Utility of Artificial Intelligence Amidst the COVID 19 Pandemic: A Review
Open accessAbstract
The term machine learning refers to a collection of tools used for identifying patterns in data. As opposed to traditional methods of pattern identification, machine learning tools relies on artificial intelligence to map out patters from large amounts of data, can self-improve as and when new data becomes available and is quicker in accomplishing these tasks. This review describes various techniques of machine learning that have been used in the past in the prediction, detection and management of infectious diseases, and how these tools are being brought into the battle against COVID-19. In addition, we also discuss their applications in various stages of the pandemic, the advantages, disadvantages and possible pit falls.
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Zhou, F., Yu, T., Du, R., Fan, G., Liu, Y., Liu, Z., Xiang, J., Wang, Y., Song, B., Gu, X., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30566-3 · Cited by 27,263 · see citing works
An interactive web-based dashboard to track COVID-19 in real time
Dong, E., Du, H., & Gardner, L. (2020) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(20)30120-1 · Cited by 10,048 · see citing works
Santos, A. G., da Rocha, G. O., & de Andrade, J. B. (2019) · Scientific Reports · doi:10.1038/s41598-018-37186-2 · Cited by 8,495 · see citing works
Global trends in emerging infectious diseases
Jones, K. E., Patel, N. G., Levy, M. A., Storeygard, A., Balk, D., Gittleman, J. L., & Daszak, P. (2008) · Nature · doi:10.1038/nature06536 · Cited by 8,338 · see citing works
Ai, T., Yang, Z., Hou, H., Zhan, C., Chen, C., Lv, W., Tao, Q., Sun, Z., & Xia, L. (2020) · Radiology · doi:10.1148/radiol.2020200642 · Cited by 5,697 · see citing works
Wu, J. T., Leung, K., & Leung, G. M. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30260-9 · Cited by 4,389 · see citing works
Early dynamics of transmission and control of COVID-19: a mathematical modelling study
Kucharski, A. J., Russell, T., Diamond, C., Liu, Y., Edmunds, J., Funk, S., Eggo, R. M., Sun, F., Jit, M., Munday, J. D., et al. (2020) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(20)30144-4 · Cited by 2,675 · see citing works
A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19)
Wang, S., Kang, B., Ma, J., Zeng, X., Xiao, M., Guo, J., Cai, M., Yang, J., Li, Y., Meng, X., et al. (2021) · European Radiology · doi:10.1007/s00330-021-07715-1 · Cited by 1,668 · see citing works
Response to COVID-19 in Taiwan: Big Data Analytics, New Technology, and Proactive Testing
Wang, C. J., Ng, C. Y., & Brook, R. H. (2020) · JAMA · doi:10.1001/jama.2020.3151 · Cited by 1,391 · see citing works
A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19)Preprint
Wang, S., Kang, B., Ma, J., et al. (2020) · medRxiv · doi:10.1101/2020.02.14.20023028 · Cited by 1,035 · see citing works
Bogoch, I. I., Watts, A., Thomas-Bachli, A. L., Huber, C., Kraemer, M. U. G., & Khan, K. (2020) · Journal of Travel Medicine · doi:10.1093/jtm/taaa008 · Cited by 899 · see citing works
Digital Phenotyping: Technology for a New Science of Behavior
Insel, T. R. (2017) · JAMA · doi:10.1001/jama.2017.11295 · Cited by 833 · see citing works
Interventions to mitigate early spread of SARS-CoV-2 in Singapore: a modelling study
Koo, J. R., Cook, A. R., Park, M., Sun, Y., Sun, H., Lim, J. T., Tam, C. C., & Dickens, B. S. L. (2020) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(20)30162-6 · Cited by 733 · see citing works
Harnessing Smartphone-Based Digital Phenotyping to Enhance Behavioral and Mental Health
Onnela, J.-P., & Rauch, S. L. (2016) · Neuropsychopharmacology · doi:10.1038/npp.2016.7 · Cited by 726 · see citing works
