2020) [108]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Detect Covid-19 through clinical text data
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|LoR; MNB
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Data repository GitHub
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Kuo & Fu (2021) [109]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Analyze demographic and environmental impact and mobility during the pandemic period
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Elastic net model; PCR; PLSR; KNN;  RT; RF; GBM; 2-layer ANN
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|New York Times; USDA ERA; gridMed; Google
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Lam et al. (2021) [110]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Present a ML system capable of identifying patients who could be treated with a corticosteroid or remdesivir
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|GBM
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|893 patients
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|M. Li et al. (2021) [16]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Detect novel critical factors associated with Covid-19 in 154 countries and in the 50 USA states
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|LoR; LASSO
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Johns Hopkins University
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Lip et al. (2021) [111]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Identify patients with Covid-19 who are at the highest risk of developing incident AF
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Inferential statistics and ML computations (LoR)
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Data from April 1, 2018 to Nov 30, 2020
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Loey et al. (2021) [112]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Develop a DL and classical ML for face detection
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|DL; DT; SVM; EA
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|3 datasets
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Lovrić et al. (2021) [113]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Analyze improvements in air quality during the Covid-19 lockdown
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|RFR
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Graz, Styria, Austria
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Magazzino et al. (2021) [114]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Analyze the relationship between Covid-19 deaths, economic growth and air pollution
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|DL
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|--
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|McRae et al. (2020) [115]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Develop a decision support tool and rapid point-of-care platform to determine severity in patients with Covid-19
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Statistical learning algorithm
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|160 patients from Wuhan, China
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Malki et al. (2020) [116]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Verify the relationship between weather and Covid-19
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Regressor ML models
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Meteoblue website
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Mele & Magazzino (2021) [117]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Explore the relationship between pollution, economic growth and

Covid-19 deaths in India
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Time Series approach; Stationarity

and Toda-Yamamoto causality tests
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Indian data from January 29 to May 18, 2020
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Petetin et al. (2020) [118]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Use meteorological

data to estimate the

“business-as-usual” NO2 mixing ratios
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|GBM
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|GHOST
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Qiang et al. (2020) [119]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Evaluate the infection risk of Covid-19 for early warning through spike protein feature
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|RF models
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|507 human origin

viruses and 2,159 non-human-origin viruses
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Radanliev et al. (2020) [120]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Investigate the scientific research response from the early stages of the pandemic
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Statistical methods
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|WSCC
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Randhawa et al. (2020) [121]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Use intrinsic genomic signatures to classify Covid-19 rapidly
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|MLDSP for genome analyses; DT
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Dataset of over 5,000 unique viral genomic sequences from the NCBI
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Shrock et al. (2020) [122]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Explore antiviral antibody

responses across the human virome
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|XGBoost
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|232 coronavirus disease patients and 190 pre-Covid-19
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|X. Sun et al. (2020) [123]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Explore TCM formulae to investigate their compatibility with the CMM to understand their potential mechanisms for treatment of Covid-19
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|TCM; CMM
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Encyclopedia of Traditional Chinese Medicine database; BATMAN-TCM database
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|C. L. F. Sun et al. (2020) [124]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Identify risks and vectors of infection in nursing homes
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|GBA
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|1146 NHs in Massachusetts
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Swapnarekha et al. (2020) [125]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Present a state-of-the-art analysis using ML and DL methods in the diagnosis and prediction of Covid-19
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|ML; DL
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|January 23, 2020 to April, 21, 2020
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|S. Tiwari et al. (2020) [126]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Prepare Indian government and citizens to take control measures (SEIR)
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Time Series
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Kaggle (data available between January 22, 2020, and April 3, 2020, from India and China)
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|A. Tiwari et al. (2021) [127]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Define a Covid-19 Vulnerability Index (C19VI) for identifying and mapping counties considered vulnerable
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|RF
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Johns Hopkins University; Centers for Disease Control and Prevention
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Toğaçar et al. (2020) [128]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Detect Coronavirus
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|DL; SVM; SMOM
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|GitHub; Kaggle
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Vaishya et al. (2020) [129]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Revise the effectiveness of AI techniques for Covid-19
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|AI techniques
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|PubMed, Scopus and Google Scholar datasets
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|W.-C. Wang et al. (2021) [130]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Develop a system for monitoring global and local community outbreaks
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|k-means
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Johns Hopkins; data with daily infected, recovered and death cases
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Yacchirema & Chura (2021) [131]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Implement a system based on IoT for saver mobility during the pandemic
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|SVM; DT; LoR; RF; KNN

(to detect the location of people)
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|From portable IoT devices
|-
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Yang et al. (2020) [132]
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|Demonstrate control measures impact the containment of the epidemic
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|SEIR model
|  style="border-top: 1pt solid black;border-bottom: 1pt solid black;text-align: center;"|2003 SARS data
|}

Acronyms (alphabetical): See Table 1.

==4.2 Bibliometric Literature Review==

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

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

<div class="center" style="width: auto; margin-left: auto; margin-right: auto;">
'''Figure 2.''' Top 54% of journals in the selected papers.</div>

<div class="center" style="width: auto; margin-left: auto; margin-right: auto;">
[[File:Review_936792395077_5667_Figure 2.svg|500px]] </div>


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

<div class="center" style="width: auto; margin-left: auto; margin-right: auto;">
'''Figure 3.''' Top 61% of publications with the most used methods.</div>

<div class="center" style="width: auto; margin-left: auto; margin-right: auto;">
 [[File:Review_936792395077_1718_Figure 3.svg|500px]] </div>


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

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

<div class="center" style="width: auto; margin-left: auto; margin-right: auto;">
'''Figure 4.''' Top 70% of authors' nationality.</div>

<div class="center" style="width: auto; margin-left: auto; margin-right: auto;">
[[File:Review_936792395077_2204_Figure 4.svg|500px]] </div>


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

=5. Concluding Remarks=

The aim of the present study was to conduct a systematic and bibliometric review of the articles involving the protocol shown in '''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.

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

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

==Declaration of competing interest==

None.

==Funding sources==

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

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