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Epidemiologically and Socio-economically Optimal Policies via Bayesian
Optimization
#MMPMID38624421
Chandak A
; Dey D
; Mukhoty B
; Kar P
Trans Indian Natl Acad Eng
2020[]; 5
(2
): 117-127
PMID38624421
show ga
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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free
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