Hodgson David, Hay James, Jarju Sheikh, Jobe Dawda, Wenlock Rhys, de Silva Thushan I, Kucharski Adam J
Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine.
Pandemic Sciences Institute, Nuffield Department of Medicine, University of Oxford.
medRxiv. 2025 Mar 5:2025.03.04.25323335. doi: 10.1101/2025.03.04.25323335.
Understanding acute infectious disease dynamics at individual and population levels is critical for informing public health preparedness and response. Serological assays, which measure a range of biomarkers relating to humoral immunity, can provide a valuable window into immune responses generated by past infections and vaccinations. However, traditional methods for interpreting serological data, such as binary seropositivity and seroconversion thresholds, often rely on heuristics that fail to account for individual variability in antibody kinetics and timing of infection, potentially leading to biased estimates of infection rates and post-exposure immune responses. To address these limitations, we developed , a novel probabilistic framework and software package that uses individual-level serological data to infer infection status, timing, and subsequent antibody kinetics. We validated using simulated serological data and real-world SARS-CoV-2 datasets from The Gambia. In simulation studies, the model accurately recovered individual infection status, population-level antibody kinetics, and the relationship between biomarkers and immunity against infection, demonstrating robustness under observational noise. Benchmarking against standard serological heuristics in real-world data revealed that achieves higher sensitivity in identifying infections, outperforming static threshold-based methods and precision in inferred infection timing. Application of to longitudinal SARS-CoV-2 serological data taken during the Delta wave provided additional insights into i) missed infections based on sub-threshold rises in antibody level and ii) antibody responses to multiple biomarkers post-vaccination and infection. Our findings highlight the utility of as a pathogen-agnostic, flexible tool for serological inference, enabling deeper insights into infection dynamics, immune responses, and correlates of protection. The open-source framework offers researchers a platform for extracting information from serological datasets, with potential applications across various infectious diseases and study designs.
了解个体和群体层面的急性传染病动态对于指导公共卫生防范和应对至关重要。血清学检测可测量一系列与体液免疫相关的生物标志物，能为过去感染和疫苗接种所产生的免疫反应提供宝贵窗口。然而，传统的血清学数据解读方法，如二元血清阳性和血清转化阈值，往往依赖经验法则，未能考虑抗体动力学和感染时间的个体差异，这可能导致对感染率和暴露后免疫反应的估计产生偏差。为解决这些局限性，我们开发了一种新颖的概率框架和软件包，该框架利用个体层面的血清学数据来推断感染状态、时间以及后续的抗体动力学。我们使用来自冈比亚的模拟血清学数据和真实世界的SARS-CoV-2数据集对其进行了验证。在模拟研究中，该模型准确地恢复了个体感染状态、群体层面的抗体动力学以及生物标志物与抗感染免疫力之间的关系，证明了其在观测噪声下的稳健性。在真实世界数据中与标准血清学经验法则进行基准测试表明，该模型在识别感染方面具有更高的灵敏度，优于基于静态阈值的方法，并且在推断感染时间方面具有更高的精度。将该模型应用于Delta波期间采集的纵向SARS-CoV-2血清学数据，能进一步深入了解：i）基于抗体水平低于阈值的上升而漏诊的感染；ii）接种疫苗和感染后对多种生物标志物的抗体反应。我们的研究结果凸显了该模型作为一种病原体无关的灵活血清学推断工具的实用性，能够更深入地了解感染动态、免疫反应和保护相关因素。这个开源框架为研究人员提供了一个从血清学数据集中提取信息的平台，在各种传染病和研究设计中都有潜在应用。
