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To mitigate the impact of the pandemic, several measures include lockdowns,rapid vaccination programs, school closures, and economic stimulus. Theseinterventions can have positive or unintended negative consequences. Currentresearch to model and determine an optimal intervention automatically throughround-tripping is limited by the simulation objectives, scale (a few thousandindividuals), model types that are not suited for intervention studies, and thenumber of intervention strategies they can explore (discrete vs continuous). Weaddress these challenges using a Deep Deterministic Policy Gradient (DDPG)based policy optimization framework on a large-scale (100,000 individual)epidemiological agent-based simulation where we perform multi-objectiveoptimization. We determine the optimal policy for lockdown and vaccination in aminimalist age-stratified multi-vaccine scenario with a basic simulation foreconomic activity. With no lockdown and vaccination (mid-age and elderly),results show optimal economy (individuals below the poverty line) with balancedhealth objectives (infection, and hospitalization). An in-depth simulation isneeded to further validate our results and open-source our framework.

Epidemic Control on a Large-Scale-Agent-Based Epidemiology Model using Deep Deterministic Policy Gradient