Predicting influenza hospitalizations

dc.contributor.advisorMeyers, Lauren Ancel
dc.contributor.advisorDamien, Paul, 1960-
dc.creatorRamakrishnan, Anurekhaen
dc.date.accessioned2014-10-15T14:38:03Zen
dc.date.issued2012-08en
dc.date.submittedAugust 2012en
dc.date.updated2014-10-15T14:38:03Zen
dc.descriptiontexten
dc.description.abstractSeasonal influenza epidemics are a major public health concern, causing three to five million cases of severe illness and about 250,000 to 500,000 deaths worldwide. Given the unpredictability of these epidemics, hospitals and health authorities are often left unprepared to handle the sudden surge in demand. Hence early detection of disease activity is fundamental to reduce the burden on the healthcare system, to provide the most effective care for infected patients and to optimize the timing of control efforts. Early detection requires reliable forecasting methods that make efficient use of surveillance data. We developed a dynamic Bayesian estimator to predict weekly hospitalizations due to influenza related illnesses in the state of Texas. The prediction of peak hospitalizations using our model is accurate both in terms of number of hospitalizations and the time at which the peak occurs. For 1-to 8 week predictions, the predicted number of hospitalizations was within 8% of actual value and the predicted time of occurrence was within a week of actual peak.en
dc.description.departmentStatisticsen
dc.format.mimetypeapplication/pdfen
dc.identifier.urihttp://hdl.handle.net/2152/26598en
dc.subjectBayesian estimationen
dc.subjectFlu forecastingen
dc.subjectInfluenza hospitalization predictionen
dc.subjectKalman filteren
dc.titlePredicting influenza hospitalizationsen
dc.typeThesisen
thesis.degree.departmentStatisticsen
thesis.degree.disciplineStatisticsen
thesis.degree.grantorThe University of Texas at Austinen
thesis.degree.levelMastersen
thesis.degree.nameMaster of Science in Statisticsen

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