Introduction: Machine learning (ML) plays a significant role in the fight against the COVID-19 (officially known as SARS-CoV-2) pandemic. ML techniques enable the rapid detection of patterns and trends in large datasets. Therefore, ML provides efficient methods to generate knowledge from structured and unstructured data. This potential is particularly significant when the pandemic affects all aspects of human life. It is necessary to collect a large amount of data to identify methods to prevent the spread of infection, early detection, reduction of consequences, and finding appropriate medicine. Modern information and communication technologies (ICT) such as the Internet of Things (IoT) allow the collection of large amounts of data from various sources. Thus, we can create predictive ML-based models for assessments, predictions, and decisions. Methods: This is a review article based on previous studies and scientifically proven knowledge. In this paper, bibliometric data from authoritative databases of research publications (Web of Science, Scopus, PubMed) are combined for bibliometric analyses in the context of ML applications for COVID-19. Aim: This paper reviews some ML-based applications used for mitigating COVID-19. We aimed to identify and review ML potentials and solutions for mitigating the COVID-19 pandemic as well as to present some of the most commonly used ML techniques, algorithms, and datasets applied in the context of COVID-19. Also, we provided some insights into specific emerging ideas and open issues to facilitate future research. Conclusion: ML is an effective tool for diagnosing and early detection of symptoms, predicting the spread of a pandemic, developing medicines and vaccines, etc.
References
1.
Albahri OS, Al-Obaidi JR, Zaidan AA, Albahri AS, Zaidan BB, Salih MM, et al. Helping doctors hasten COVID-19 treatment: Towards a rescue framework for the transfusion of best convalescent plasma to the most critical patients based on biological requirements via ml and novel MCDM methods. Computer Methods and Programs in Biomedicine. 2020;196:105617.
2.
Gozes O, Frid-Adar M, Greenspan H, Browning PD, Zhang H, Ji W, et al. Rapid AI Development Cycle for the Coronavirus (COVID-19) Pandemic: Initial Results for Automated Detection & Patient Monitoring using Deep Learning CT Image Analysis.
3.
Su Z, McDonnell D, Wen J, Cheshmehzangi A, Ahmad J, Goh E, et al. Young adults’ preferences for influenza vaccination campaign messages: Implications for COVID-19 vaccine intervention design and development. Brain, Behavior, & Immunity - Health. 2021;14:100261.
4.
Romeo L, Frontoni E. A Unified Hierarchical XGBoost model for classifying priorities for COVID-19 vaccination campaign. Pattern Recognition. 2022;121:108197.
5.
Beck BR, Shin B, Choi Y, Park S, Kang K. Predicting commercially available antiviral drugs that may act on the novel coronavirus (SARS-CoV-2) through a drug-target interaction deep learning model. Computational and Structural Biotechnology Journal. 2020;18:784–90.
6.
Banerjee A, Santra D, Maiti S. Energetics and IC50 based epitope screening in SARS CoV-2 (COVID 19) spike protein by immunoinformatic analysis implicating for a suitable vaccine development. Journal of Translational Medicine. 2020;18(1).
7.
Ahmed SF, Quadeer AA, McKay MR. Preliminary Identification of Potential Vaccine Targets for the COVID-19 Coronavirus (SARS-CoV-2) Based on SARS-CoV Immunological Studies. Viruses. 12(3):254.
8.
Magar R, Yadav P, Barati Farimani A. Potential neutralizing antibodies discovered for novel corona virus using machine learning. Scientific Reports. 11(1).
9.
Zhavoronkov A, Aladinskiy V, Zhebrak A, Zagribelnyy B, Terentiev V, Bezrukov DS, et al. Potential 2019-nCoV 3C-like Protease Inhibitors Designed Using Generative Deep Learning Approaches.
10.
Ward D, Higgins M, Phelan JE, Hibberd ML, Campino S, Clark TG. An integrated in silico immuno-genetic analytical platform provides insights into COVID-19 serological and vaccine targets. Genome Medicine. 2021;13(1).
11.
Chen S, Yang J, Yang W, Wang C, Bärnighausen T. COVID-19 control in China during mass population movements at New Year. The Lancet. 2020;395(10226):764–6.
12.
Haleem A, Vaishya R, Javaid M, Khan IH. Artificial Intelligence (AI) applications in orthopaedics: An innovative technology to embrace. Journal of Clinical Orthopaedics and Trauma. 2020;11:S80–1.
13.
