Epidemiologically and Socio-economically Optimal Policies via Bayesian Optimization

• URL: http://arxiv.org/abs/2005.11257v2
• Date: Mon, 15 Jun 2020 03:44:27 GMT
• Title: Epidemiologically and Socio-economically Optimal Policies via Bayesian Optimization
• Authors: Amit Chandak and Debojyoti Dey and Bhaskar Mukhoty and Purushottam Kar
• Abstract summary: Mass public quarantining, colloquially known as a lock-down, is a non-pharmaceutical intervention to check spread of disease. This paper presents ESOP, 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.
• Score: 4.910276496877174
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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