AUTHOR=Sarmiento Varón Lindybeth , González-Puelma Jorge , Medina-Ortiz David , Aldridge Jacqueline , Alvarez-Saravia Diego , Uribe-Paredes Roberto , Navarrete Marcelo A. TITLE=The role of machine learning in health policies during the COVID-19 pandemic and in long COVID management JOURNAL=Frontiers in Public Health VOLUME=11 YEAR=2023 URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2023.1140353 DOI=10.3389/fpubh.2023.1140353 ISSN=2296-2565 ABSTRACT=

The ongoing COVID-19 pandemic is arguably one of the most challenging health crises in modern times. The development of effective strategies to control the spread of SARS-CoV-2 were major goals for governments and policy makers. Mathematical modeling and machine learning emerged as potent tools to guide and optimize the different control measures. This review briefly summarizes the SARS-CoV-2 pandemic evolution during the first 3 years. It details the main public health challenges focusing on the contribution of mathematical modeling to design and guide government action plans and spread mitigation interventions of SARS-CoV-2. Next describes the application of machine learning methods in a series of study cases, including COVID-19 clinical diagnosis, the analysis of epidemiological variables, and drug discovery by protein engineering techniques. Lastly, it explores the use of machine learning tools for investigating long COVID, by identifying patterns and relationships of symptoms, predicting risk indicators, and enabling early evaluation of COVID-19 sequelae.