Epidemiologically and Socio-economically Optimal Policies via Bayesian Optimization

TL;DR

This paper introduces ESOP, a Bayesian optimization-based method that interacts with epidemiological models to generate optimal lock-down schedules balancing health benefits and socio-economic impacts.

Contribution

It presents a novel active machine learning approach, ESOP, capable of optimizing lock-down policies using epidemiological simulations, including a new stochastic agent-based model VIPER.

Findings

ESOP effectively balances public health and socio-economic factors.

Demonstrated with case studies using the VIPER simulator.

Flexible interaction with various epidemiological models.

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.