Modelling the mitigation of anti-vaccine opinion propagation to suppress epidemic spread: A computational approach

TL;DR

It is demonstrated that strategic targeting and engagement with the dynamics of anti-vaccine influence diffusion in the network can effectively mitigate the spread of anti-vaccine sentiment, thereby reducing the 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 time. We demonstrate that strategic targeting and engagement with the dynamics of anti-vaccine influence diffusion in the network can effectively mitigate the spread of anti-vaccine sentiment, thereby reducing the epidemic size. However, the effectiveness of the campaigns differs across different targeting strategies and is impacted by a range of factors. We find that the primary advantage of static campaigns lies in their capacity to act as an obstacle, preventing the clustering of emerging anti-vaccine communities, thereby resulting in smaller and unconnected anti-vaccine groups. On the other hand, dynamic campaigns reach a broader segment of the population and adapt to the evolution of anti-vaccine diffusion, not only protecting susceptible agents from negative influence but also fostering positive propagation within negative regions.

Figures (10)

• Figure 1: Illustration of the model describing opinion formation and disease propagation. Blue circles represent neutral individuals, green circles represent pro-vaccine individuals, and red circles represent anti-vaccine individuals. The first stage involves the generation of the social network and the initialization of agent opinion states as agents with neutral opinions. Then, external exposures to positive and negative information triggers the initial seed sets for both anti-vaccine and pro-vaccine contagion. Opinion diffusion continues until a stopping criterion is reached. In this stage, a vaccination takes place for all non-negative individuals. Subsequently, a randomly chosen non-vaccinated individual is infected, and the spread of the disease continues until no further newly infected agents are generated. Finally, we record the number of recovered agents to measure the epidemic size.
• Figure 2: Illustration of opinion propagation and campaigning methods. The figure shows the exchange of vaccine-related opinions and external exposures, as well as the positive campaign types. (A) Random dissemination of negative and positive vaccine-related sentiments from external campaigns to the public. (B) Targeted positive campaign. $\mu^-$, and $\mu^+$ are the general exposure rates for negative and positive sentiments, respectively. $\omega^-$, $\omega^+$ are the social exposure rates for negative and positive opinions, respectively.
• Figure 3: Average epidemic size for the random campaign as a function of the social rate $\omega=\omega^+=\omega^-$. (A) $\tau=\infty$ (B) $\tau=400$. The figure shows results for different positive exposure rates $\mu^+$ with fixed negative exposure rate $\mu^-$ = 0.001.
• Figure 4: Average epidemic size for static campaigns as a function of the social rate $\omega=\omega^-=\omega^+$. (A) and (B) for the targeted random campaign. (C) and (D) for the centrality-based campaign. (A) and (C) $\tau=\infty$, (B) and (D) $\tau=400$. The figures show different positive exposure rates $\mu^+$ with fixed negative exposure rate $\mu^-$ = 0.001. Target set size is $T =500$.
• Figure 5: Dependence of the average epidemic size on the campaign updating interval using dynamic campaigns.(A) and (B) represent the dynamic random campaign, and (C) and (D) represent the Locl-Info campaign. The left panels illustrate the long-run scenarios, while the right panels illustrate the short-run scenarios. The targets set $T=50$, social rate is $\omega^+=\omega^-= 0.006$. The figures show different positive exposure rates $\mu^+$ with fixed negative exposure rate $\mu^-=0.001$.