Modelling behavioural interactions in infection disclosure during an outbreak: An evolutionary game theory approach

In confronting the critical challenge of disease outbreak management, health authorities consistently encourage individuals to voluntarily disclose a potential exposure to infection and adhere to self-quarantine protocols by assuring medical care (hospital beds, oxygen, and constant health monitoring) and helplines for severe patients. These have been observed during pandemics; for example, COVID-19 phases in many middle-income countries, such as India, promoted quarantine and reduced stigma. Here, we present a game-theoretic model to elucidate the behavioural interactions in infection disclosure during an outbreak. By employing a fractional derivative approach to model disease propagation, we determine the minimum level of voluntary disclosure required to disrupt the chain of transmission and allow the epidemic to fade. Our findings suggest that higher transmission rates and an increased perceived severity of infection may change the externality of the disclosing strategy, leading to an increase in the proportion of individuals who choose disclosure, and ultimately reducing disease incidence. We estimate the behavioural parameters and transmission rates by fitting the model to COVID-19 hospitalized cases in Chile, South America. The results from our paper underscore the potential for promoting the voluntary disclosure of infection during emerging outbreaks through effective risk communication, thereby emphasizing the severity of the disease and providing accurate information to the public about capacities within hospitals and medical care facilities.

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Publication Details

Journal
Mathematical Biosciences & Engineering
Published
2025-01-01
DOI
https://doi.org/10.3934/mbe.2025070
Citations
1
Primary Topic
COVID-19 epidemiological studies
Type
article
Field-Weighted Citation Impact
1.37
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article

Modelling behavioural interactions in infection disclosure during an outbreak: An evolutionary game theory approach

Samit Bhattacharyya, Viney Kumar, Pranav Verma
1 citations
Mathematical Biosciences & Engineering
COVID-19 epidemiological studies
1.37
article

Modelling behavioural interactions in infection disclosure during an outbreak: An evolutionary game theory approach

Samit Bhattacharyya, Viney Kumar, Pranav Verma
article en
1 citations

Abstract

In confronting the critical challenge of disease outbreak management, health authorities consistently encourage individuals to voluntarily disclose a potential exposure to infection and adhere to self-quarantine protocols by assuring medical care (hospital beds, oxygen, and constant health monitoring) and helplines for severe patients. These have been observed during pandemics; for example, COVID-19 phases in many middle-income countries, such as India, promoted quarantine and reduced stigma. Here, we present a game-theoretic model to elucidate the behavioural interactions in infection disclosure during an outbreak. By employing a fractional derivative approach to model disease propagation, we determine the minimum level of voluntary disclosure required to disrupt the chain of transmission and allow the epidemic to fade. Our findings suggest that higher transmission rates and an increased perceived severity of infection may change the externality of the disclosing strategy, leading to an increase in the proportion of individuals who choose disclosure, and ultimately reducing disease incidence. We estimate the behavioural parameters and transmission rates by fitting the model to COVID-19 hospitalized cases in Chile, South America. The results from our paper underscore the potential for promoting the voluntary disclosure of infection during emerging outbreaks through effective risk communication, thereby emphasizing the severity of the disease and providing accurate information to the public about capacities within hospitals and medical care facilities.

Mathematical Biosciences & EngineeringVol. 22(8)
Shiv Nadar University (IN)
Openalex Percentile: Top 20%
COVID-19 epidemiological studies
1.37
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