Data-driven multiscale dynamical framework to control a pandemic evolution with non-pharmaceutical interventions.

ABSTRACT: Before the availability of vaccines, many countries have resorted multiple times to drastic social restrictions to prevent saturation of their health care system, and to regain control over an otherwise exponentially increasing COVID-19 pandemic. With the advent of data-sharing, computational approaches are key to efficiently control a pandemic with non-pharmaceutical interventions (NPIs). Here we develop a data-driven computational framework based on a time discrete and age-stratified compartmental model to control a pandemic evolution inside and outside hospitals in a constantly changing environment with NPIs. Besides the calendrical time, we introduce a second time-scale for the infection history, which allows for non-exponential transition probabilities. We develop inference methods an

SUBMITTER: Reingruber J 

PROVIDER: S-EPMC9844884 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature