Space-Time Models for Aggregated Infectious Disease Data with Different Strains

• Activity Number: 650
• Type: Contributed
• Date/Time: Thursday, August 8, 2013 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Cici Bauer*+
• Companies: Brown
• Keywords: space-time ; Bayesian ; B-spline ; virus strain ; Gaussian Markov Random Field

Abstract

Our motivating example concerns the hand-foot-mouth disease (HFMD) data collected in China between 2009 and 2010. The data we analyze contain total disease counts from the surveillance system, along with a limited amount of strain-specific information gathered on a subset of individuals. We propose a Bayesian hierarchical model that provides a coherent approach to estimating the total number of cases by strain. When data is available for multiple areas and time points, the spatial and temporal variability can be modeled by decomposing the log relative risk of disease into three components: a large-scale temporal trend, a large-scale spatial trend and a spatial-temporal interaction. We fit the model in a Bayesian framework and the structure of the interaction between space and time is imposed through a prior on the coefficients of the basis functions, which are constructed as a tensor product of cubic B-splines. This model is amenable to prediction through the use of Gaussian Markov Random Field (GMRF) space-time priors. This model can also be extended to accommodate multiple virus strains or multiple clinically-diagnosed severity categories.