title: Bayesian prediction of an epidemic curve.
abstract: An epidemic curve is a graph in which the number of new cases of an outbreak disease is plotted against time. Epidemic curves are ordinarily constructed after the disease outbreak is over. However, a good estimate of the epidemic curve early in an outbreak would be invaluable to health care officials. Currently, techniques for predicting the severity of an outbreak are very limited. As far as predicting the number of future cases, ordinarily epidemiologists simply make an educated guess as to how many people might become affected. We develop a model for estimating an epidemic curve early in an outbreak, and we show results of experiments testing its accuracy.
authors.affiliation: Department of Biomedical Informatics, University of Pittsburgh, Parkvale Building, M-183, 200 Meyran Avenue, Pittsburgh, PA 15260, USA. xij6@pitt.edu
authors.forename: Xia
authors.initials: X
authors.lastname: Jiang
authors.forename: Garrick
authors.initials: G
authors.lastname: Wallstrom
authors.forename: Gregory F
authors.initials: GF
authors.lastname: Cooper
authors.forename: Michael M
authors.initials: MM
authors.lastname: Wagner
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D055877:Biosurveillance / Q000379:methods*; D003457:Cryptosporidiosis / Q000188:drug therapy / Q000453:epidemiology; D004196:Disease Outbreaks / Q000706:statistics & numerical data*; D004636:Emergency Service, Hospital / Q000706:statistics & numerical data; D006801:Humans; D007251:Influenza, Human / Q000188:drug therapy / Q000453:epidemiology; D008954:Models, Biological; D015233:Models, Statistical; D004366:Nonprescription Drugs / Q000627:therapeutic use
chemical_list: D004366:Nonprescription Drugs