Hay James A, Routledge Isobel, Takahashi Saki
Pandemic Sciences Institute, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom.
Department of Medicine, University of California San Francisco, San Francisco, CA, USA.
Epidemics. 2024 Dec;49:100806. doi: 10.1016/j.epidem.2024.100806. Epub 2024 Nov 30.
We present a review and primer of methods to understand epidemiological dynamics and identify past exposures from serological data, referred to as serodynamics. We discuss processing and interpreting serological data prior to fitting serodynamical models, and review approaches for estimating epidemiological trends and past exposures, ranging from serocatalytic models applied to binary serostatus data, to more complex models incorporating quantitative antibody measurements and immunological understanding. Although these methods are seemingly disparate, we demonstrate how they are derived within a common mathematical framework. Finally, we discuss key areas for methodological development to improve scientific discovery and public health insights in seroepidemiology.
我们对用于理解流行病学动态并从血清学数据(即血清动力学)中识别过去暴露情况的方法进行综述并给出入门介绍。我们讨论在拟合血清动力学模型之前处理和解释血清学数据的方法,并综述估计流行病学趋势和过去暴露情况的方法,范围从应用于二元血清状态数据的血清催化模型到纳入定量抗体测量和免疫学理解的更复杂模型。尽管这些方法看似不同,但我们展示了它们是如何在一个共同的数学框架内推导出来的。最后,我们讨论方法学发展的关键领域,以改善血清流行病学中的科学发现和公共卫生见解。