Choi Boseung, Busch Sydney, Kazadi Dieudonné, Ilunga Benoit, Okitolonda Emile, Dai Yi, Lumpkin Robert, Saucedo Omar, KhudaBukhsh Wasiur R, Tien Joseph, Yotebieng Marcel, Kenah Eben, Rempala Grzegorz A
Department of National Statistics, Korea University Sejoung Campus Sejoung, Republic of Korea.
Department of Mathematics, Augsburg College Minneapolis, MN, USA.
Biomath (Sofia). 2019;8(2). doi: 10.11145/j.biomath.2019.10.037. Epub 2019 Oct 15.
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 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.
我们描述了两种对小规模至中等规模疫情爆发数据进行建模的方法。第一种方法基于分支过程近似和对传播网络的直接分析，而第二种方法基于从经典SIR方程推导而来的生存模型，且无明确的传播信息。我们使用刚果民主共和国伊西罗市2012年埃博拉病毒病疫情的数据对这些方法进行比较。分支过程模型允许直接比较不同环境（如普通社区或埃博拉治疗单元）中的疾病传播情况。然而，在某些情况下，生存模型似乎能产生更准确且精度更高的参数估计。
