{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2026,7,26]],"date-time":"2026-07-26T01:49:41Z","timestamp":1785030581700,"version":"3.55.0"},"posted":{"date-parts":[[2025,3,5]]},"group-title":"Infectious Diseases (except HIV\/AIDS)","reference-count":29,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2025,3,5]],"date-time":"2025-03-05T00:00:00Z","timestamp":1741132800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2025,3,5]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                <jats:p>\n                  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\n                  <jats:bold>\n                    <jats:italic>serojump<\/jats:italic>\n                  <\/jats:bold>\n                  , a novel probabilistic framework and software package that uses individual-level serological data to infer infection status, timing, and subsequent antibody kinetics. We validated\n                  <jats:bold>\n                    <jats:italic>serojump<\/jats:italic>\n                  <\/jats:bold>\n                  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\n                  <jats:bold>\n                    <jats:italic>serojump<\/jats:italic>\n                  <\/jats:bold>\n                  achieves higher sensitivity in identifying infections, outperforming static threshold-based methods and precision in inferred infection timing. Application of\n                  <jats:bold>\n                    <jats:italic>serojump<\/jats:italic>\n                  <\/jats:bold>\n                  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\n                  <jats:bold>\n                    <jats:italic>serojump<\/jats:italic>\n                  <\/jats:bold>\n                  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.\n                <\/jats:p>\n                <jats:sec id=\"s1\">\n                  <jats:title>AUTHOR SUMMARY<\/jats:title>\n                  <jats:p>\n                    Tracking how infections spread and how our immune systems respond to them is essential for improving public health. One way to study this is by analysing blood samples to measure antibody levels, which can help estimate who has been infected, when they were infected, and how their immune response has developed over time. However, traditional methods for interpreting these antibody levels often use simple cutoffs that do not account for how much people\u2019s immune responses can vary, which can lead to inaccurate results. To solve this, we created a new tool in R called\n                    <jats:bold>\n                      <jats:italic>serojump<\/jats:italic>\n                    <\/jats:bold>\n                    . This tool uses advanced statistical methods to analyse changes in antibody levels over time, helping to more accurately identify who has been infected, when they were infected, and what happens to their antibodies afterwards. In our tests,\n                    <jats:bold>\n                      <jats:italic>serojump<\/jats:italic>\n                    <\/jats:bold>\n                    was better at detecting infections than traditional methods and provided more detailed insights about how people\u2019s immune systems responded to the virus and vaccines. It also revealed infections that were missed by standard testing methods. Our tool is flexible and can be used for many different diseases. By helping researchers and public health workers better understand infection patterns and immune responses,\n                    <jats:bold>\n                      <jats:italic>serojump<\/jats:italic>\n                    <\/jats:bold>\n                    can support efforts to control the spread of diseases and develop more effective treatments and vaccines.\n                  <\/jats:p>\n                <\/jats:sec>","DOI":"10.1101\/2025.03.04.25323335","type":"posted-content","created":{"date-parts":[[2025,3,6]],"date-time":"2025-03-06T01:15:20Z","timestamp":1741223720000},"source":"Crossref","is-referenced-by-count":1,"title":["<i>serojump<\/i>\n                  : A Bayesian tool for inferring infection timing and antibody kinetics from longitudinal serological data"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5585-8974","authenticated-orcid":false,"given":"David","family":"Hodgson","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1998-1844","authenticated-orcid":false,"given":"James","family":"Hay","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sheikh","family":"Jarju","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dawda","family":"Jobe","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rhys","family":"Wenlock","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6498-9212","authenticated-orcid":false,"given":"Thushan I.","family":"de Silva","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adam J.","family":"Kucharski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"54368","reference":[{"key":"2025030712201529000_2025.03.04.25323335v1.1","doi-asserted-by":"publisher","DOI":"10.1111\/tmi.12737"},{"key":"2025030712201529000_2025.03.04.25323335v1.2","doi-asserted-by":"publisher","DOI":"10.31219\/osf.io\/kqdsn"},{"key":"2025030712201529000_2025.03.04.25323335v1.3","doi-asserted-by":"publisher","DOI":"10.3390\/pathogens11070732"},{"key":"2025030712201529000_2025.03.04.25323335v1.4","doi-asserted-by":"publisher","DOI":"10.1186\/s12916-021-01982-x"},{"key":"2025030712201529000_2025.03.04.25323335v1.5","doi-asserted-by":"publisher","DOI":"10.1086\/505400"},{"key":"2025030712201529000_2025.03.04.25323335v1.6","doi-asserted-by":"crossref","unstructured":"Wansom T , Muangnoicharoen S , Nitayaphan S , Kitsiripornchai S , Crowell TA , Francisco L , et al. Risk Factors for HIV sero-conversion in a high incidence cohort of men who have sex with men and transgender women in Bangkok, Thailand. EClinicalMedicine. 