Prospective analysis of infectious disease surveillance data using syndromic information. (December 2014)

• Record Type: Journal Article
• Authors: Corberán-Vallet, Ana; Lawson, Andrew B
• Other Names: Lawson Andrew B guest-editor.; Ugarte María Dolores guest-editor.; MacNab Ying C guest-editor.; Lee Duncan guest-editor.
• Is Part Of: Statistical methods in medical research. Volume 23:Number 6(2014:Dec.)
• Page Start: 572
• Page End: 590
• Publication Date: 2014-12
• Subjects: Infectious disease -- prospective analysis -- epidemic prediction -- targeted surveillance -- Bayesian hierarchical space–time model -- syndromic information; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/0962280214527385
• Languages: English
• ISSNs: 0962-2802

Abstract

In this paper, we describe a Bayesian hierarchical Poisson model for the prospective analysis of data for infectious diseases. The proposed model consists of two components. The first component describes the behavior of disease during nonepidemic periods and the second component represents the increase in disease counts due to the presence of an epidemic. A novelty of our model formulation is that 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 a better description of the data and more accurate one-step-ahead forecasts. These real-time forecasts can be used to identify high-risk areas for outbreaks and, consequently, to develop efficient targeted surveillance. We apply the methodology to weekly emergency room discharges for acute bronchitis in South Carolina.