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Speaker: Claus Kadelka (Iowa State Univ) Title: Effect of clustering and correlation of belief Systems on COVID-19 and other infectious disease outbreaks.

 

Abstract: Contact between people with similar opinions and characteristics occurs at a higher rate than among other people, a phenomenon known as homophily. The presence of clusters of unvaccinated people has been associated with increased incidence of infectious disease outbreaks despite high vaccination coverage. The epidemiological consequences of homophily regarding other beliefs and correlations among belief systems are however poorly understood. Here, we use a simple compartmental network model as well as a more complex, recently developed COVID-19 model to study how homophily and correlation of belief systems in a social interaction network affect the probability of disease outbreak and COVID-19-related mortality. We find that the current social context, characterized by the presence of homophily and correlations between who vaccinates, who engages in risk reduction, and individual risk status, corresponds to a situation with substantially worse disease burden than in the absence of heterogeneities. In the presence of an effective vaccine, relative effects of homophily and correlation of belief systems become stronger. Further, the optimal vaccination strategy depends on the degree of homophily regarding vaccination status as well as the relative level of risk mitigation high- and low-risk individuals practice. The developed methods are broadly applicable to any investigation in which node attributes in a graph might reasonably be expected to cluster or exhibit correlations. Throughout the talk, I will highlight future avenues for research in mathematical epidemiology and network science. (This is joint work with Audrey McCombs)