Epidemiologically and Socio-economically Optimal Policies via Bayesian Optimization
This addresses the challenge of designing effective non-pharmaceutical interventions for policymakers, though it is incremental as it applies existing optimization techniques to a specific domain.
The paper tackles the problem of balancing public health and socio-economic costs during pandemics by developing ESOP, a method using Bayesian optimization to generate optimal lock-down schedules, demonstrated with case studies on a proposed stochastic agent-based simulator.
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.