2023/04/10 by Gaurav Deshkar, Deshkar, Gaurav, Jayanta Kshirsagar +5
Decision Sciences · Mathematics · #Artificial Intelligence (cs.AI) #COVID-19 epidemiological studies #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Systems and Control (eess.SY) #demographic modeling and climate adaptation #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2304.04475
openalex publication_date 2023/04/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
To mitigate the impact of the pandemic, several measures include lockdowns, rapid vaccination programs, school closures, and economic stimulus. These interventions can have positive or unintended negative consequences. Current research to model and determine an optimal intervention automatically through round-tripping is limited by the simulation objectives, scale (a few thousand individuals), model types that are not suited for intervention studies, and the number of intervention strategies they can explore (discrete vs continuous). We address 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-objective optimization. We determine the optimal policy for lockdown and vaccination in a minimalist age-stratified multi-vaccine scenario with a basic simulation for economic activity. With no lockdown and vaccination (mid-age and elderly), results show optimal economy (individuals below the poverty line) with balanced health objectives (infection, and hospitalization). An in-depth simulation is needed to further validate our results and open-source our framework.