Coupled Dynamics of Behavior and Disease Contagion Among Antagonistic Groups

Abstract

Disease transmission and behavior change are both fundamentally social phenomena. Behavior change can have profound consequences for disease transmission, and epidemic conditions can favor the more rapid adoption of behavioral innovations. We analyze a simple model 3 of coupled behavior-change and infection in a structured population characterized by homophily and outgroup aversion. Outgroup aversion slows the rate of adoption and leads to bifurcation when outgroup aversion exceeds positive ingroup influence. When the rates of adoption differ by 6 group, high outgroup aversion causes the later-adopting group to have a lower equilibrium adoption fraction, even when the behavior is highly desirable. When disease dynamics are coupled to the behavior-adoption model, a wide variety of dynamics are possible. Homophily can either 9 increase or decrease the final size of the epidemic depending on its relative strength in the two groups and R 0 for the infection. When R 0 ≈ 1 and homophily is strong in the second group, it can be protective for this group. However, when R 0 1, we find that strong homophily in 12 the second group, together with outgroup aversion, can lead to a much larger epidemic in that group. Similarly, if the first group is homophilic and the second is not, the second group will have a larger epidemic. Homophily and outgroup aversion can also produce a "second wave" in 15 the first group that follows the peak of the epidemic in the second group. In general, adding outgroup aversion to a population characterized by homophily can result in paradoxical failure to adopt behavior that is protective from infection. These simple models reveal complex dynamics 18 that are suggestive of the processes currently observed under pandemic conditions in culturally and/or politically polarized populations such as the United States. 21for cumulative culture, and particularly to the rapid and flexible adaptability that arisesHébert-Dufresne et al., 2020; Mehta and Rosenberg, 2020). These models typically assume that individuals differ only in behavior and disease status. Thus, the spread of both disease and behavior depend primarily on rates of behavior transmission and dis-51 ease recovery. This is true even of models in which the population is structured on networks. Network structure can change the dynamics of contagion. However, contrary to the assumptions of most models, behavioral distributions on social networks are any-54 thing but random. People assort in highly non-random ways (McPherson et al., 2001) and these non-random associations both drive and are driven by social identity. This suggests that the role of social identity is an important, but under-studied, component 57 of coupled contagion models. Identity exerts a powerful force on the dynamics of behavior (Hogg and Abrams,

Antivax movement and epidemic spreading in the era of social networks: Nonmonotonic effects, bistability and network segregation

“…A recent work considered a coupled behavior-change and infection in a structured population characterized by homophily and outgroup aversion [26]. It was found that homophily can either increase or decrease the final size of the epidemic depending on its relative strength in the two groups.…”

Dynamics of behavior change in the COVID world

“…The result may be opposing value judgments on the same information, and a failure by each group to adopt beneficial behaviors that are associated with another group (Akerlof & Kranton, 2000; Smaldino, Janssen, Hillis, & Bednar, 2017). This has particularly damaging consequences when public health behaviors become politicized (Smaldino & Jones, 2020). For example, wearing a mask in a nearly empty park, or not wearing one in a crowded market, can not only indicate one's perceptions of mask efficacy, but also signal a political affiliation.…”