MIXING PATTERNS AMONG EPIDEMIC GROUPS

Epidemic modeling has a long tradition in facilitating an understanding of the spread of infectious agents with direct impact on public health. Recent modeling approaches have specifically improved our understanding of the connections between transmission network topology and epidemic spread: Host populations are generally not well mixed and show heterogeneous and dynamic contact behavior. This results in a broad range of epidemic patterns regarding both speed and width of epidemic expansion. However, there is also a mutual influence between transmission network structure, mixing between susceptible and infected hosts and the epidemic process: epidemic control gets more difficult if infected hosts mix well within the susceptible population. We present a new mathematical framework which allows for a study of mixing patterns between susceptible and infected hosts in populations with very general structural features. While clustering of infected hosts is a qualitatively well known phenomenon, our approach allows for its quantification and puts it in relation to the hosts' overall contact behavior. It shows that different degrees of assortativity emerge depending on the transmission network structure and epidemic stage. This method's use is demonstrated for synthetic populations. It can however easily be transferred to more realistic settings in which information about mixing patterns of epidemic groups is vital for improved epidemic control.Epidemic modeling, SIR, mixing, assortativity, transmission network