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

Anil Babu Payedimarri, Diego Concina, Luigi Portinale, Massimo Canonico, Deborah Seys, Kris Vanhaecht and Massimiliano Panella
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Anil Babu Payedimarri: Department of Translational Medicine (DIMET), Università del Piemonte Orientale, 28100 Novara, Italy
Diego Concina: Department of Translational Medicine (DIMET), Università del Piemonte Orientale, 28100 Novara, Italy
Luigi Portinale: Department of Science and Technological Innovation (DISIT) Università del Piemonte Orientale, 15121 Alessandria, Italy
Massimo Canonico: Department of Science and Technological Innovation (DISIT) Università del Piemonte Orientale, 15121 Alessandria, Italy
Deborah Seys: Leuven Institute for Healthcare Policy, Department of Public Health and Primary Care, KU Leuven, 3000 Leuven, Belgium
Kris Vanhaecht: Leuven Institute for Healthcare Policy, Department of Public Health and Primary Care, KU Leuven, 3000 Leuven, Belgium
Massimiliano Panella: Department of Translational Medicine (DIMET), Università del Piemonte Orientale, 28100 Novara, Italy

IJERPH, 2021, vol. 18, issue 9, 1-11

Abstract: 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.

Keywords: artificial intelligence; machine learning; COVID-19; public health interventions; prediction models; epidemic; pandemic; severe acute respiratory syndrome coronavirus-2 (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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