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疫情曲线的贝叶斯预测。

Bayesian prediction of an epidemic curve.

作者信息

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

机构信息

Department of Biomedical Informatics, University of Pittsburgh, Parkvale Building, M-183, 200 Meyran Avenue, Pittsburgh, PA 15260, USA.

出版信息

J Biomed Inform. 2009 Feb;42(1):90-9. doi: 10.1016/j.jbi.2008.05.013. Epub 2008 Jun 13.

DOI:10.1016/j.jbi.2008.05.013
PMID:18593605
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.

摘要

流行曲线是一种图表,其中将爆发疾病的新病例数随时间绘制出来。流行曲线通常在疾病爆发结束后构建。然而,在疫情爆发早期对流行曲线进行良好估计对卫生保健官员来说将非常宝贵。目前,预测疫情严重程度的技术非常有限。就预测未来病例数而言,通常流行病学家只是凭经验猜测可能会有多少人受到影响。我们开发了一种在疫情爆发早期估计流行曲线的模型,并展示了测试其准确性的实验结果。

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