Epidemiologically and Socio-economically Optimal Policies via Bayesian Optimization

arXiv2005.11257MaRDI QIDQ6341245

Purushottam Kar, Amit Chandak, Bhaskar Mukhoty, Debojyoti Dey

Publication date: 22 May 2020

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.

Has companion code repository: https://github.com/purushottamkar/esop

Mathematics Subject Classification ID

Epidemiology (92D30) Gaussian processes (60G15) Derivative-free methods and methods using generalized derivatives (90C56)

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