Gravity Models for Studying the Spatiotemporal Dynamics of an Infectious Disease

• Activity Number: 520
• Type: Contributed
• Date/Time: Wednesday, August 4, 2010 : 10:30 AM to 12:20 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Murali Haran*+ and Roman Jandarov and Ottar N. Bjornstad
• Companies: Penn State and Penn State and Penn State
• Keywords: gravity models ; infectious disease dynamics ; Markov chain Monte Carlo ; spatiotemporal models ; measles

Abstract

Extremely contagious, acute, immunizing childhood infections like measles can exhibit spatiotemporal dynamics that depend on the nature of spatial contagion and spatiotemporal variations in population structure and demography. We study a metapopulation model for regional measles dynamics that uses a gravity coupling model and a time series susceptible-infected-recovered (TSIR) model for local dynamics. Standard maximum likelihood or Bayesian inference for this model is challenging as there are potentially tens of thousands of latent variables in the model and each evaluation of the likelihood is expensive. We develop computational approaches that overcome these challenges. Using our approach, we analyze the historical records of measles outbreaks in England and Wales between 1944 and 1965.