Hodgson, D. orcid.org/0000-0002-5585-8974, Hay, J., Jarju, S. et al. (4 more authors) (2025) serojump: a Bayesian tool for inferring infection timing and antibody kinetics from longitudinal serological data. PLoS Computational Biology, 21 (9). e1013467. ISSN: 1553-734X
Abstract
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 serojump, a novel probabilistic framework and software package that uses individual-level serological data to infer infection status, timing, and subsequent antibody kinetics. We validated serojump 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 serojump achieves higher sensitivity in identifying infections, outperforming static threshold-based methods and precision in inferred infection timing. Application of serojump 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 serojump 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.
Metadata
Item Type: | Article |
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Copyright, Publisher and Additional Information: | © 2025 Hodgson et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
Keywords: | Biomedical and Clinical Sciences; Immunology; Immunization; Emerging Infectious Diseases; Vaccine Related; Infectious Diseases; Prevention; 2.1 Biological and endogenous factors; 2.5 Research design and methodologies (aetiology); Inflammatory and immune system; Infection; Good Health and Well Being |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > School of Medicine and Population Health |
Date Deposited: | 29 Sep 2025 13:41 |
Last Modified: | 29 Sep 2025 13:41 |
Status: | Published |
Publisher: | Public Library of Science (PLoS) |
Refereed: | Yes |
Identification Number: | 10.1371/journal.pcbi.1013467 |
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Sustainable Development Goals: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:232316 |