Evidence map›Paper›PMID 40392798›Full record

ArticlePloS one2025

Modeling and analysis of infectious diseases based on behavioral game theory on two-layered networks under media coverage.

Jianrong Wang, Rong Zeng, Xinghua Chang

Abstract read
In one paragraph

Article in PloS one, 2025. 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
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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

3 authors.

Jianrong WangSchool of Automation and Software Engineering, Shanxi University, Taiyuan, China.
Rong ZengSchool of Mathematics and Statistics, Shanxi University, Taiyuan, China.
Xinghua ChangDepartment of Mathematics, Taiyuan University, Taiyuan, China.ORCID https://orcid.org/0000-0001-8044-4602

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The spread of infectious diseases poses significant threats to public health, the economy, and society as a whole. Despite governmental control measures over individual behavior, the public might still be influenced by factors such as costs, expected benefits, and the behavior of others, leading to incomplete adherence to disease control measures. Therefore, this paper proposes a behavioral game theory based infectious disease model on two-layer networks. First, considering the dynamic interaction between individual awareness behavior and disease spreading, a two-layer coupled network of individual behavioral awareness and disease spreading is established. Second, game theory is used to describe the impact of relevant factors on individual awareness behavior. The first layer represents the individual protective behavior game layer, while the second layer represents the disease spreading layer. Government intervention in individual behavior is also considered in the two-layer network model, according to the situation of infectious disease spreading, a threshold model is introduced to control individual protective behavior. Finally, MMCA is used to analyze the disease spreading threshold, and the proportion of the final population state and the spreading threshold under different model parameters are analyzed. The results show that by reducing personal protective costs, increasing individual attention to disease information, and enhancing governmental adjustments to disease control measures, the outbreak threshold of disease spreading can be effectively increased.

Indexed as

Communicable DiseasesGame TheoryMass MediaModels, TheoreticalDisease OutbreaksHumans

Identifiers

PMID40392798
PMCPMC12091790

What OpenQuestion holds

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LicenceCC BY
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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.