Chimmula VKR, Zhang L. Time series forecasting of COVID-19 transmission in Canada using LSTM networks. Chaos, Solitons & Fractals. 2020;135:109864.
14.
Haleem A, Javaid M, Vaishya R. Effects of COVID-19 pandemic in daily life. Current Medicine Research and Practice. 2020;10(2):78–9.
15.
Bai X, Fang C, Zhou Y, Bai S, Liu Z, Xia L, et al. Predicting COVID-19 Malignant Progression with AI Techniques. SSRN Electronic Journal.
16.
Hu C, Liu Z, Jiang Y, Shi O, Zhang X, Xu K, et al. Early prediction of mortality risk among patients with severe COVID-19, using machine learning. International Journal of Epidemiology. 2021;49(6):1918–29.
17.
Carrillo-Larco RM, 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 Research. 5:56.
18.
Kavadi DP, Patan R, Ramachandran M, Gandomi AH. Partial derivative Nonlinear Global Pandemic Machine Learning prediction of COVID 19. Chaos, Solitons & Fractals. 2020;139:110056.
19.
Giordano G, Blanchini F, Bruno R, Colaneri P, Di Filippo A, Di Matteo A, et al. Modelling the COVID-19 epidemic and implementation of population-wide interventions in Italy. Nature Medicine. 2020;26(6):855–60.
20.
Maghded HS, Ghafoor KZ, Sadiq AS, Curran K, Rawat DB, Rabie K. A Novel AI-enabled Framework to Diagnose Coronavirus COVID-19 using Smartphone Embedded Sensors: Design Study. 2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science (IRI). 2020. p. 180–7.
21.
Chen YC, Lu PE, Chang CS, Liu TH. A Time-Dependent SIR Model for COVID-19 With Undetectable Infected Persons. IEEE Transactions on Network Science and Engineering. 2020;7(4):3279–94.
22.
Mbunge E. Integrating emerging technologies into COVID-19 contact tracing: Opportunities, challenges and pitfalls. Diabetes & Metabolic Syndrome: Clinical Research & Reviews. 2020;14(6):1631–6.
23.
Gaeta G. A simple SIR model with a large set of asymptomatic infectives. Mathematics in Engineering. 2021;3(2):1–39.
24.
Crokidakis N. Modeling the early evolution of the COVID-19 in Brazil: Results from a Susceptible–Infectious–Quarantined–Recovered (SIQR) model. International Journal of Modern Physics C. 2020;31(10):2050135.
25.
Dandekar R, Barbastathis G. Neural Network aided quarantine control model estimation of COVID spread in Wuhan, China.
26.
Tuncer T, Dogan S, Ozyurt F. An automated Residual Exemplar Local Binary Pattern and iterative ReliefF based COVID-19 detection method using chest X-ray image. Chemometrics and Intelligent Laboratory Systems. 2020;203:104054.
27.
Pahar M, Klopper M, Warren R, Niesler T. COVID-19 detection in cough, breath and speech using deep transfer learning and bottleneck features. Computers in Biology and Medicine. 2022;141:105153.
28.
Despotovic V, Ismael M, Cornil M, Call RM, Fagherazzi G. Detection of COVID-19 from voice, cough and breathing patterns: Dataset and preliminary results. Computers in Biology and Medicine. 2021;138:104944.
29.
Brown C, Chauhan J, Grammenos A, Han J, Hasthanasombat A, Spathis D, et al. Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2020. p. 3474–84.
30.
Wang Y, Hu M, Li Q, Zhang XP, Zhai G, Yao N. Abnormal respiratory patterns classifier may contribute to large-scale screening of people infected with COVID-19 in an accurate and unobtrusive manner.
31.
Jiang Z, Hu M, Gao Z, Fan L, Dai R, Pan Y, et al. Detection of Respiratory Infections Using RGB-Infrared Sensors on Portable Device. IEEE Sensors Journal. 2020;20(22):13674–81.
32.
Wang Y, Hu M, Zhou Y, Li Q, Yao N, Zhai G, et al. Unobtrusive and Automatic Classification of Multiple People’s Abnormal Respiratory Patterns in Real Time Using Deep Neural Network and Depth Camera. IEEE Internet of Things Journal. 2020;7(9):8559–71.
33.
Khanday AMUD, Rabani ST, Khan QR, Rouf N, Mohi Ud Din M. Machine learning based approaches for detecting COVID-19 using clinical text data. International Journal of Information Technology. 2020;12(3):731–9.
34.
