An open source tool to infer epidemiological and immunological dynamics from serological data: serosolver
An open source tool to infer epidemiological and immunological dynamics from serological data: serosolver
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DOI：
10.1371/journal.pcbi.1007840
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发表时间：
2020-05-01
影响因子：
4.3
通讯作者：
Riley, Steven
中科院分区：
文献类型：
--
作者：
Hay, James A.;Minter, Amanda;Riley, Steven
抗体水平可以确定以前暴露于病原体和个人有多大可能在未来被感染。然而，抗体浓度随着时间的推移而变化，并且一些病原体不断进化。在这种情况下，个体可能会被感染并多次接种疫苗，当他们预先存在的免疫力失败时，导致广泛的抗体谱。分析此类数据的传统方法通常无法解释这一点。此外，收集抗体数据的研究可能会有不同的设计，但通常是由类似的生物过程支撑的。我们开发了一种统计方法和配套的软件包，以更好地了解这些复杂系统的免疫学和流行病学使用血清学数据。我们提出了两个案例研究，以证明我们的软件包，serosolver，可以应用于不同的设置：i）2009年大流行的A/H1N1流感病毒在香港的流行病学和ii）在中国广州的A/H3 N2流感感染的历史模式。这些结果表明，现代分析方法可以揭示额外的信息，否则错过了使用传统的approaches.We提出了一个灵活的，开源的R包，旨在从血清学数据集获得生物学和流行病学的见解。表征多菌株病原体的既往暴露构成了特定的统计挑战：在血清学测定中测量的观察到的抗体应答取决于产生交叉反应性抗体应答的多种未观察到的既往感染。我们提供了一个通用的建模框架，以共同推断感染历史，并使用针对当前和历史菌株的抗体滴度描述这些感染产生的免疫反应。我们通过将潜伏感染动力学与抗体动力学的机械模型联系起来来实现这一点，该模型随着时间的推移产生预期的抗体滴度。我们的目标是提供一个灵活的包，以确定感染的历史，可以适用于一系列的病原体。我们提出了两个案例研究来说明我们的模型如何推断关键的免疫学参数，例如抗体滴度增强、减弱和交叉反应，以及潜在的流行病学过程，例如攻击率和年龄分层的感染风险。
Author summaryAntibody levels can determine previous exposure to a pathogen and how likely individuals are to be infected in the future. However, antibody concentrations change over time, and some pathogens are continually evolving. In such cases, individuals may be infected and vaccinated multiple times when their pre-existing immunity fails, leading to a wide range of antibody profiles. Traditional approaches to analyse such data do not typically account for this. In addition, studies collecting antibody data may be designed differently, but are often underpinned by similar biological processes. We developed a statistical method and accompanying software package to better understand the immunology and epidemiology of these complex systems using serological data. We present two case studies to demonstrate how our software package, serosolver, can be applied to different settings: i) the epidemiology of the 2009 pandemic A/H1N1 influenza virus in Hong Kong and ii) historical patterns of A/H3N2 influenza infection in Guangzhou, China. These results demonstrate how modern analytical methods can reveal additional information from serological data that is otherwise missed using traditional approaches.We present a flexible, open source R package designed to obtain biological and epidemiological insights from serological datasets. Characterising past exposures for multi-strain pathogens poses a specific statistical challenge: observed antibody responses measured in serological assays depend on multiple unobserved prior infections that produce cross-reactive antibody responses. We provide a general modelling framework to jointly infer infection histories and describe immune responses generated by these infections using antibody titres against current and historical strains. We do this by linking latent infection dynamics with a mechanistic model of antibody kinetics that generates expected antibody titres over time. Our aim is to provide a flexible package to identify infection histories that can be applied to a range of pathogens. We present two case studies to illustrate how our model can infer key immunological parameters, such as antibody titre boosting, waning and cross-reaction, as well as latent epidemiological processes such as attack rates and age-stratified infection risk.