{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"bioRxiv"}],"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T07:08:22Z","timestamp":1791443302911,"version":"4.3.3"},"posted":{"date-parts":[[2026,6,1]]},"group-title":"Microbiology","reference-count":47,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,6,8]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:p>\n                  Dose-response modeling provides estimates of infectious and lethal doses, which can be used to inform control and prevention measures. Unfortunately, data from experimental challenge studies, which are needed to perform dose-response modeling, are often sparse. For example, non-human primate (NHP) challenge studies tend to have small samples sizes and little dose variation, often with only one or two dose levels per study. Thus, it is infeasible to apply traditional dose-response modeling approaches to data from single NHP studies. To address this challenge, we developed a mechanistic Bayesian model that aggregates and analyzes NHP pathogen load data across multiple studies. Our model links dose-infectivity to pathogen kinetics, which allows us to estimate the infectious dose and evaluate dose effects on within-host viral kinetics simultaneously. With this model, we obtained the first-ever ID\n                  <jats:sub>50<\/jats:sub>\n                  estimate for SARS-CoV-1 in NHPs using data compiled from six NHP challenge studies. Our work demonstrates the value in reusing previous data from animal experiments. Our modeling framework can be applied to other pathogens, enabling robust dose-response inference when individual challenge studies are inconclusive.\n                <\/jats:p>\n                <jats:sec>\n                  <jats:title>Author summary<\/jats:title>\n                  <jats:p>Dose-response models are used to estimate pathogen doses needed to cause infection in humans, so they are useful for informing outbreak control policies. Unfortunately, performing dose-response modeling can be difficult due to limitations in the available data. If the pathogen causes significant risk of severe disease or death in humans, then controlled human infections cannot be performed. Additionally, experimental challenge studies of relevant animal models, such as non-human primates (NHPs), often have small sample sizes and limited dose ranges, which make dose-response modeling unfeasible using data from single studies. We developed an approach to aggregate data across multiple challenge studies to enable dose-response modeling in the absence of dose-response experiments. We applied our approach to data from six NHP challenge studies to perform the first-ever dose-response analysis of SARS-CoV-1 in NHPs. Our approach also included a mechanistic, mathematical model of within-host pathogen kinetics, which allowed us to assess the effect of SARS-CoV-1 dosage on patterns of viral RNA shedding. The framework we developed can be readily applied to other host-pathogen systems, and the mechanistic components of our model contribute to a growing movement towards understanding dose effects beyond simple infectivity.<\/jats:p>\n                <\/jats:sec>","DOI":"10.64898\/2026.06.01.729195","type":"posted-content","created":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T04:30:24Z","timestamp":1780374624000},"source":"Crossref","is-referenced-by-count":1,"title":["Data aggregation and mechanistic modeling enable dose-response analysis of SARS-CoV-1 in non-human primates"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-5984-5486","authenticated-orcid":false,"given":"Philip C.","family":"Lee","sequence":"first","affiliation":[{"name":"Center for Computational Biology, University of California Berkeley, Berkeley, California, United States of America"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Celine 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