Brinati D, Campagner A, Ferrari D, Locatelli M, Banfi G, Cabitza F. Detection of COVID-19 Infection from Routine Blood Exams with Machine Learning: A Feasibility Study. Journal of Medical Systems. 2020;44(8).
35.
Wu J, Zhang P, Zhang L, Meng W, Li J, Tong C, et al. Rapid and accurate identification of COVID-19 infection through machine learning based on clinical available blood test results.
36.
de Moraes Batista AF, Miraglia JL, Rizzi Donato TH, Porto Chiavegatto Filho AD. COVID-19 diagnosis prediction in emergency care patients: a machine learning approach.
37.
Yang HS, Hou Y, Vasovic LV, Steel PAD, Chadburn A, Racine-Brzostek SE, et al. Routine Laboratory Blood Tests Predict SARS-CoV-2 Infection Using Machine Learning. Clinical Chemistry. 2020;66(11):1396–404.
38.
Zhong X, Deng F, Ouyang H. Sharp Threshold for the Dynamics of a SIRS Epidemic Model With General Awareness- Induced Incidence and Four Independent Brownian Motions. IEEE Access. 2020;8:29648–57.
39.
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. Computers in Biology and Medicine. 2020;121:103805.
40.
Yoo SH, Geng H, Chiu TL, Yu SK, Cho DC, Heo J, et al. Deep Learning-Based Decision-Tree Classifier for COVID-19 Diagnosis From Chest X-ray Imaging. Frontiers in Medicine. 7.
41.
Sethy PK, Behera SK, Ratha PK, Biswas P. Detection of coronavirus Disease (COVID-19) based on Deep Features and Support Vector Machine. International Journal of Mathematical, Engineering and Management Sciences. 5(4):643–51.
42.
Abbasian Ardakani A, Acharya UR, Habibollahi S, Mohammadi A. COVIDiag: a clinical CAD system to diagnose COVID-19 pneumonia based on CT findings. European Radiology. 2021;31(1):121–30.
43.
Zhou L, Li Z, Zhou J, Li H, Chen Y, Huang Y, et al. A Rapid, Accurate and Machine-Agnostic Segmentation and Quantification Method for CT-Based COVID-19 Diagnosis. IEEE Transactions on Medical Imaging. 2020;39(8):2638–52.
44.
Kang H, Xia L, Yan F, Wan Z, Shi F, Yuan H, et al. Diagnosis of Coronavirus Disease 2019 (COVID-19) With Structured Latent Multi-View Representation Learning. IEEE Transactions on Medical Imaging. 2020;39(8):2606–14.
45.
Tuli S, Tuli S, Tuli R, Gill SS. Predicting the growth and trend of COVID-19 pandemic using machine learning and cloud computing. Internet of Things. 2020;11:100222.
46.
Paul SK, Jana S, Bhaumik P. A Multivariate Spatiotemporal Model of COVID-19 Epidemic Using Ensemble of ConvLSTM Networks. Journal of The Institution of Engineers (India): Series B. 2021;102(6):1137–42.
47.
Shahid F, Zameer A, Muneeb M. Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM. Chaos, Solitons & Fractals. 2020;140:110212.
48.
Ye Y, Hou S, Fan Y, Zhang Y, Qian Y, Sun S, et al. $\alpha$-Satellite: An AI-Driven System and Benchmark Datasets for Dynamic COVID-19 Risk Assessment in the United States. IEEE Journal of Biomedical and Health Informatics. 2020;24(10):2755–64.
49.
Rustam F, Reshi AA, Mehmood A, Ullah S, On BW, Aslam W, et al. COVID-19 Future Forecasting Using Supervised Machine Learning Models. IEEE Access. 2020;8:101489–99.
50.
Yin AL, Guo WL, Sholle ET, Rajan M, Alshak MN, Choi JJ, et al. Comparing automated vs. manual data collection for COVID-specific medications from electronic health records. International Journal of Medical Informatics. 2022;157:104622.
51.
Cohen JP, Morrison P, Dao L. COVID-19 Image Data Collection.
52.
Zhao X, Liu X, Li X. Tracking the spread of novel coronavirus (2019-nCoV) based on big data.
53.
Yadav M, Perumal M, Srinivas M. Analysis on novel coronavirus (COVID-19) using machine learning methods. Chaos, Solitons & Fractals. 2020;139:110050.
54.
