一种基于模拟的方法,用于从时间汇总的疾病发病率时间序列数据中估算随时间变化的繁殖数量

IF 3 3区 医学 Q2 INFECTIOUS DISEASES
I. Ogi-Gittins , W.S. Hart , J. Song , R.K. Nash , J. Polonsky , A. Cori , E.M. Hill , R.N. Thompson
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引用次数: 0

摘要

在传染病爆发期间跟踪病原体的传播性对于评估公共卫生措施的有效性和规划未来的控制策略至关重要。衡量传播性的一个关键指标是随时间变化的繁殖数,在一系列病原体爆发期间,可从疾病发病时间序列数据中实时估算出该繁殖数。在频繁记录疾病发病率的情况下,估算随时间变化的繁殖数的常用方法是可靠的,但这些发病率数据通常是按时间汇总的(例如,病例数可能是每周而不是每天报告)。正如我们所展示的,当传播的时间尺度短于数据记录的时间尺度时,常用的可传播性估算方法可能并不可靠。为了解决这个问题,我们在此开发了一种基于模拟的方法,该方法涉及近似贝叶斯计算(Approximate Bayesian Computation),用于从时间聚合的疾病发病率时间序列数据中估计随时间变化的繁殖数量。我们首先使用了一个模拟数据集,该数据集代表了一种无法获得每日疾病发病率数据而只能报告每周汇总值的情况,证明了我们的方法在这种情况下能够准确估计随时间变化的繁殖数量。然后,我们将我们的方法应用于两个疫情数据集,包括英国威尔士 2019-20 年和 2022-23 年的每周流感病例数。我们的方法简单易用,可以在未来的传染病爆发期间,从时间聚合数据中获得随时间变化的繁殖数量的准确估计。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A simulation-based approach for estimating the time-dependent reproduction number from temporally aggregated disease incidence time series data

Tracking pathogen transmissibility during infectious disease outbreaks is essential for assessing the effectiveness of public health measures and planning future control strategies. A key measure of transmissibility is the time-dependent reproduction number, which has been estimated in real-time during outbreaks of a range of pathogens from disease incidence time series data. While commonly used approaches for estimating the time-dependent reproduction number can be reliable when disease incidence is recorded frequently, such incidence data are often aggregated temporally (for example, numbers of cases may be reported weekly rather than daily). As we show, commonly used methods for estimating transmissibility can be unreliable when the timescale of transmission is shorter than the timescale of data recording. To address this, here we develop a simulation-based approach involving Approximate Bayesian Computation for estimating the time-dependent reproduction number from temporally aggregated disease incidence time series data. We first use a simulated dataset representative of a situation in which daily disease incidence data are unavailable and only weekly summary values are reported, demonstrating that our method provides accurate estimates of the time-dependent reproduction number under such circumstances. We then apply our method to two outbreak datasets consisting of weekly influenza case numbers in 2019–20 and 2022–23 in Wales (in the United Kingdom). Our simple-to-use approach will allow accurate estimates of time-dependent reproduction numbers to be obtained from temporally aggregated data during future infectious disease outbreaks.

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来源期刊
Epidemics
Epidemics INFECTIOUS DISEASES-
CiteScore
6.00
自引率
7.90%
发文量
92
审稿时长
140 days
期刊介绍: Epidemics publishes papers on infectious disease dynamics in the broadest sense. Its scope covers both within-host dynamics of infectious agents and dynamics at the population level, particularly the interaction between the two. Areas of emphasis include: spread, transmission, persistence, implications and population dynamics of infectious diseases; population and public health as well as policy aspects of control and prevention; dynamics at the individual level; interaction with the environment, ecology and evolution of infectious diseases, as well as population genetics of infectious agents.
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