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从SARS-CoV-2的相同序列集群的大小来估计二次病例中的Re和过度分散

Emma B Hodcroft^1,2,3, Martin S Wohlfender^1,2,4, Richard A Neher^3,5

¹Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland.

PLoS computational biology

|April 15, 2025

概括

这项研究引入了一个新的贝叶斯框架来分析SARS-CoV-2基因组数据,估计传播率和过度分散. 结果显示了不同地区传播动态的变化,为传染病传播提供了洞察力.

科学领域:

流行病学流行病学
基因组学就是基因组学.
计算生物学计算生物学

背景情况:

来自COVID-19流行病的基因组数据为SARS-CoV-2传播动态提供了洞察力.
了解有效繁殖数 (R) 和过度分散 (k) 的变化对于公共卫生至关重要.
现有的方法可能无法仅从序列数据中完全捕捉传输异质性.

研究的目的:

开发一种新的贝叶斯框架,直接从病毒基因组数据中推断SARS-CoV-2传播参数 (R和k).
模拟相同序列集群的分布,整合传播,突变率和病例检测.
将框架应用于来自多个国家的真实世界基因组数据.

主要方法:

开发了一种相同序列集群大小分布的数学模型.
在贝叶斯推理框架内使用基因组序列实现了模型.
通过模拟研究验证了该模型,并将其应用于2021年来自瑞士,丹麦和德国的SARS-CoV-2序列.

主要成果:

每月生成有效繁殖数 (R) 和过度分散参数 (k) 的估计值.
R的估计值略低于其他方法;k的估计值与之前的研究可比.
丹麦显示了较高的k估计值,这表明2021年更少的取代和更受控制的传播.

结论:

开发的贝叶斯框架提供了一种新的方法,可以从基因组数据中推断传染病的传播和过度分散.
这些发现突显了SARS-CoV-2传播动态的区域差异.
该方法有可能应用于其他病原体,并增加基因组监测.

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