Iwendi C, Bashir AK, Peshkar A, Sujatha R, Chatterjee JM, Pasupuleti S, et al. COVID-19 Patient Health Prediction Using Boosted Random Forest Algorithm. Frontiers in Public Health. 8.
55.
Malki Z, Atlam ES, Hassanien AE, Dagnew G, Elhosseini MA, Gad I. Association between weather data and COVID-19 pandemic predicting mortality rate: Machine learning approaches. Chaos, Solitons & Fractals. 2020;138:110137.
56.
Shuja J, Alanazi E, Alasmary W, Alashaikh A. COVID-19 open source data sets: a comprehensive survey. Applied Intelligence. 2021;51(3):1296–325.
57.
Chen CM, Jyan HW, Chien SC, Jen HH, Hsu CY, Lee PC, et al. Containing COVID-19 Among 627,386 Persons in Contact With the Diamond Princess Cruise Ship Passengers Who Disembarked in Taiwan: Big Data Analytics. Journal of Medical Internet Research. 22(5):e19540.
58.
Geng Z, Zhang X, Fan Z, Lv X, Su Y, Chen H. Recent Progress in Optical Biosensors Based on Smartphone Platforms. Sensors. 17(11):2449.
59.
Ndiaye M, Oyewobi SS, Abu-Mahfouz AM, Hancke GP, Kurien AM, Djouani K. IoT in the Wake of COVID-19: A Survey on Contributions, Challenges and Evolution. IEEE Access. 2020;8:186821–39.
60.
Chen B, Shi M, Ni X, Ruan L, Jiang H, Yao H, et al. Visual Data Analysis and Simulation Prediction for COVID-19. International Journal of Educational Excellence. 6(1):95–114.
61.
Hou Z, Du F, Jiang H, Zhou X, Lin L. Assessment of public attention, risk perception, emotional and behavioural responses to the COVID-19 outbreak: social media surveillance in China.
62.
Dong E, Du H, Gardner L. An interactive web-based dashboard to track COVID-19 in real time. The Lancet Infectious Diseases. 2020;20(5):533–4.
63.
Panch T, Szolovits P, Atun R. Artificial intelligence, machine learning and health systems. Journal of Global Health. 2018;8(2).
64.
Libbrecht MW, Noble WS. Machine learning applications in genetics and genomics. Nature Reviews Genetics. 2015;16(6):321–32.
65.
Islam MdZ, Islam MdM, Asraf A. A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images. Informatics in Medicine Unlocked. 2020;20:100412.
66.
Hassan H, Ren Z, Zhao H, Huang S, Li D, Xiang S, et al. Review and classification of AI-enabled COVID-19 CT imaging models based on computer vision tasks. Computers in Biology and Medicine. 2022;141:105123.
67.
Verma A, Amin SB, Naeem M, Saha M. Detecting COVID-19 from chest computed tomography scans using AI-driven android application. Computers in Biology and Medicine. 2022;143:105298.
68.
Xu B, Kraemer MUG, Xu B, Gutierrez B, Mekaru S, Sewalk K, et al. Open access epidemiological data from the COVID-19 outbreak. The Lancet Infectious Diseases. 2020;20(5):534.
69.
Rasheed J, Jamil A, Hameed AA, Aftab U, Aftab J, Shah SA, et al. A survey on artificial intelligence approaches in supporting frontline workers and decision makers for the COVID-19 pandemic. Chaos, Solitons & Fractals. 2020;141:110337.
70.
Ai T, Yang Z, Hou H, Zhan C, Chen C, Lv W, et al. Correlation of Chest CT and RT-PCR Testing for Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 Cases. Radiology. 2020;296(2):E32–40.
71.
Campbell TW, Wilson MP, Roder H, MaWhinney S, Georgantas RW, Maguire LK, et al. Predicting prognosis in COVID-19 patients using machine learning and readily available clinical data. International Journal of Medical Informatics. 2021;155:104594.
72.
Kucharski AJ, Russell TW, Diamond C, Liu Y, Edmunds J, Funk S, et al. Early dynamics of transmission and control of COVID-19: a mathematical modelling study. The Lancet Infectious Diseases. 2020;20(5):553–8.
73.
Nwanosike EM, Conway BR, Merchant HA, Hasan SS. Potential applications and performance of machine learning techniques and algorithms in clinical practice: A systematic review. International Journal of Medical Informatics. 2022;159:104679.
74.
J.L. G, Abraham B, M.S. S, Nair MS. A computer-aided diagnosis system for the classification of COVID-19 and non-COVID-19 pneumonia on chest X-ray images by integrating CNN with sparse autoencoder and feed forward neural network. Computers in Biology and Medicine. 2022;141:105134.