Gozes, O., Frid-Adar, M., Greenspan, H. K., Browning, P. D., Zhang, H., Ji, W., Bernheim, A., & Siegel, E. L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.05037 · Cited by 638 · see citing works
Machine Learning for Healthcare: On the Verge of a Major Shift in Healthcare Epidemiology
Wiens, J., & Shenoy, E. S. (2018) · Clinical Infectious Diseases · doi:10.1093/cid/cix731 · Cited by 574 · see citing works
COVID-19 Coronavirus Vaccine Design Using Reverse Vaccinology and Machine Learning
Ong, E., Wong, M. U., Huffman, A., & He, Y. (2020) · Frontiers in Immunology · doi:10.3389/fimmu.2020.01581 · Cited by 499 · see citing works
Zhao, S., Musa, S. S., Lin, Q., Ran, J., Yang, G., Wang, W., Lou, Y., Yang, L., Gao, D., He, D., et al. (2020) · Journal of Clinical Medicine · doi:10.3390/jcm9020388 · Cited by 439 · see citing works
COVID-19 and artificial intelligence: protecting health-care workers and curbing the spread
McCall, B. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30054-6 · Cited by 396 · see citing works
Rao, A. S. R. S., & Vázquez, J. A. (2020) · Infection Control and Hospital Epidemiology · doi:10.1017/ice.2020.61 · Cited by 394 · see citing works
CoronaTracker: World-wide COVID-19 Outbreak Data Analysis and PredictionPreprint
Hamzah, F. A. B., Lau, C. H., Nazri, H., Ligot, D. V., Lee, G., Tan, C. L., Shaib, M. K. B. M., Zaidon, U. H. B., Abdullah, A. B., Chung, M. H., et al. (2020) · doi:10.2471/blt.20.255695 · Cited by 376 · see citing works
Predicting Infectious Disease Using Deep Learning and Big Data
Chae, S., Kwon, S., & Lee, D.-H. (2018) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph15081596 · Cited by 323 · see citing works
Global Leprosy Status in 2020: Still Losing Touch
Franco‐Paredes, C., Sánchez, G. M. D. O., & White, C. (2020) · Annals of the Academy of Medicine Singapore · doi:10.47102/annals-acadmedsg.2019252 · Cited by 294 · see citing works
Human trophoblast stem cell self-renewal and differentiation: Role of decorin
Nandi, P., Lim, H. J., Torres-Garcia, E. J., & Lala, P. K. (2018) · Scientific Reports · doi:10.1038/s41598-018-27119-4 · Cited by 265 · see citing works
Goh, K. J., Choong, M. C., Cheong, E. H., Kalimuddin, S., Wen, S. D., Phua, G. C., Chan, K. S., & Mohideen, S. H. (2020) · Annals of the Academy of Medicine Singapore · doi:10.47102/annals-acadmedsg.202057 · Cited by 260 · see citing works
Mobile health use in low- and high-income countries: an overview of the peer-reviewed literature
Bastawrous, A., & Armstrong, M. J. (2013) · Journal of the Royal Society of Medicine · doi:10.1177/0141076812472620 · Cited by 236 · see citing works
Machine learning applications in drug development
Réda, C., Kaufmann, E., & Delahaye‐Duriez, A. (2019) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2019.12.006 · Cited by 224 · see citing works
An open challenge to advance probabilistic forecasting for dengue epidemics
Johansson, M., Apfeldorf, K., Dobson, S., Devita, J., Buczak, A. L., Baugher, B., Moniz, L., Bagley, T., Babin, S., Guven, E., et al. (2019) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.1909865116 · Cited by 216 · see citing works
Artificial Intelligence for Drug Discovery, Biomarker Development, and Generation of Novel Chemistry
Zhavoronkov, A. A. (2018) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.8b00930 · Cited by 198 · see citing works
Developing Global Norms for Sharing Data and Results during Public Health Emergencies
Modjarrad, K., Moorthy, V., Millett, P. D., Gsell, P., Roth, C. E., & Kiény, M.-P. (2016) · PLoS Medicine · doi:10.1371/journal.pmed.1001935 · Cited by 193 · see citing works
Sentiment Analysis of Health Care Tweets: Review of the Methods Used
Gohil, S., Vuik, S., & Darzi, A. W. (2018) · JMIR Public Health and Surveillance · doi:10.2196/publichealth.5789 · Cited by 185 · see citing works
