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A practical generation-interval-based approach to inferring the strength of epidemics from their speed

机译:一种基于世代间隔的实用方法,可以从流行的速度推断流行的强度

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摘要

Infectious disease outbreaks are often characterized by the reproduction number R and exponential rate of growth r . R provides information about outbreak control and predicted final size, but estimating R is difficult, while r can often be estimated directly from incidence data. These quantities are linked by the generation interval – the time between when an individual is infected by an infector, and when that infector was infected. It is often infeasible to obtain the exact shape of a generation-interval distribution, and to understand how this shape affects estimates of R . We show that estimating generation interval mean and variance provides insight into the relationship between R and r . We use examples based on Ebola, rabies and measles to explore approximations based on gamma-distributed generation intervals, and find that use of these simple approximations are often sufficient to capture the r – R relationship and provide robust estimates of R .
机译:传染病暴发通常以繁殖数R和指数增长率r为特征。 R提供有关爆发控制和预测的最终规模的信息,但是估算R很难,而r通常可以直接从发病数据中估算。这些数量与世代间隔(即个体被感染者感染到感染者被感染之间的时间)相关。获得世代间隔分布的确切形状,并理解该形状如何影响R的估计,通常是不可行的。我们表明,估计生成间隔的均值和方差可以深入了解R和r之间的关系。我们使用基于埃博拉病毒,狂犬病和麻疹的示例探索基于伽玛分布的世代间隔的近似值,发现使用这些简单近似值通常足以捕获r-R关系并提供R的可靠估计。

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