What If…? Pandemic policy-decision-support to guide a cost-benefit-optimised, country-specific response.

ABSTRACT:

Background

After 18 months of responding to the COVID-19 pandemic, there is still no agreement on the optimal combination of mitigation strategies. The efficacy and collateral damage of pandemic policies are dependent on constantly evolving viral epidemiology as well as the volatile distribution of socioeconomic and cultural factors. This study proposes a data-driven approach to quantify the efficacy of the type, duration, and stringency of COVID-19 mitigation policies in terms of transmission control and economic loss, personalised to individual countries.

Methods

We present What If…?, a deep learning pandemic-policy-decision-support algorithm simulating pandemic scenarios to guide and evaluate policy impact in real time. It leverages a uniquely diverse live global data-str

SUBMITTER: Mannarini G 

PROVIDER: S-EPMC10022143 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature