Bayesian prediction of an epidemic curve

Xia Jiang; Garrick Wallstrom; Gregory F. Cooper; Michael M. Wagner

doi:10.1016/j.jbi.2008.05.013

Bayesian prediction of an epidemic curve

Xia Jiang, Garrick Wallstrom, Gregory F. Cooper, Michael M. Wagner

Research output: Contribution to journal › Article › peer-review

24 Scopus citations

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.

Original language	English (US)
Pages (from-to)	90-99
Number of pages	10
Journal	Journal of Biomedical Informatics
Volume	42
Issue number	1
DOIs	https://doi.org/10.1016/j.jbi.2008.05.013
State	Published - Feb 2009
Externally published	Yes

Keywords

Bayesian network
Biosurveillance
Disease surveillance
Epidemic curve

ASJC Scopus subject areas

Health Informatics
Computer Science Applications

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10.1016/j.jbi.2008.05.013

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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.",

keywords = "Bayesian network, Biosurveillance, Disease surveillance, Epidemic curve",

author = "Xia Jiang and Garrick Wallstrom and Cooper, {Gregory F.} and Wagner, {Michael M.}",

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T1 - Bayesian prediction of an epidemic curve

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AU - Wallstrom, Garrick

AU - Cooper, Gregory F.

AU - Wagner, Michael M.

N1 - Funding Information: This research was supported by the National Science Foundation grant IIS-0325581.

PY - 2009/2

Y1 - 2009/2

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AB - 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.

KW - Bayesian network

KW - Biosurveillance

KW - Disease surveillance

KW - Epidemic curve

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Bayesian prediction of an epidemic curve

Abstract

Keywords

ASJC Scopus subject areas

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