疫情数据建模:2012 年刚果(金)埃博拉病毒疾病疫情分析。

Q2 Agricultural and Biological Sciences Biomath Pub Date : 2019-01-01 Epub Date: 2019-10-15 DOI:10.11145/j.biomath.2019.10.037
Boseung Choi, Sydney Busch, Dieudonné Kazadi, Benoit Ilunga, Emile Okitolonda, Yi Dai, Robert Lumpkin, Omar Saucedo, Wasiur R KhudaBukhsh, Joseph Tien, Marcel Yotebieng, Eben Kenah, Grzegorz A Rempala
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引用次数: 0

摘要

我们介绍了对中小规模疫情数据建模的两种方法。第一种方法基于分支过程近似和对传播网络的直接分析,而第二种方法则基于由经典 SIR 方程推导出的生存模型,没有明确的传播信息。我们利用 2012 年在刚果民主共和国伊西罗市爆发的由本迪布吉埃博拉病毒引起的埃博拉病毒病的数据对这两种方法进行了比较。分支过程模型可直接比较疾病在不同环境中的传播情况,如普通社区或埃博拉治疗单位。不过,在某些情况下,生存模型似乎能得出更准确、更精确的参数估计。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Modeling outbreak data: Analysis of a 2012 Ebola virus disease epidemic in DRC.

We describe two approaches to modeling data from a small to moderate-sized epidemic outbreak. The first approach is based on a branching process approximation and direct analysis of the transmission network, whereas the second one is based on a survival model derived from the classical SIR equations with no explicit transmission information. We compare these approaches using data from a 2012 outbreak of Ebola virus disease caused by Bundibugyo ebolavirus in city of Isiro, Democratic Republic of the Congo. The branching process model allows for a direct comparison of disease transmission across different environments, such as the general community or the Ebola treatment unit. However, the survival model appears to yield parameter estimates with more accuracy and better precision in some circumstances.

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来源期刊
Biomath
Biomath Agricultural and Biological Sciences-Agricultural and Biological Sciences (miscellaneous)
CiteScore
2.20
自引率
0.00%
发文量
6
审稿时长
20 weeks
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