ABSTRACT
Rapid development and distribution of vaccines have been a hallmark of the battle against COVID-19. While the efficacy, clinical trials, adverse health effects, and sociodemographic and clinical factors determining the distribution of vaccines have been studied extensively, there has been little effort to design cost-effective vaccine provisioning schemes. We introduce a vaccine provisioning scheme that leverages coalitional game theory to improve the cost of vaccines while meeting the epidemiological demand of neighboring zones. The proposed approach incentivizes bulk purchases by groups (or coalitions) of zones at lower prices while penalizing large coalitions to avoid logistical challenges. Moreover, it enables the policymaker to model the vaccine demand of zones based on their epidemiological profiles, such as susceptible, infected numbers or population density, or a combination thereof. We carry out experiments using the SEIRD (susceptible, exposed, infected, recovered, death) epidemic model as well as the daily confirmed cases in the five boroughs of New York City to show the efficacy of the approach. © 2022 IEEE.
ABSTRACT
Rapid development and distribution of vaccines have been a hallmark of the battle against COVID-19. While the efficacy, clinical trials, adverse health effects, and sociodemographic and clinical factors determining the distribution of vaccines have been studied extensively, there has been little effort to design cost-effective vaccine provisioning schemes. We introduce a vaccine provisioning scheme that leverages coalitional game theory to improve the cost of vaccines while meeting the epidemiological demand of neighboring zones. The proposed approach incentivizes bulk purchases by groups (or coalitions) of zones at lower prices while penalizing large coalitions to avoid logistical challenges. Moreover, it enables the policymaker to model the vaccine demand of zones based on their epidemiological profiles, such as susceptible, infected numbers or population density, or a combination thereof. We carry out experiments using the SEIRD (susceptible, exposed, infected, recovered, death) epidemic model as well as the daily confirmed cases in the five boroughs of New York City to show the efficacy of the approach. © 2022 IEEE.
ABSTRACT
In the absence of effective treatment for COVID-19, disease prevention and control have become a top priority across the world. However, the general lack of effective cooperation between communities makes it difficult to suppress the community spread of the global pandemic; hence repeated outbreaks of COVID-19 have become the norm. To address this problem, this paper considers community cooperation in disease monitoring and designs a joint epidemic monitoring mechanism, in which adjacent communities cooperate to enhance their monitoring capability. In this work, we formulate the epidemiological monitoring process as a coalitional game. Then, we propose a Shapley value-based payoffs distribution scheme for the coalitional game. A comprehensive analytical framework is developed to evaluate the advantages and sustainability of the cooperation between communities. Experimental results show that the proposed mechanism performs much better than the conventional non-cooperative monitoring design and can greatly increase each community's payoffs.