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A coalitional game-based joint monitoring mechanism for combating COVID-19.
Huang, Da-Wen; Liu, Bing; Bi, Jichao; Wang, Jingpei; Wang, Mengzhi; Wang, Huan.
  • Huang DW; College of Computer Science, Sichuan Normal University, Chengdu, 610066, Sichuan, China.
  • Liu B; Zhejiang Institute of Industry and Information Technology, Hangzhou, Zhejiang, China.
  • Bi J; State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, 310058, Zhejiang, China.
  • Wang J; Zhejiang Institute of Industry and Information Technology, Hangzhou, Zhejiang, China.
  • Wang M; State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, 310058, Zhejiang, China.
  • Wang H; State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, 310058, Zhejiang, China.
Comput Commun ; 199: 168-176, 2023 Feb 01.
Article in English | MEDLINE | ID: covidwho-2165187
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.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Experimental Studies Language: English Journal: Comput Commun Year: 2023 Document Type: Article Affiliation country: J.comcom.2022.12.020

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Experimental Studies Language: English Journal: Comput Commun Year: 2023 Document Type: Article Affiliation country: J.comcom.2022.12.020