从指数增长率估算 R0 的世代时间分布不确定性边界。

IF 1.8 4区 数学 Q3 ECOLOGY
Journal of Biological Dynamics Pub Date : 2024-12-01 Epub Date: 2024-10-16 DOI:10.1080/17513758.2024.2410720
James Cochran, Bogdan Oancea, Dan Pirjol
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引用次数: 0

摘要

基本繁殖数 R0 是决定流行病在易感人群中传播的主要参数之一。Wallinga 和 Lipsitch 提出了一种利用欧拉-洛特卡方程估算 R0 的方法,该方法需要对世代间隔分布进行拉普拉斯变换。确定世代时间分布具有挑战性,因为世代时间无法直接观测。我们仅利用发电间隔分布的前几个时刻就证明了 R0 的上界和下界,并研究了边界对这些参数的敏感性。这些界值不需要发电间隔分布的确切形状,并给出了 R-R0 关系的稳健估计值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bounding the generation time distribution uncertainty on R0 estimation from exponential growth rates.

The basic reproduction number R0 is one of the main parameters determining the spreading of an epidemic in a population of susceptible individuals. Wallinga and Lipsitch proposed a method for estimating R0 using the Euler-Lotka equation, which requires the Laplace transform of the generation interval distribution. The determination of the generation time distribution is challenging, as the generation time is not directly observable. We prove upper and lower bounds on R0 using only the first few moments of the generation interval distributions and study the sensitivity of the bounds to these parameters. The bounds do not require the exact shape of the generation interval distribution and give robust estimates of the r-R0 relationship.

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来源期刊
Journal of Biological Dynamics
Journal of Biological Dynamics ECOLOGY-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
4.90
自引率
3.60%
发文量
28
审稿时长
33 weeks
期刊介绍: Journal of Biological Dynamics, an open access journal, publishes state of the art papers dealing with the analysis of dynamic models that arise from biological processes. The Journal focuses on dynamic phenomena at scales ranging from the level of individual organisms to that of populations, communities, and ecosystems in the fields of ecology and evolutionary biology, population dynamics, epidemiology, immunology, neuroscience, environmental science, and animal behavior. Papers in other areas are acceptable at the editors’ discretion. In addition to papers that analyze original mathematical models and develop new theories and analytic methods, the Journal welcomes papers that connect mathematical modeling and analysis to experimental and observational data. The Journal also publishes short notes, expository and review articles, book reviews and a section on open problems.
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