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

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.

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Publication Details

Journal
PLoS ONE
Published
2025-05-20
DOI
https://doi.org/10.1371/journal.pone.0320904
Primary Topic
COVID-19 epidemiological studies
Type
article
Field-Weighted Citation Impact
0.00

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article

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

Xinghua Chang, Rong Zeng, Jianrong Wang
PLoS ONE
COVID-19 epidemiological studies
article

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

Xinghua Chang, Rong Zeng, Jianrong Wang
article en

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.

PLoS ONEVol. 20(5)
Shanxi University (CN)
China Postdoctoral Science Foundation
Peace, Justice and strong institutions
Openalex Percentile: Top 87%
COVID-19 epidemiological studies
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