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SIR传染病模型的参数识别问题:识别未报告病例。

The parameter identification problem for SIR epidemic models: identifying unreported cases.

作者信息

Magal Pierre, Webb Glenn

机构信息

Univ. Bordeaux, IMB, UMR 5251, 33400, Talence, France.

CNRS, IMB, UMR 5251, 33400, Talence, France.

出版信息

J Math Biol. 2018 Dec;77(6-7):1629-1648. doi: 10.1007/s00285-017-1203-9. Epub 2018 Jan 13.

Abstract

A SIR epidemic model is analyzed with respect to identification of its parameters, based upon reported case data from public health sources. The objective of the analysis is to understand the relation of unreported cases to reported cases. In many epidemic diseases the ratio of unreported to reported cases is very high, and of major importance in implementing measures for controlling the epidemic. This ratio can be estimated by the identification of parameters for the model from reported case data. The analysis is applied to three examples: (1) the Hong Kong seasonal influenza epidemic in New York City in 1968-1969, (2) the bubonic plague epidemic in Bombay, India in 1906, and (3) the seasonal influenza epidemic in Puerto Rico in 2016-2017.

摘要

基于公共卫生来源报告的病例数据,对一个SIR传染病模型的参数识别进行了分析。分析的目的是了解未报告病例与报告病例之间的关系。在许多传染病中,未报告病例与报告病例的比例非常高,这在实施疫情控制措施中至关重要。这个比例可以通过从报告病例数据中识别模型参数来估计。该分析应用于三个例子:(1)1968 - 1969年纽约市的香港季节性流感疫情,(2)1906年印度孟买的腺鼠疫疫情,以及(3)2016 - 2017年波多黎各的季节性流感疫情。

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