TY  - JOUR
PY  - 2020//
TI  - Epidemiologically and socio-economically optimal policies via Bayesian optimization
JO  - Transactions of the Indian National Academy of Engineering
A1  - Chandak, Amit
A1  - Dey, Debojyoti
A1  - Mukhoty, Bhaskar
A1  - Kar, Purushottam
SP  - 117
EP  - 127
VL  - 5
IS  - 2
N2  - 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.<p />  <p>Language: en</p>
LA  - en
SN  - 2662-5415
UR  - http://dx.doi.org/10.1007/s41403-020-00142-6
ID  - ref1
ER  -