Contact networks on which epidemic spreading occurs vary over time. Epidemic processes on such temporal networks are complicated by complexity of both network structure and temporal dimensions. We discuss two mathematical modeling topics on "temporal network epidemiology". First, we analyze how concurrency, i.e., the number of partnerships that an individual (i.e., node of the network) simultaneously owns affects the epidemic threshold. We particularly use a temporal network model with which we can vary the degree of concurrency while preserving the structure of the aggregate, static network. Second, we analyze the epidemic threshold and dynamics when each node switches between a high-activity state and a low-activity state in a Markovian manner. This assumption facilitates theoretical analyses and also allows us to produce distributions of inter-event times resembling heavy-tailed distributions, which are prevalent in empirical data. We argue that it is not the tail of the distribution but the small values of inter-event time that impact epidemic dynamics.

Zoom: https://upenn.zoom.us/j/96846928909?pwd=Q3JPTTc5dURmQk5xL01OMjZUc2FXUT09...

### MathBio Seminar

Tuesday, March 2, 2021 - 4:00pm

#### Naoki Masuda

SUNY Buffalo