Hierarchical Modeling of Influenza-Epidemic Dynamics in Space and Time

• Activity Number: 208
• Type: Contributed
• Date/Time: Tuesday, August 13, 2002 : 10:30 AM to 12:30 PM
• Sponsor: Section on Statistics & the Environment*
• Author(s): Andrew Mugglin*+ and Noel Cressie and Islay Gemmell
• Affiliation(s): Medtronic, Inc. and Ohio State University and University of Manchester
• Keywords: Bayesian ; autoregressive ; small-area counts ; spatio-temporal ; infectious disease ; Markov chain Monte Carlo

Abstract

An infectious disease typically spreads via contact between infected and susceptible individuals. Since the small-scale movements and contacts between people are generally not recorded, available data regarding infectious disease are often aggregations in space and time, yielding small-area counts of the number infected during successive, regular time intervals. In this talk, we describe a spatially descriptive, temporally dynamic hierarchical model to be fitted to such data. Disease counts are viewed as a realization from an underlying multivariate autoregressive process, where the relative risk of infection incorporates the space-time dynamic. The approach is Bayesian, using Markov chain Monte Carlo to compute posterior estimates of all parameters of interest. We apply the methodology to an influenza epidemic in Scotland during the years 1989-1990.