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Title: Demography-aware COVID-19 Confinement with Game Theory
In the last decades, emerging and re-emerging epidemics such as AIDS, measles, SARS, HINI influenza, and tuberculosis cause death to millions of people each year. In response, a large and intensive research is evolving for the design of better drugs and vaccines. However, studies warn that the new pandemics such as Coronavirus (COVID-19) and even deadly pandemics can emerge in the future. The existing confinement approaches rely on large amount of available data to determine policies. Such dependencies could cause an irreversible effect before proper strategies are developed. Furthermore, the existing approaches follow a one-size fits all approach, which might not be effective. In contrast, we develop a game-theory inspired approach that considers societal and economic impacts and formulates the epidemic control as a non-zero sum dynamic game. Further, the proposed approach considers the demographic information leading to providing a tailored solution to each demography. We explore different strategies including masking, social distancing, contact tracing, quarantining, partial-, and full-lockdowns and their combinations and present demography-aware optimal solutions to confine a pandemic with minimal history information and optimal impact on economy.
Authors:
; ; ; ;
Award ID(s):
2029291
Publication Date:
NSF-PAR ID:
10337257
Journal Name:
IEEE 3rd International Conference on Artificial Intelligence Circuits and Systems (AICAS)
Page Range or eLocation-ID:
1 to 4
Sponsoring Org:
National Science Foundation
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