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Review

Prediction Models for Public Health Containment Measures on COVID-19 Using Artificial Intelligence and Machine Learning: A Systematic Review

1
Department of Translational Medicine (DIMET), Università del Piemonte Orientale, 28100 Novara, Italy
2
Department of Science and Technological Innovation (DISIT) Università del Piemonte Orientale, 15121 Alessandria, Italy
3
Leuven Institute for Healthcare Policy, Department of Public Health and Primary Care, KU Leuven, 3000 Leuven, Belgium
4
Department of Quality Management, University Hospitals Leuven, University of Leuven, 3000 Leuven, Belgium
*
Author to whom correspondence should be addressed.
Academic Editor: Samina Abidi
Int. J. Environ. Res. Public Health 2021, 18(9), 4499; https://doi.org/10.3390/ijerph18094499
Received: 16 March 2021 / Revised: 21 April 2021 / Accepted: 22 April 2021 / Published: 23 April 2021
Artificial Intelligence (AI) and Machine Learning (ML) have expanded their utilization in different fields of medicine. During the SARS-CoV-2 outbreak, AI and ML were also applied for the evaluation and/or implementation of public health interventions aimed to flatten the epidemiological curve. This systematic review aims to evaluate the effectiveness of the use of AI and ML when applied to public health interventions to contain the spread of SARS-CoV-2. Our findings showed that quarantine should be the best strategy for containing COVID-19. Nationwide lockdown also showed positive impact, whereas social distancing should be considered to be effective only in combination with other interventions including the closure of schools and commercial activities and the limitation of public transportation. Our findings also showed that all the interventions should be initiated early in the pandemic and continued for a sustained period. Despite the study limitation, we concluded that AI and ML could be of help for policy makers to define the strategies for containing the COVID-19 pandemic. View Full-Text
Keywords: artificial intelligence; machine learning; COVID-19; public health interventions; prediction models; epidemic; pandemic; severe acute respiratory syndrome coronavirus-2 artificial intelligence; machine learning; COVID-19; public health interventions; prediction models; epidemic; pandemic; severe acute respiratory syndrome coronavirus-2
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MDPI and ACS Style

Payedimarri, A.B.; Concina, D.; Portinale, L.; Canonico, M.; Seys, D.; Vanhaecht, K.; Panella, M. Prediction Models for Public Health Containment Measures on COVID-19 Using Artificial Intelligence and Machine Learning: A Systematic Review. Int. J. Environ. Res. Public Health 2021, 18, 4499. https://doi.org/10.3390/ijerph18094499

AMA Style

Payedimarri AB, Concina D, Portinale L, Canonico M, Seys D, Vanhaecht K, Panella M. Prediction Models for Public Health Containment Measures on COVID-19 Using Artificial Intelligence and Machine Learning: A Systematic Review. International Journal of Environmental Research and Public Health. 2021; 18(9):4499. https://doi.org/10.3390/ijerph18094499

Chicago/Turabian Style

Payedimarri, Anil B., Diego Concina, Luigi Portinale, Massimo Canonico, Deborah Seys, Kris Vanhaecht, and Massimiliano Panella. 2021. "Prediction Models for Public Health Containment Measures on COVID-19 Using Artificial Intelligence and Machine Learning: A Systematic Review" International Journal of Environmental Research and Public Health 18, no. 9: 4499. https://doi.org/10.3390/ijerph18094499

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