TY - GEN

T1 - The Effect of News Dissemination on Infection Dynamics: An Evolutionary Epidemic Model in a Network Setting

AU - Тайницкий, Владислав Александрович

AU - Губар, Елена Алексеевна

AU - Dahmouni, Ilyass

PY - 2024

Y1 - 2024

N2 - This paper delves into the ramifications of unofficial news dissemination on the spread of pandemics, aiming to gauge its influence on contagion levels, a pivotal factor in pandemic surveillance. To assess and regulate contagion, we’ve adapted a traditional SIR model, incorporating informed citizens exposed to both official and unofficial information streams. Our methodology combines conventional analysis with an evolutionary game framework, wherein the ratio of individuals correctly informed through authentic pandemic news evolves over time. We posit that exposure to distinct news types influences participants’ behavior, thereby impacting contagion rates and the acceptance of official versus unofficial news. Our findings underscore the necessity for swift governmental action against unofficial news upon pandemic declaration to curb infection spikes. This study underscores the imperative for stringent measures to combat unofficial news during initial panic stages, as well as the significance of beliefs and collective coordination post-pandemic control. Our model accounts for virus spread in a scale-free network, where an ongoing evolutionary game unfolds among network neighbours. The model’s control parameter is the flow of pandemic information, and we present the structure of optimal control alongside numerical simulations to illustrate our findings.

AB - This paper delves into the ramifications of unofficial news dissemination on the spread of pandemics, aiming to gauge its influence on contagion levels, a pivotal factor in pandemic surveillance. To assess and regulate contagion, we’ve adapted a traditional SIR model, incorporating informed citizens exposed to both official and unofficial information streams. Our methodology combines conventional analysis with an evolutionary game framework, wherein the ratio of individuals correctly informed through authentic pandemic news evolves over time. We posit that exposure to distinct news types influences participants’ behavior, thereby impacting contagion rates and the acceptance of official versus unofficial news. Our findings underscore the necessity for swift governmental action against unofficial news upon pandemic declaration to curb infection spikes. This study underscores the imperative for stringent measures to combat unofficial news during initial panic stages, as well as the significance of beliefs and collective coordination post-pandemic control. Our model accounts for virus spread in a scale-free network, where an ongoing evolutionary game unfolds among network neighbours. The model’s control parameter is the flow of pandemic information, and we present the structure of optimal control alongside numerical simulations to illustrate our findings.

KW - Control system analysis

KW - Effective control

KW - Epidemic process

KW - Misinformation spreading

KW - News dissemination

KW - SIR model

UR - https://www.mendeley.com/catalogue/409bafeb-21b6-3fb3-83f6-e01b8499602b/

U2 - 10.1007/978-3-031-72260-8_1

DO - 10.1007/978-3-031-72260-8_1

M3 - Conference contribution

SN - 978-3-031-72259-2

T3 - Communications in Computer and Information Science

SP - 3

EP - 16

BT - Modeling and Simulation of Social-Behavioral Phenomena in Creative Societies

PB - Springer Nature

T2 - Modeling and Simulation of Social-Behavioral Phenomena in Creative Societies. MSBC 2024.

Y2 - 17 September 2024 through 19 September 2024

ER -