title: Prospective analysis of infectious disease surveillance data using syndromic information.
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.
authors.affiliation: Department of Statistics and Operations Research, University of Valencia, Burjassot, Spain ana.corberan@uv.es.
authors.forename: Ana
authors.initials: A
authors.lastname: Corberán-Vallet
authors.affiliation: Department of Public Health Sciences, Medical University of South Carolina, Charleston, U.S.A.
authors.forename: Andrew B
authors.initials: AB
authors.lastname: Lawson
mesh_terms: D001499:Bayes Theorem; D001991:Bronchitis / Q000453:epidemiology; D003141:Communicable Diseases / Q000453:epidemiology*; D006801:Humans; D008962:Models, Theoretical; D011159:Population Surveillance; D011446:Prospective Studies; D013022:South Carolina / Q000453:epidemiology
keywords: Bayesian hierarchical space–time model; Infectious disease; epidemic prediction; prospective analysis; syndromic information; targeted surveillance