What If...? Pandemic Policy-Decision-Support to Guide a Cost-Benefit-Optimised, Country-Specific Response

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-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 (RE) and unemployment rate (UER). Reinforcement learning then pits these models against each other to find the optimal policies minimising both RE and UER.Findings: The models made high accuracy predictions of RE (average mean squared error [aMSE] 0·043 with a 95% confidence interval [CI95] of [0·042, 0·044] over 116 countries) and UER (aMSE 4·473 CI95 [2·619, 6·326]% over 29 countries). Comparing policy strategies of our reinforcement learning agent to those that were truly implemented, the agent outperformed policymakers across all 29 countries over a range of randomly selected time periods (predicted average RE reduction of 0·250 (versus 0·025) and 1·595% (versus 0·057%) for UER). Interpretation: These results show that deep learning has the potential to guide evidence-based understanding and implementation of public health policies. Funding Information: Unfunded.Declaration of Interests: The authors have no conflicts of interest to declare.