The role of machine learning in health policies during the COVID-19 pandemic and in long COVID management

Front Public Health. 2023 Apr 11:11:1140353. doi: 10.3389/fpubh.2023.1140353. eCollection 2023.

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.

Keywords: COVID-19; SARS-CoV-2; long COVID; machine learning; mathematical models; public health policies.

Publication types

  • Review
  • Research Support, Non-U.S. Gov't

MeSH terms

  • COVID-19* / epidemiology
  • Health Policy
  • Humans
  • Machine Learning
  • Pandemics
  • Post-Acute COVID-19 Syndrome
  • SARS-CoV-2

Grants and funding

The authors acknowledge funding by the MAG-2095 project, Ministry of Education, Chile. DM-O acknowledges ANID for the project SUBVENCIÓN A INSTALACIÓN EN LA ACADEMIA CONVOCATORIA AÑO 2022, Folio 85220004. MN acknowledges ANID for project ACT210085 and GORE Magallanes for project FIC-R 40036196-0.