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Motivated by massive outbreaks of COVID-19 that occurred even in populationswith high vaccine uptake, we propose a novel multi-population temporal networkmodel for the spread of recurrent epidemic diseases. We study the effect ofhuman behavior, testing, and vaccination campaigns on the control of localoutbreaks and infection prevalence. Our modeling framework decouples thevaccine effectiveness in protecting against transmission and the development ofsevere symptoms. Furthermore, the framework accounts for the polarizing effectof the decision to vaccinate and captures homophily, i.e., the tendency ofpeople to interact with like-minded individuals. By means of a mean-fieldapproach, we analytically derive the epidemic threshold. Our theoreticalresults suggest that, while vaccination campaigns reduce pressure on hospitals,they might facilitate resurgent outbreaks, highlighting the key role thattesting campaigns may have in eradicating the disease. Numerical simulationsare then employed to confirm and extend our theoretical findings to morerealistic scenarios. Our numerical and analytical results agree thatvaccination is not sufficient to achieve full eradication, without employingmassive testing campaigns or relying on the population's responsibility.Furthermore, we show that homophily plays a critical role in the control oflocal outbreaks, highlighting the peril of a polarized network structure.

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