75.
Chamola V, Hassija V, Gupta V, Guizani M. A Comprehensive Review of the COVID-19 Pandemic and the Role of IoT, Drones, AI, Blockchain, and 5G in Managing its Impact. IEEE Access. 2020;8:90225–65.
76.
Imran A, Posokhova I, Qureshi HN, Masood U, Riaz MS, Ali K, et al. AI4COVID-19: AI enabled preliminary diagnosis for COVID-19 from cough samples via an app. Informatics in Medicine Unlocked. 2020;20:100378.
77.
Li WT, Ma J, Shende N, Castaneda G, Chakladar J, Tsai JC, et al. Using machine learning of clinical data to diagnose COVID-19: a systematic review and meta-analysis. BMC Medical Informatics and Decision Making. 2020;20(1).
78.
LAHSAINI I, EL HABIB DAHO M, CHIKH MA. Deep transfer learning based classification model for covid-19 using chest CT-scans. Pattern Recognition Letters. 2021;152:122–8.
79.
Panahi A, Askari Moghadam R, Akrami M, Madani K. Deep Residual Neural Network for COVID-19 Detection from Chest X-ray Images. SN Computer Science. 2022;3(2).
80.
Jin C, Chen W, Cao Y, Xu Z, Tan Z, Zhang X, et al. Development and evaluation of an artificial intelligence system for COVID-19 diagnosis. Nature Communications. 11(1).
81.
Xu X, Jiang X, Ma C, Du P, Li X, Lv S, et al. A Deep Learning System to Screen Novel Coronavirus Disease 2019 Pneumonia. Engineering. 2020;6(10):1122–9.
82.
Wang S, Kang B, Ma J, Zeng X, Xiao M, Guo J, et al. A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19). European Radiology. 2021;31(8):6096–104.
83.
Wang L, Lin ZQ, Wong A. COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images. Scientific Reports. 10(1).
84.
Khan AI, Shah JL, Bhat MM. CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images. Computer Methods and Programs in Biomedicine. 2020;196:105581.
85.
Tang Z, Zhao W, Xie X, Zhong Z, Shi F, Liu J, et al. Severity Assessment of Coronavirus Disease 2019 (COVID-19) Using Quantitative Features from Chest CT Images.
86.
Lalmuanawma S, Hussain J, Chhakchhuak L. Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review. Chaos, Solitons & Fractals. 2020;139:110059.
87.
Fomsgaard AS, Rosenstierne MW. An alternative workflow for molecular detection of SARS-CoV-2 – escape from the NA extraction kit-shortage, Copenhagen, Denmark, March 2020. Eurosurveillance. 2020;25(14).
88.
Wang L, Alexander CA. Machine Learning in Big Data. International Journal of Mathematical, Engineering and Management Sciences. 1(2):52–61.
89.
Wu PY, Cheng CW, Kaddi CD, Venugopalan J, Hoffman R, Wang MD. –Omic and Electronic Health Record Big Data Analytics for Precision Medicine. IEEE Transactions on Biomedical Engineering. 2017;64(2):263–73.
90.
Berral-Garcia JL. A quick view on current techniques and machine learning algorithms for big data analytics. 2016 18th International Conference on Transparent Optical Networks (ICTON). 2016. p. 1–4.
91.
Qiu J, Wu Q, Ding G, Xu Y, Feng S. Erratum to: A survey of machine learning for big data processing. EURASIP Journal on Advances in Signal Processing. 2016;2016(1).
92.
Likas A, Vlassis N, J. Verbeek J. The global k-means clustering algorithm. Pattern Recognition. 2003;36(2):451–61.
93.
Feng S, Xu L. An intelligent decision support system for fuzzy comprehensive evaluation of urban development. Expert Systems with Applications. 1999;16(1):21–32.
94.
Fetić A, Dželihodžić A. A practical implementation of machine learning in predicting breast cancer. Science, Engineering and Technology. 1(2):16–23.
95.
European Centre for Disease Prevention and Control. Download COVID-19 datasets.
96.
Mathieu E, Ritchie H, Ortiz-Ospina E, Roser M, Hasell J, Appel C, et al. A global database of COVID-19 vaccinations. Nature Human Behaviour. 5(7):947–53.
97.
Pandey G. SEIR and Regression Model-based COVID-19 outbreak predictions in India (Preprint).
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