Speaker
Description
Understanding how epidemics spread on contact networks when the data are incomplete remains one of the central challenges in mathematical epidemiology. In this talk, I will describe a framework for analyzing stochastic epidemic models under partial observation, combining ideas from dynamical survival analysis (DSA), pairwise survival models, and recent exact closure results for SIR dynamics on random networks.
The starting point is to use DSA to estimate effective reproduction parameters directly from observed infection and recovery times, without requiring full knowledge of the underlying contact structure. These estimates are then combined with pairwise survival models, following Kenah and collaborators, to account for local dependence and correlations between connected individuals. To connect these approximations to large-network behavior, I will also discuss recent results on exact closure for configuration-model epidemics.
Taken together, these elements provide a tractable and interpretable approach to inference and uncertainty quantification in network-based epidemic models.
