Nonlinear effect of sentiments and opinion sharing on vaccination decision in face of an outbreak: A multiplex network approach

Abstract

The formation and sharing of opinions greatly influenced recent COVID vaccinations. There were many vaccine-related discussions on various social media platforms, including Twitter, Facebook, and Instagram, which affected immunization uptake. Individuals’ attitudes towards vaccinations influenced how disease spread and epidemics evolved, as communities may hold different views on the risks and benefits of vaccines. We explore this complex interaction and feedback between opinions and vaccination by constructing a vaccination behavior model on a two-layer multiplex network, where the upper layer describes sharing opinions about vaccines and the other layer follows a SIR framework to track disease spread and its dynamics. Coupled opinion sharing of vaccine and disease transmission yields synergistic interactions with each other. We deployed a Microscopic Markov Chain approach to compute the epidemic threshold and showed that it is much higher compared to a case when there is no opinion sharing or formation. We show that opinion sharing has a significant impact on vaccine coverage and disease prevalence, but that depends on the efficacy of vaccination. Furthermore, negative and positive opinions about vaccination differ in weight, which affects their spread. Network topology also plays a significant role in escalating infections in the network population. Our model demonstrates that heterogeneity in population awareness plays a critical role in vaccine uptake and disease propagation. This multiplex disease-opinion model also exhibits a similar qualitative description of the data as the empirical vaccine uptake and tweet dynamics under specific parameter regimes and settings. Our investigation is helpful for public health policymakers in designing vaccination strategies in a heterogeneous population, as opinion sharing is a very common feature in the current era of technological evolution.