A Bayesian Analysis of Small-Area Infectious Disease Surveillance Data Using Syndromic Information

• Activity Number: 290
• Type: Topic Contributed
• Date/Time: Tuesday, August 6, 2013 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Ana Corberan-Vallet*+ and Andrew B. Lawson
• Companies: University of Valencia and Medical University of South Carolina
• Keywords: Infectious disease data ; Prospective analysis ; Bayesian hierarchical model ; Syndromic information

Abstract

Infectious disease data are routinely collected and analyzed to prevent, detect, and manage infectious disease outbreaks, which pose serious threats to human health. In this work, we describe a Bayesian hierarchical Poisson model to prospectively analyze infectious disease data. In order to closely reproduce disease dynamics, the parameters describing the spread of epidemics are allowed to vary in both space and time. We also show how syndromic information can be incorporated into the model to provide more accurate real-time forecasts. These forecasts play a decisive role in identifying high-risk areas for outbreaks.