# Epidemiologically and Socio-economically Optimal Policies via Bayesian Optimization

arXiv: https://arxiv.org/abs/2005.11257 · HTML: https://mlsanity.com/paper/2005.11257/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 May 2020.

## Built on by (most cited first, 2 of 2)

- [Black-Box Optimization with Implicit Constraints for Public Policy](https://mlsanity.com/paper/2310.18449/) (2023)
- [Optimizing Interventions for Agent-Based Infectious Disease Simulations](https://mlsanity.com/paper/2604.02016/) (2026)

## Abstract (arXiv)

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.