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RESEARCH PRODUCT
Prospective analysis of infectious disease surveillance data using syndromic information.
Ana Corberán-valletAndrew B. Lawsonsubject
Statistics and ProbabilityEpidemiologySouth CarolinaBayesian probabilityDiseasecomputer.software_genreCommunicable Diseasessymbols.namesakeProspective analysisHealth Information ManagementMedicineHumansPoisson regressionProspective StudiesBronchitisbusiness.industryNoveltyOutbreakBayes TheoremModels TheoreticalInfectious disease (medical specialty)Population SurveillancesymbolsTargeted surveillanceData miningbusinesscomputerdescription
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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2014-03-25 | Statistical methods in medical research |