Mass public quarantining, colloquially known as a lock-down, is a non-pharmaceutical intervention to check spread of disease and pandemics such as the ongoing COVID-19 pandemic. We present 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. The utility of ESOP is demonstrated using case studies with VIPER  , a stochastic agent-based simulator that we also propose . However, ESOP can flexibly interact with arbitrary epidemiological simulators  and produce schedules that involve multiple phases of lock-downs.
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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and pandemics such as the ongoing COVID-19 pandemic. We present ESOP
. This paper presents ESOP (Epidemiologically and Socio-economically Optimal Policies)
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(Virus-Individual-Policy-EnviRonment)
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we also propose
this paper also proposes
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can flexibly
is flexible enough to
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in a black-box manner,