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.
Journal article
Epidemics
12/2024
49
Pandemic Sciences Institute, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom. Electronic address: james.hay@ndm.ox.ac.uk.
Humans, Seroepidemiologic Studies