摘要翻译：
产生间隔是感染者的感染时间和他或她的感染者的感染时间之间的时间。发电间隔的概率密度函数已成为流行病模型和流行病数据分析的重要输入。本文给出了一个一般的随机SIR传染病模型，证明了当易感人群存在多源传染病接触风险时，平均发生间隔减小。这背后的直觉是，当一个易感人群有多个潜在的传染者时，就会有一场“竞赛”来感染他或她，只有第一次感染接触才会导致感染。在流行病中，平均世代间隔随着感染流行率的增加而缩短。我们称之为潜在传染者之间的全球竞争。当接触者群集内迅速传播时，即使全球流行率较低，也可能因局部感染流行率较高而导致世代间隔收缩。我们称之为潜在传染者之间的局部竞争。通过仿真，我们说明了这两种类型的竞争。最后，我们证明了可以用传染病接触危害代替世代间隔来估计流行病中有效繁殖数的时间过程。这种方法自然而然地导致了与生存分析非常相似的流行病数据的部分可能性，为传染病流行病学的方法学研究开辟了一条有希望的道路。
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英文标题：
《Generation interval contraction and epidemic data analysis》
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作者：
Eben Kenah, Marc Lipsitch, James M. Robins
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最新提交年份：
2008
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分类信息：

一级分类：Quantitative Biology        数量生物学
二级分类：Quantitative Methods        定量方法
分类描述：All experimental, numerical, statistical and mathematical contributions of value to biology
对生物学价值的所有实验、数值、统计和数学贡献
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一级分类：Mathematics        数学
二级分类：Probability        概率
分类描述：Theory and applications of probability and stochastic processes: e.g. central limit theorems, large deviations, stochastic differential equations, models from statistical mechanics, queuing theory
概率论与随机过程的理论与应用：例如中心极限定理，大偏差，随机微分方程，统计力学模型，排队论
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一级分类：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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英文摘要：
  The generation interval is the time between the infection time of an infected person and the infection time of his or her infector. Probability density functions for generation intervals have been an important input for epidemic models and epidemic data analysis. In this paper, we specify a general stochastic SIR epidemic model and prove that the mean generation interval decreases when susceptible persons are at risk of infectious contact from multiple sources. The intuition behind this is that when a susceptible person has multiple potential infectors, there is a ``race'' to infect him or her in which only the first infectious contact leads to infection. In an epidemic, the mean generation interval contracts as the prevalence of infection increases. We call this global competition among potential infectors. When there is rapid transmission within clusters of contacts, generation interval contraction can be caused by a high local prevalence of infection even when the global prevalence is low. We call this local competition among potential infectors. Using simulations, we illustrate both types of competition.   Finally, we show that hazards of infectious contact can be used instead of generation intervals to estimate the time course of the effective reproductive number in an epidemic. This approach leads naturally to partial likelihoods for epidemic data that are very similar to those that arise in survival analysis, opening a promising avenue of methodological research in infectious disease epidemiology.
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PDF链接：
https://arxiv.org/pdf/706.2024