Estimating fine age structure and time trends in human contact patterns from coarse contact data: The Bayesian rate consistency model.
Estimating fine age structure and time trends in human contact patterns from coarse contact data: The Bayesian rate consistency model.
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DOI：
10.1371/journal.pcbi.1011191
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发表时间：
2023-06
影响因子：
4.3
通讯作者：
中科院分区：
文献类型：
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自从严重急性呼吸综合征冠状病毒 2 (SARS-CoV-2) 出现以来，大规模的社会接触调查现在正在纵向衡量面对大流行和非药物干预措施时人类互动的根本变化。在这里，我们提出了一种基于模型的贝叶斯方法，即使接触者的年龄按 5 或 10 岁年龄范围粗略报告，也可以以 1 年分辨率重建接触模式。这项创新植根于人口层面的一致性约束，即群体之间的接触必须如何累加，这促使我们将这里提出的方法称为贝叶斯率一致性模型。该模型还可以量化时间趋势，并通过使用计算高效的希尔伯特空间高斯过程先验来调整纵向调查中出现的报告疲劳。我们说明了模拟数据以及报告了接触者确切年龄的欧洲和非洲社交接触数据的估计准确性，然后将该模型应用于包含接触者年龄粗略信息的社交接触数据，这些信息是在 2020 年 4 月至 6 月德国 COVID-19 大流行期间通过五次纵向调查波收集的。我们估计了大流行早期阶段社会接触的精细年龄结构，并证明社会接触强度以年龄结构的、非同质的方式反弹。贝叶斯速率一致性模型提供了一种基于模型的、非参数的、计算上易于处理的方法，用于估计社会接触的精细结构和纵向趋势，并且只要报告了调查参与者的确切年龄，就适用于粗略报告接触年龄的当代调查数据。呼吸道传染病的传播发生在密切的社会接触过程中。因此，测量社会接触的强度和模式可以更好地了解疾病传播，并提供重要数据来估计实时繁殖数等中心数量。与大流行前的调查主要以一年的年龄间隔记录接触者的年龄不同，大多数新冠病毒时代的研究只记录了广泛年龄类别的接触者的年龄，以方便报告。一些研究允许参与者报告他们无法记住年龄和性别信息的联系人总数的估计值。许多研究都是部分纵向的，这引入了报告疲劳的问题。因此，直接应用现有的统计方法来估计社交接触矩阵可能会导致丢失年龄细节和混淆估计。为此，我们开发了一种基于模型的方法，通过利用在封闭种群中必须在数学上保持的特定约束，从粗年龄数据估计细年龄接触模式。该模型还可以调整聚合联系报告和报告疲劳的混杂影响，并估计社交联系动态的时间趋势。我们希望这个统计模型能够成为全球大流行防范工具包的有用补充，以重建社会接触模式的精细结构并更精确地测量实时有效繁殖数量。
Since the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), large-scale social contact surveys are now longitudinally measuring the fundamental changes in human interactions in the face of the pandemic and non-pharmaceutical interventions. Here, we present a model-based Bayesian approach that can reconstruct contact patterns at 1-year resolution even when the age of the contacts is reported coarsely by 5 or 10-year age bands. This innovation is rooted in population-level consistency constraints in how contacts between groups must add up, which prompts us to call the approach presented here the Bayesian rate consistency model. The model can also quantify time trends and adjust for reporting fatigue emerging in longitudinal surveys through the use of computationally efficient Hilbert Space Gaussian process priors. We illustrate estimation accuracy on simulated data as well as social contact data from Europe and Africa for which the exact age of contacts is reported, and then apply the model to social contact data with coarse information on the age of contacts that were collected in Germany during the COVID-19 pandemic from April to June 2020 across five longitudinal survey waves. We estimate the fine age structure in social contacts during the early stages of the pandemic and demonstrate that social contact intensities rebounded in an age-structured, non-homogeneous manner. The Bayesian rate consistency model provides a model-based, non-parametric, computationally tractable approach for estimating the fine structure and longitudinal trends in social contacts and is applicable to contemporary survey data with coarsely reported age of contacts as long as the exact age of survey participants is reported. The transmission of respiratory infectious diseases occurs during close social contacts. Hence, measuring the intensity and patterns in social contacts leads to a better understanding of disease spread and provides essential data to estimate central quantities such as the reproduction number in real-time. Unlike pre-pandemic surveys, which largely recorded contacts’ age in one-year age intervals, most COVID-era studies only recorded the age of contacts in broad age categories to facilitate reporting. Some studies allowed participants to report an estimate for the total number of contacts for which they could not remember age and gender information. Many studies were partially longitudinal, which introduced the issue of reporting fatigue. Thus, directly applying existing statistical methods for estimating social contact matrices may result in losing age detail and confounded estimates. To this end, we develop a model-based approach which estimates fine-age contact patterns from coarse-age data by exploiting particular constraints that must hold mathematically in closed populations. The model can also adjust for the confounding effects of aggregate contact reporting and reporting fatigue and estimate the time trends in social contact dynamics. We hope this statistical model is a useful addition to the global pandemic preparedness toolkit to reconstruct the fine structure of social contact patterns and measure real-time effective reproduction numbers with greater precision.
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DOI：
10.1038/s41579-021-00535-6
发表时间：
2021-08
期刊：
Nature reviews. Microbiology
影响因子：
--
作者：
Leung NHL
通讯作者：
Leung NHL
DOI：
10.1198/016214504000001754
发表时间：
2005-06-01
影响因子：
3.7
作者：
Farrington, CP;Whitaker, HJ
通讯作者：
Whitaker, HJ
影响因子：
3.7
作者：
Eichner, Martin;Schwehm, Markus;Schmidt-Ott, Ruprecht
通讯作者：
Schmidt-Ott, Ruprecht
影响因子：
15.8
作者：
Gimma A;Munday JD;Wong KLM;Coletti P;van Zandvoort K;Prem K;CMMID COVID-19 working group;Klepac P;Rubin GJ;Funk S;Edmunds WJ;Jarvis CI
通讯作者：
Jarvis CI
影响因子：
3.7
作者：
Melegaro, Alessia;Del Fava, Emanuele;Manfredi, Piero
通讯作者：
Manfredi, Piero