Abstract:

Infectious disease transmission is a nonlinear partially observed stochastic dynamic system with topical interest. For low-dimensional systems, models can be fitted to time series data using Monte Carlo particle filter methods. As dimension increases, for example when analyzing epidemics among a collection of spatially coupled populations, particle filter methods rapidly degenerate. We show that many independent Monte Carlo calculations, each of which does not attempt to solve the filtering problem, can be combined to give a global filtering solution with favorable theoretical scaling properties under a weak coupling condition. The independent Monte Carlo calculations are called islands, and the operation carried out on each island is called adapted simulation, so the complete algorithm is called an adapted simulation island filter. We demonstrate this methodology and some related algorithms on a model for measles transmission within and between cities. The COVID-19 situation will also be discussed.

This seminar will be livestreamed and is accessible at
https://umich.zoom.us/j/252293050
Problems? Email stat-phd-council-seminar+streaming@umich.edu

A virtual reception will follow and is accessible at
https://umich.zoom.us/j/496003846
Note: The virtual reception will begin shortly after the seminar livestream has concluded.