Bayesian Models for Infectious Disease Surveillance Data

• Activity Number: 268
• Type: Invited
• Date/Time: Tuesday, August 5, 2014 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistics and the Environment
• Author(s): Ana Corberán-Vallet+ and Andrew B. Lawson and Georgiana Onicescu*
• Companies: University of Valencia and Medical University of South Carolina and Medical University of South Carolina
• Keywords: Infectious diseases ; Prospective analysis ; Syndromic information ; Surveillance

Abstract

As infectious diseases pose a growing threat to population health, numerous statistical models have been recently proposed to understand their evolution and prevent, detect and manage infectious disease outbreaks. Here we review some of the commonly used models to retrospectively describe the progression of disease when small-area counts of the number of infected individuals at regular time intervals are available. We present then a model for the prospective analysis of data for infectious diseases and show its multivariate extension to incorporate syndromic information. The proposed model provides accurate forecasts that can be used to develop targeted surveillance. Finally, the counterpart spatio-temporal point process model is developed. This individual level model provides a more accurate description of the spread of epidemics through time and space at the level of individuals in a population.