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血清催化模型的数学及其在公共卫生数据中的应用

The Mathematics of Serocatalytic Models With Applications to Public Health Data.

作者信息

Kamau Everlyn, Chen Junjie, Bajaj Sumali, Torres Nicolás, Creswell Richard, Pavlich-Mariscal Jaime A, Donnelly Christl, Cucunubá Zulma, Lambert Ben

机构信息

Francis I. Proctor Foundation, University of California San Francisco, San Francisco, California, USA.

Department of Statistics & Pandemic Sciences Institute, University of Oxford, Oxford, UK.

出版信息

Stat Med. 2025 Jul;44(15-17):e70188. doi: 10.1002/sim.70188.

DOI:10.1002/sim.70188
PMID:40696544
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12284347/
Abstract

Serocatalytic models are powerful tools which can be used to infer historical infection patterns from age-structured serological surveys. These surveys are especially useful when disease surveillance is limited and have an important role to play in providing a ground truth gauge of infection burden. In this tutorial, we consider a wide range of serocatalytic models to generate epidemiological insights. With mathematical analysis, we explore the properties and intuition behind these models and include applications to real data for a range of pathogens and epidemiological scenarios. We also include practical steps and code in R and Stan for interested learners to build experience with this modeling framework. Our work highlights the usefulness of serocatalytic models and shows that accounting for the epidemiological context is crucial when using these models to understand infectious disease epidemiology.

摘要

血清催化模型是强大的工具，可用于从年龄结构血清学调查中推断历史感染模式。当疾病监测有限时，这些调查特别有用，并且在提供感染负担的基本真实衡量标准方面发挥着重要作用。在本教程中，我们考虑了广泛的血清催化模型以产生流行病学见解。通过数学分析，我们探索了这些模型背后的特性和直觉，并包括了对一系列病原体和流行病学情景的实际数据的应用。我们还为有兴趣的学习者提供了在R和Stan中的实际步骤和代码，以积累使用此建模框架的经验。我们的工作突出了血清催化模型的有用性，并表明在使用这些模型理解传染病流行病学情况时，考虑流行病学背景至关重要。

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本文引用的文献

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Serodynamics: A primer and synthetic review of methods for epidemiological inference using serological data.血清动力学：使用血清学数据进行流行病学推断方法的入门介绍与综合综述
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肠道病毒D68型——一种再度出现的非脊髓灰质炎肠道病毒，可导致儿童严重呼吸道和神经系统疾病。
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