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. 2021 Apr 23;18(9):4499.
doi: 10.3390/ijerph18094499.

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

Anil Babu Payedimarri  1 , Diego Concina  1 , Luigi Portinale  2 , Massimo Canonico  2 , Deborah Seys  3 , Kris Vanhaecht  3   4 , Massimiliano Panella  1
Affiliations

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

Anil Babu Payedimarri et al. Int J Environ Res Public Health. .

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: COVID-19; artificial intelligence; epidemic; machine learning; pandemic; prediction models; public health interventions; severe acute respiratory syndrome coronavirus-2.

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Conflict of interest statement

The authors declare no conflict of interest.

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PRISMA Flow Diagram for the selection of articles.
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