2021;38: 101033.","DOI":"10.1016\/j.eclinm.2021.101033"},{"key":"2025030712201529000_2025.03.04.25323335v1.7","doi-asserted-by":"crossref","first-page":"e0005475","DOI":"10.1371\/journal.pntd.0005475","article-title":"Dengue seroprevalence, seroconversion and risk factors in Dhaka, Bangladesh","volume":"11","year":"2017","journal-title":"PLoS Negl Trop Dis"},{"key":"2025030712201529000_2025.03.04.25323335v1.8","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pntd.0009457"},{"key":"2025030712201529000_2025.03.04.25323335v1.9","doi-asserted-by":"crossref","first-page":"e136","DOI":"10.1017\/S095026882300122X","article-title":"Seroprevalence of Toxoplasma gondii and associated risk factors for infection in the Netherlands: third cross-sectional national study","volume":"151","year":"2023","journal-title":"Epidemiol Infect"},{"key":"2025030712201529000_2025.03.04.25323335v1.10","doi-asserted-by":"crossref","unstructured":"Colton H , Hodgson D , Hornsby H , Brown R , Mckenzie J , Bradley KL , et al. Risk factors for SARS-CoV-2 seroprevalence following the first pandemic wave in UK healthcare workers in a large NHS Foundation Trust. Wellcome Open Res. 2021;6: 220.","DOI":"10.12688\/wellcomeopenres.17143.1"},{"key":"2025030712201529000_2025.03.04.25323335v1.11","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1111\/irv.12805","article-title":"Incidence of influenza virus infections confirmed by serology in children and adult in a suburb community, northern China, 2018-2019 influenza season","volume":"15","year":"2021","journal-title":"Influenza Other Respi Viruses"},{"key":"2025030712201529000_2025.03.04.25323335v1.12","doi-asserted-by":"publisher","DOI":"10.1371\/journal.ppat.1003061"},{"key":"2025030712201529000_2025.03.04.25323335v1.13","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1004882"},{"key":"2025030712201529000_2025.03.04.25323335v1.14","doi-asserted-by":"publisher","DOI":"10.1111\/ele.12732"},{"key":"2025030712201529000_2025.03.04.25323335v1.15","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-018-0157-4"},{"key":"2025030712201529000_2025.03.04.25323335v1.16","doi-asserted-by":"crossref","first-page":"1557","DOI":"10.1038\/s41467-022-29310-8","article-title":"Reconstructing antibody dynamics to estimate the risk of influenza virus infection","volume":"13","year":"2022","journal-title":"Nat Commun"},{"key":"2025030712201529000_2025.03.04.25323335v1.17","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/82.4.711"},{"key":"2025030712201529000_2025.03.04.25323335v1.18","doi-asserted-by":"crossref","first-page":"e1011384","DOI":"10.1371\/journal.pcbi.1011384","article-title":"Hay JA. serosim: An R package for simulating serological data arising from vaccination, epidemiological and antibody kinetics processes","volume":"19","year":"2023","journal-title":"PLoS Comput Biol"},{"key":"2025030712201529000_2025.03.04.25323335v1.19","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1007840"},{"key":"2025030712201529000_2025.03.04.25323335v1.20","doi-asserted-by":"publisher","DOI":"10.1017\/S0022172400022610"},{"key":"2025030712201529000_2025.03.04.25323335v1.21","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-021-01377-8"},{"key":"2025030712201529000_2025.03.04.25323335v1.22","doi-asserted-by":"publisher","DOI":"10.1016\/j.immuni.2024.01.017.23"},{"key":"2025030712201529000_2025.03.04.25323335v1.23","doi-asserted-by":"crossref","first-page":"3814","DOI":"10.1038\/s41467-024-48098-3","article-title":"High SARS-CoV-2 incidence and asymptomatic fraction during Delta and Omicron BA.1 waves in The Gambia","volume":"15","year":"2024","journal-title":"Nat Commun"},{"key":"2025030712201529000_2025.03.04.25323335v1.24","doi-asserted-by":"publisher","DOI":"10.1002\/sim.3592"},{"key":"2025030712201529000_2025.03.04.25323335v1.25","unstructured":"R Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2016. Available: http:\/\/www.r-project.org\/"},{"key":"2025030712201529000_2025.03.04.25323335v1.26","unstructured":"Seamless R and C++ Integration [R package Rcpp version 1.0.14]. 2025 [cited 4 Mar 2025]. Available: https:\/\/CRAN.R-project.org\/package=Rcpp"},{"key":"2025030712201529000_2025.03.04.25323335v1.27","doi-asserted-by":"crossref","first-page":"1340","DOI":"10.3390\/microorganisms9061340","article-title":"Confounding factors influencing the kinetics and magnitude of serological response following administration of BNT162b2","volume":"9","year":"2021","journal-title":"Microorganisms"},{"key":"2025030712201529000_2025.03.04.25323335v1.28","doi-asserted-by":"crossref","first-page":"1169","DOI":"10.3390\/vaccines10081169","article-title":"Antibodies to NCP, RBD and S2 SARS-CoV-2 in vaccinated and unvaccinated healthcare workers","volume":"10","year":"2022","journal-title":"Vaccines (Basel"},{"key":"2025030712201529000_2025.03.04.25323335v1.29","doi-asserted-by":"publisher","DOI":"10.1371\/journal.ppat.1002802"}],"container-title":[],"original-title":[],"link":[{"URL":"https:\/\/syndication.highwire.org\/content\/doi\/10.1101\/2025.03.04.25323335","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T20:29:57Z","timestamp":1768508997000},"score":1,"resource":{"primary":{"URL":"http:\/\/medrxiv.org\/lookup\/doi\/10.1101\/2025.03.04.25323335"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,5]]},"references-count":29,"URL":"https:\/\/doi.org\/10.1101\/2025.03.04.25323335","relation":{"is-preprint-of":[{"id-type":"doi","id":"10.1371\/journal.pcbi.1013467","asserted-by":"subject"}]},"subject":[],"published":{"date-parts":[[2025,3,5]]},"subtype":"preprint"}}