Department of Biostatistics, University of Washington, Seattle, WA 98105, USA.
Biostatistics. 2011 Jul;12(3):548-66. doi: 10.1093/biostatistics/kxq068. Epub 2010 Nov 11.
We argue that the time from the onset of infectiousness to infectious contact, which we call the "contact interval," is a better basis for inference in epidemic data than the generation or serial interval. Since contact intervals can be right censored, survival analysis is the natural approach to estimation. Estimates of the contact interval distribution can be used to estimate R(0) in both mass-action and network-based models. We apply these methods to 2 data sets from the 2009 influenza A(H1N1) pandemic.
我们认为，从传染性出现到传染性接触的时间，即我们所谓的“接触间隔”，是传染病数据推断的更好基础，而不是生成或序列间隔。由于接触间隔可能会被右删失，因此生存分析是估计的自然方法。接触间隔分布的估计可以用于在质量作用和基于网络的模型中估计 R(0)。我们将这些方法应用于 2009 年甲型 H1N1 流感大流行的 2 个数据集。
