Predicting Infectious Disease Outbreak Carried by Migratory Waterfowl

• Activity Number: 396
• Type: Topic Contributed
• Date/Time: Tuesday, August 2, 2011 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics and the Environment
• Author(s): Jacob J. Oleson*+ and Christopher K. Wikle
• Companies: University of Iowa and University of Missouri
• Keywords: Bayesian ; Functional ; Hierarchical ; Markov chain Monte Carlo ; Risk ; Spatio-temporal

Abstract

Given the uncertainties associated with vector-born infectious diseases, it is critical to develop statistical models to address how and when an infectious disease could spread throughout a region such as the United States. Modeling spatio-temporal data of this type is inherently difficult given the uncertainty associated with observations, complexity of the dynamics, high dimensionality of the underlying process, and the presence of excessive zeros. The spatio-temporal dynamics of a waterfowl migration are developed by way of a novel two-tiered functional temporal and spatial dimension reduction procedure that captures spatial and seasonal trends, as well as regional dynamics. Furthermore, the model relates the migration to a population of poultry farms that are known to be susceptible to such diseases, and is one of the possible avenues towards transmission to domestic poultry and humans. The result is a predictive distribution of those counties containing poultry farms that are at the greatest risk of having the infectious disease infiltrate their flocks assuming that the migratory population was infected. The model fits into the hierarchical Bayesian framework.