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This content will become publicly available on June 1, 2023

Title: Scalable and Demography-Agnostic Confinement Strategies for COVID-19 Pandemic with Game Theory and Graph Algorithms
In the past, epidemics such as AIDS, measles, SARS, H1N1 influenza, and tuberculosis caused the death of millions of people around the world. In response, intensive research is evolving to design efficient drugs and vaccines. However, studies warn that new pandemics such as Coronavirus (COVID-19), variants, and even deadly pandemics can emerge in the future. The existing epidemic confinement approaches rely on a 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 control technique, which might not be effective. To overcome this, in this work, we develop a game-theory-inspired approach that considers societal and economic impacts and formulates epidemic control as a non-zero-sum game. Further, the proposed approach considers the demographic information that provides 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 the economy. To facilitate scalability, we propose a novel graph learning approach, which learns from the previously obtained COVID-19 game outputs and mobility rates of one state (region) more » depending on the other to produce an optimal solution. Our optimal solution is strategized to restrict the mobility between states based on the impact they are causing on COVID-19 spread. We aim to control the COVID-19 spread by more than 50% and model a dynamic solution that can be applied to different strains of COVID-19. Real-world demographic conditions specific to each state are created, and an optimal strategic solution is obtained to reduce the infection rate in each state by more than 50%. « less
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767 to 792
Sponsoring Org:
National Science Foundation
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  1. 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.
  2. Abstract This project is funded by the US National Science Foundation (NSF) through their NSF RAPID program under the title “Modeling Corona Spread Using Big Data Analytics.” The project is a joint effort between the Department of Computer & Electrical Engineering and Computer Science at FAU and a research group from LexisNexis Risk Solutions. The novel coronavirus Covid-19 originated in China in early December 2019 and has rapidly spread to many countries around the globe, with the number of confirmed cases increasing every day. Covid-19 is officially a pandemic. It is a novel infection with serious clinical manifestations, including death, and it has reached at least 124 countries and territories. Although the ultimate course and impact of Covid-19 are uncertain, it is not merely possible but likely that the disease will produce enough severe illness to overwhelm the worldwide health care infrastructure. 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