Modelling the mitigation of anti-vaccine opinion propagation to suppress epidemic spread: A computational approach
Sarah Alahmadi, Rebecca Hoyle, Michael Head, Markus Brede

TL;DR
This paper models how targeted positive vaccine campaigns can counteract anti-vaccine sentiment and reduce epidemic spread using a coupled agent-based network model, comparing static and dynamic strategies.
Contribution
It introduces a novel coupled opinion-disease diffusion model and compares static versus dynamic campaign targeting strategies for mitigating anti-vaccine influence.
Findings
Static campaigns prevent anti-vaccine clustering
Dynamic campaigns reach more individuals and adapt over time
Strategic targeting effectively reduces epidemic size
Abstract
Information regarding vaccines from sources such as health services, media, and social networks can significantly shape vaccination decisions. In particular, the dissemination of negative information can contribute to vaccine hesitancy, thereby exacerbating infectious disease outbreaks. This study investigates strategies to mitigate anti-vaccine social contagion through effective counter-campaigns that disseminate positive vaccine information and encourage vaccine uptake, aiming to reduce the size of epidemics. In a coupled agent-based model that consists of opinion and disease diffusion processes, we explore and compare different heuristics to design positive campaigns based on the network structure and local presence of negative vaccine attitudes. We examine two campaigning regimes: a static regime with a fixed set of targets, and a dynamic regime in which targets can be updated over…
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Taxonomy
TopicsCOVID-19 epidemiological studies · Misinformation and Its Impacts · Influenza Virus Research Studies
