Epidemiologically and Socio-economically Optimal Policies via Bayesian Optimization
Abstract
Mass public quarantining, colloquially known as a lock-down, is a non-pharmaceutical intervention to check spread of disease. This paper presents ESOP (Epidemiologically and Socio-economically Optimal Policies), a novel application of active machine learning techniques using Bayesian optimization, that interacts with an epidemiological model to arrive at lock-down schedules that optimally balance public health benefits and socio-economic downsides of reduced economic activity during lock-down periods. The utility of ESOP is demonstrated using case studies with VIPER (Virus-Individual-Policy-EnviRonment), a stochastic agent-based simulator that this paper also proposes. However, ESOP is flexible enough to interact with arbitrary epidemiological simulators in a black-box manner, and produce schedules that involve multiple phases of lock-downs.
Cite
@article{arxiv.2005.11257,
title = {Epidemiologically and Socio-economically Optimal Policies via Bayesian Optimization},
author = {Amit Chandak and Debojyoti Dey and Bhaskar Mukhoty and Purushottam Kar},
journal= {arXiv preprint arXiv:2005.11257},
year = {2020}
}
Comments
Keywords: COVID-19, Optimal Policy, Lock-down, Epidemiology, Bayesian Optimization Code available at https://github.com/purushottamkar/esop