Serodynamics: A primer and synthetic review of methods for epidemiological inference using serological data
James A. Hay, Isobel Routledge, Saki Takahashi
Abstract
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. • There is a growing interest in using detailed, high-throughput immunological assays in infectious disease epidemiology. • We review current analytical methods for estimating epidemiological dynamics from serological data, termed serodynamics. • We discuss key considerations for modelers, data requirements and limitations when modeling serological data. • Despite different presentation, we show that most serodynamics models can be described by a single mathematical framework.