Human behavior plays a critical role in shaping epidemic trajectories. During health crises, people respond in diverse ways in terms of self-protection and adherence to recommended measures, largely reflecting differences in how individuals assess risk. This behavioral variability induces effective heterogeneity into key epidemic parameters, such as infectivity and susceptibility. We introduce a minimal extension of the susceptible-infected-removed (SIR) model, denoted HeSIR, that captures these effects through a simple bimodal scheme, where individuals may have higher- or lower-transmission-related traits. We derive a closed-form expression for the epidemic threshold in terms of the model parameters, and the network's degree distribution and homophily, defined as the tendency of like-risk individuals to preferentially interact. We identify a resurgence regime just beyond the classical threshold, where the number of infected individuals may initially decline before surging into large-scale transmission. Through simulations on homogeneous and heterogeneous network topologies we corroborate the analytical results and highlight how variations in susceptibility and infectivity influence the epidemic dynamics. We further show that, under suitable assumptions, the HeSIR model maps onto a standard SIR process on an appropriately modified contact network, providing a unified interpretation in terms of structural connectivity. Our findings quantify the effect of heterogeneous behavioral responses, especially in the presence of homophily, and caution against underestimating epidemic potential in fragmented populations, which may undermine timely containment efforts. The results also extend to heterogeneity arising from biological or other nonbehavioral sources.

The COVID-19 pandemic has impacted on every human activity and, because of theurgency of finding the proper responses to such an unprecedented emergency, itgenerated a diffused societal debate. The online version of this discussion was notexempted by the presence of misinformation campaigns, but, differently from whatalready witnessed in other debates, the COVID-19 -intentional or not- flow of falseinformation put at severe risk the public health, possibly reducing the efficacy ofgovernment countermeasures. In this manuscript, we study theeffectiveimpact ofmisinformation in the Italian societal debate on Twitter during the pandemic,focusing on the various discursive communities. In order to extract suchcommunities, we start by focusing on verified users, i.e., accounts whose identity isofficially certified by Twitter. We start by considering each couple of verified users andcount how many unverified ones interacted with both of them via tweets or retweets:if this number is statically significant, i.e. so great that it cannot be explained only bytheir activity on the online social network, we can consider the two verified accountsas similar and put a link connecting them in a monopartite network of verified users.The discursive communities can then be found by running a community detectionalgorithm on this network.We observe that, despite being a mostly scientific subject, the COVID-19 discussionshows a clear division in what results to be different political groups. We filter thenetwork of retweets from random noise and check the presence of messagesdisplaying URLs. By using the well known browser extension NewsGuard, we assessthe trustworthiness of the most recurrent news sites, among those tweeted by thepolitical groups. The impact of low reputable posts reaches the 22.1% in the right andcenter-right wing community and its contribution is even stronger in absolutenumbers, due to the activity of this group: 96% of all non reputable URLs shared bypolitical groups come from this community.