Speaker: Claus Kadelka, Iowa State University (Mathematics)
Abstract: Contact networks are heterogeneous. People with similar characteristics are more likely to interact, a phenomenon called assortative mixing or homophily. Empirical age-stratified social contact matrices have been derived by extensive survey work and used in many recent COVID-19 models. We lack however similar empirical studies that provide social contact matrices for a population stratified by attributes beyond age, such as gender, sexual orientation, or ethnicity. In this talk, I use ethnic homophily and the problem of identifying optimal strategies to allocate limited COVID-19 vaccines as an example to show that accounting for heterogeneities with respect to attributes beyond age can have a profound effect on model dynamics and predictions. I describe a new method, which uses linear algebra and non-linear optimization, to expand a given contact matrix to populations stratified by binary attributes with a known level of homophily. I conclude by briefly describing more complicated extensions. This new method enables any modeler to account for the presence of homophily with respect to binary attributes in contact patterns, ultimately yielding more accurate predictive models.