Pandemic lockdown, isolation, and exit policies based on machine learning predictions.

ABSTRACT: The widespread lockdowns imposed in many countries at the beginning of the COVID-19 pandemic elevated the importance of research on pandemic management when medical solutions such as vaccines are unavailable. We present a framework that combines a standard epidemiological SEIR (susceptible-exposed-infected-removed) model with an equally standard machine learning classification model for clinical severity risk, defined as an individual's risk of needing intensive care unit (ICU) treatment if infected. Using COVID-19-related data and estimates for France as of spring 2020, we then simulate isolation and exit policies. Our simulations show that policies considering clinical risk predictions could relax isolation restrictions for millions of the lowest risk population months earlier while cons

SUBMITTER: Evgeniou T 

PROVIDER: S-EPMC9115407 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature