Predicting the Risk of Novel Pathogen Introductions from Disease Surveillance Data
Abstract
In the course of an infectious disease outbreak, researchers often must estimate or infer the source of the causative pathogen, the risk factors associated the spread and growth of the pathogen, and risk factors that may be associated with new outbreaks. Because the exact time and location of introduction for the pathogen is usually unobserved, these questions must be addressed using incomplete or indirect data, such as spatio-temporal disease surveillance data. We introduce a Bayesian hierarchical mixture model for spatio-temporal, binary disease surveillance data that accounts for the dynamic process of the pathogen diffusing and multiplying through a population from multiple sources. Our framework provides approximate posterior estimates for the number, locations, and times of introduction of the pathogen in a population, as well posterior inference on parameters associated with pathogen growth and diffusion. We also obtain posterior inference on the generative spatial process that produced the pathogen introductions. We demonstrate this framework using disease surveillance data for chronic wasting disease in white tailed deer from Wisconsin and Illinois in the United States.
Document Details
- Document Type
- Technical Report
- Publication Date
- Sep 21, 2022
- Accession Number
- AD1183281
Entities
People
- Daniel Skinner
- Daniel Storm
- Daniel Walsh
- Ian Mcgahan
- Nelson Walker
- Trevor Hefley
Organizations
- Illinois Department of Natural Resources
- Kansas State University
- Natural Resources Canada