Evidence map›Paper›PMID 36589785›Full record

ArticleComputer communications2023

A coalitional game-based joint monitoring mechanism for combating COVID-19.

Da-Wen Huang, Bing Liu, Jichao Bi, Jingpei Wang, Mengzhi Wang, Huan Wang

Abstract read
In one paragraph

Article in Computer communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Da-Wen HuangCollege of Computer Science, Sichuan Normal University, Chengdu, 610066, Sichuan, China.
Bing LiuZhejiang Institute of Industry and Information Technology, Hangzhou, Zhejiang, China.
Jichao BiState Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, 310058, Zhejiang, China.
Jingpei WangState Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, 310058, Zhejiang, China.
Mengzhi WangState Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, 310058, Zhejiang, China.
Huan WangGuangdong Institute of Scientific and Technical Information, Guangzhou, Guangdong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Coalitional gameCommunity spreadCOVID-19Epidemic monitoring

Identifiers

PMID36589785
PMCPMC9793961

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.