Bagged filters for inference on metapopulation dynamics, with epidemiological applications

• Activity Number: 12 - High-Dimensional Parameter Learning on Spatio-Temporal Hidden Markov Models and Its Applications in Epidemiology
• Type: Invited
• Date/Time: Sunday, August 8, 2021 : 1:30 PM to 3:20 PM
• Sponsor: IMS
• Author(s): Edward L Ionides*
• Companies: University of Michigan, Ann Arbor

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 multiple spatially coupled populations, particle filter methods rapidly degenerate. We show that a collection of independent Monte Carlo calculations 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 bootstrap replicates, and they are aggregated into a bagged filter. We demonstrate this methodology and some related algorithms on a model for measles transmission within and between cities.