A Polarized Temporal Network Model to Study the Spread of Recurrent Epidemic Diseases in a Partially Vaccinated Population

Motivated by massive outbreaks of COVID-19 that occurred even in populations with high vaccine uptake, we propose a novel multi-population temporal network model for the spread of recurrent epidemic diseases. We study the effect of human behavior, testing, and vaccination campaigns on infection prevalence and local outbreak control. Our modeling framework decouples a vaccine's effectiveness in protecting against transmission and severe symptom development. Additionally, it captures the polarizing effect of vaccination decisions and homophily, i.e., people's tendency to interact with like-minded individuals. Through a mean-field approach, we analytically derive the epidemic threshold and, under further assumptions, we compute the endemic equilibrium. Our results show that while vaccination campaigns are highly beneficial in reducing pressure on hospitals, they may also facilitate resurgent outbreaks, particularly in the absence of effective testing campaigns. Subsequently, we employ numerical simulations to confirm and extend our theoretical findings to more realistic scenarios. Our analytical and numerical results demonstrate that vaccination programs are crucial, but as a sole control measure, they are not sufficient to achieve disease eradication without employing massive testing campaigns or relying on the population's responsibility. Furthermore, we show that homophily impedes local outbreak control, highlighting the peril of a polarized network structure.