Estimation of local time-varying reproduction numbers in noisy surveillance data.
Estimation of local time-varying reproduction numbers in noisy surveillance data.
复制标题
噪声监视数据中局部时变繁殖数的估计。
DOI：
10.1101/2021.04.23.21255958
复制
发表时间：
2022
期刊：
影响因子：
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通讯作者：
Kolaczyk,EricD
中科院分区：
文献类型：
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作者：
Li,Wenrui;Bulekova,Katia;Gregor,Brian;White,LauraF;Kolaczyk,EricD
了解当地传染病动态的一个有价值的指标是当地随时间变化的繁殖数，即由每个感染者造成的预期本地继发性病例数量。对这一数量的准确估计需要区分本地传播引起的病例和从其他地方输入的病例。实际上，我们可以预计，对本地或输入病例的识别是不完善的。我们研究了这种误差在局部时变再生数估计中的传播问题。此外，我们还提出了一个贝叶斯框架，用于在存在识别误差时估计真实的局部时变再生数。我们通过模拟研究以及新冠肺炎在香港和澳大利亚维多利亚州的爆发来说明我们的估计器的实际性能。本文是主题问题“为现实生活中的流行病建模的技术挑战和克服这些挑战的例子”的一部分。
A valuable metric in understanding local infectious disease dynamics is the local time-varying reproduction number, i.e. the expected number of secondary local cases caused by each infected individual. Accurate estimation of this quantity requires distinguishing cases arising from local transmission from those imported from elsewhere. Realistically, we can expect identification of cases as local or imported to be imperfect. We study the propagation of such errors in estimation of the local time-varying reproduction number. In addition, we propose a Bayesian framework for estimation of the true local time-varying reproduction number when identification errors exist. And we illustrate the practical performance of our estimator through simulation studies and with outbreaks of COVID-19 in Hong Kong and Victoria, Australia.This article is part of the theme issue ‘Technical challenges of modelling real-life epidemics and examples of overcoming these’.