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.
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-stream of socioeconomic, demographic, climatic, and epidemic trends on over a year of data (04/2020—06/2021) from 116 countries. The economic damage of the policies is also evaluated on the 29 countries for which data is available. The efficacy and economic damage estimates are derived from two hybrid (recurrent + feed forward) neural networks that infer respectively the daily R-value and unemployment rate.
Reinforcement learning then pits these models against each other to find the optimal policies minimising both R-value and unemployment rate.