Dynamic Forecasting of Zika Epidemics Using Google Trends
Teng, Y., Bi, D., Xie, G., et al. (2017) · PLoS ONE · doi:10.1371/journal.pone.0165085 · Cited by 181 · see citing works
Paolotti, D., Carnahan, A., Vittoria Colizza, A., Eames, K. T. D., Edmunds, J., Gomes, M. G. M., Koppeschaar, C. E., Rehn, M., Smallenburg, R., Turbelin, C., et al. (2013) · Clinical Microbiology and Infection · doi:10.1111/1469-0691.12477 · Cited by 179 · see citing works
Using online social networks to track a pandemic: a systematic review
Al-garadi, M. A., Khan, M. S., Varathan, K. D., Mujtaba, G., & Al-Kabsi, A. M. (2016) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2016.05.005 · Cited by 166 · see citing works
Real-time forecasting of infectious disease dynamics with a stochastic semi-mechanistic model
Funk, S., Camacho, A., Kucharski, A. J., et al. (2016) · Epidemics · doi:10.1016/j.epidem.2016.11.003 · Cited by 142 · see citing works
A Novel Data-Driven Model for Real-Time Influenza Forecasting
Venna, S. R., Tavanaei, A., Gottumukkala, R. N., Raghavan, V. V., Maida, A. S., & Nichols, S. (2018) · IEEE Access · doi:10.1109/access.2018.2888585 · Cited by 123 · see citing works
Lim, S., Tucker, C. S., & Kumara, S. R. T. (2016) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2016.12.007 · Cited by 121 · see citing works
Rethinking Social Amplification of Risk: Social Media and Zika in Three Languages
Wirz, C. D., Xenos, M. A., Brossard, D., Scheufele, D. A., Chung, J. H., & Massarani, L. (2018) · Risk Analysis · doi:10.1111/risa.13228 · Cited by 119 · see citing works
COVID-19 coronavirus vaccine design using reverse vaccinology and machine learningPreprint
Ong, E., Wong, M. U., Huffman, A., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.03.20.000141 · Cited by 119 · see citing works
A dynamic neural network model for predicting risk of Zika in real time
Akhtar, M., Kraemer, M. U. G., & Gardner, L. (2019) · BMC Medicine · doi:10.1186/s12916-019-1389-3 · Cited by 109 · see citing works
Analysis of COVID-19 infection spread in Japan based on stochastic transition model
Karako, K., Song, P., Chen, Y., & Tang, W. (2020) · BioScience Trends · doi:10.5582/bst.2020.01482 · Cited by 92 · see citing works
Colubri, A., Silver, T., Fradet, T., Retzepi, K., Fry, B., & Sabeti, P. C. (2016) · PLoS neglected tropical diseases · doi:10.1371/journal.pntd.0004549 · Cited by 86 · see citing works
Choi, S., Lee, J., Kang, M., Min, H., Chang, Y., & Yoon, S. (2017) · Methods · doi:10.1016/j.ymeth.2017.07.027 · Cited by 70 · see citing works
A decision-support framework to optimize border control for global outbreak mitigation
Zlojutro, A., Rey, D., & Gardner, L. (2019) · Scientific Reports · doi:10.1038/s41598-019-38665-w · Cited by 58 · see citing works
Mapping the transmission risk of Zika virus using machine learning models
Jiang, D., Hao, M., Ding, F., Fu, J., & Li, M. (2018) · Acta Tropica · doi:10.1016/j.actatropica.2018.06.021 · Cited by 56 · see citing works
Ebola Virus Bayesian Machine Learning Models Enable New in Vitro Leads
Anantpadma, M., Lane, T., Zorn, K. M., Lingerfelt, M. A., Clark, A. M., Freundlich, J. S., Davey, R. A., Madrid, P. B., & Ekins, S. (2019) · ACS Omega · doi:10.1021/acsomega.8b02948 · Cited by 54 · see citing works
Xiao, M., Brugler, M. R., Broe, M. B., Gusmão, L. C., Daly, M., & Rodríguez, E. (2019) · Scientific Reports · doi:10.1038/s41598-019-54637-6 · Cited by 53 · see citing works
Gui, X., Wang, Y., Kou, Y., Reynolds, T. L., Chen, Y., Mei, Q., & Zheng, K. (2017) · PubMed Central · Cited by 46 · see citing works
Li, H., & Sun, F. (2018) · Scientific Reports · doi:10.1038/s41598-018-28308-x · Cited by 39 · see citing works
NEURAL MODELS FOR PREDICTING VIRAL VACCINE TARGETS
Zhang, G. L., Khan, A. M., Kellathur, S. N., et al. (2005) · Journal of Bioinformatics and Computational Biology · doi:10.1142/s0219720005001466 · Cited by 38 · see citing works
Main factors influencing recovery in MERS Co-V patients using machine learning
John, M., & Shaiba, H. A. (2019) · Journal of Infection and Public Health · doi:10.1016/j.jiph.2019.03.020 · Cited by 31 · see citing works
Zhang, P., Chen, B., Ma, L., et al. (2015) · Computational Intelligence and Neuroscience · doi:10.1155/2015/531650 · Cited by 26 · see citing works
Predicting the short-term success of human influenza virus variants with machine learning
Hayati, M., Biller, P. D. N., & Colijn, C. (2020) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2020.0319 · Cited by 24 · see citing works
Gambhir, M., Bozio, C. H., O’Hagan, J. J., Uzicanin, A., Johnson, L. E., Biggerstaff, M., & Swerdlow, D. L. (2015) · Clinical Infectious Diseases · doi:10.1093/cid/civ083 · Cited by 19 · see citing works
Risk assessment of Ebola virus disease spreading in Uganda using a two-layer temporal network
Riad, M. H., Sekamatte, M., Ocom, F., Makumbi, I., & Scoglio, C. (2019) · Scientific Reports · doi:10.1038/s41598-019-52501-1 · Cited by 19 · see citing works
Soam, S. S., Bhasker, B., & Mishra, B. N. (2011) · Advances in experimental medicine and biology · doi:10.1007/978-1-4419-7046-6_22 · Cited by 13 · see citing works
Rasmussen, S. A., & Redd, S. C. (2015) · Clinical Infectious Diseases · doi:10.1093/cid/civ090 · Cited by 5 · see citing works
Allen, K. C. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-809318-4.00019-8 · Cited by 4 · see citing works
Analytics, Machine Learning & NLP – use in BioSurveillance and Public Health practice
B, M. B. K., Jalal, A., Vishnuprasad, V., et al. (2015) · Online Journal of Public Health Informatics · doi:10.5210/ojphi.v7i1.5950 · Cited by 2 · see citing works
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- [40]Toninelli D, Toninelli D, Pinter R, Pedraza P de, Pinter R, Pedraza P de. Mobile research methods. Ubiquity press [Internet]. 2015 Sep 25 [cited 2020 Mar 29]; Available from: https://www.ubiquitypress.com/site/chapters/10.5334/bar.c/ · DOI
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Nguyen, D. C., Ding, M., Pathirana, P. N., & Seneviratne, A. (2021) · IEEE Access · doi:10.1109/access.2021.3093633 · Cited by 286 · see citing works
Domain Shifts in Machine Learning Based Covid-19 Diagnosis From Blood Tests
Roland, T., Böck, C., Tschoellitsch, T., Maletzky, A., Hochreiter, S., Meier, J., & Klambauer, G. (2022) · Journal of Medical Systems · doi:10.1007/s10916-022-01807-1 · Cited by 42 · see citing works
- Authors
- Bansal, A., Padappayil, R. P., Garg, C., Singal, A., Gupta, M., & Klein, A.
- Affiliations
- Cleveland Clinic (US)
- Brigham and Women's Hospital (US)
- Columbia University (US)
- All India Institute of Medical Sciences (IN)
- Corresponding author
- Bansal, Agam
- Padappayil, Rana Prathap
- Garg, Chandan
- Singal, Anjali
- Gupta, Mohak
- Klein, Allan
- Venue
- Journal of Medical Systems
- Published
- 2020-08-01
- Volume
- 44
- Issue
- 9
- Pages
- 156-156
- ISSN
- 0148-5598 (ISSN-L)
- 1573-689X
- PMC
- PMC7395799
- OpenAlex
- W3046413668
- Cited by
- 87
- References
- 59
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 11 · 2023: 19 · 2022: 23 · 2021: 28 · 2020: 3
- FWCI
- 2.67
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of Medical Systems (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- artificial intelligence, machine learning, COVID-19 pandemic, infectious disease forecasting, disease detection, disease management, pattern recognition, large data analysis
- MeSH terms
- Machine Learning, Betacoronavirus, COVID-19, SARS-CoV-2, Algorithms, Artificial Intelligence, Humans, Pneumonia, Viral, Coronavirus Infections, Pandemics
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