Spline Models for the Analysis of Spatio-Temporal Count Data

• Activity Number: 396
• Type: Invited
• Date/Time: Tuesday, August 6, 2013 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics and the Environment
• Author(s): Jon Wakefield*+ and Cici Bauer
• Companies: University of Washington and Brown
• Keywords: Spatio-Time Modeling ; Gaussian Markov Random Field ; Smoothing Priors ; Infectious Diseases

Abstract

In this talk I will describe a model for the analysis of infectious disease data collected over time and aggregated over a set of areal units. The aim of the analysis of such data is often the prediction of future disease counts in areas over the study region. The model combines a Poisson model with B-splines and Gaussian Markov random field prior distributions to carry out spatio-temporal smoothing. The motivating data consist of counts of hand, foot and mouth disease collected in China over 2009-2010. In addition to the counts, a small number of cases in each area provided strain-specific information. An extension of the model will be described that reconstructs strain information on all cases, again smoothing over time and space.