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Natl. Acad. Sci. U.S.A."],"published-print":{"date-parts":[[2026,3,31]]},"abstract":"<jats:p>\n                    Infectious disease spread is a multiscale process composed of within-host (biological) and between-host (social) drivers and disentangling them from each other is a central challenge in epidemiology. Here, we introduce VIBES, a multiscale modeling framework that explicitly integrates viral dynamics based on patient-level data with population-level transmission on a data-driven network of social contacts. Using SARS-CoV-2 as a case study, we analyze three emergent epidemic properties, namely the generation time, serial interval, and presymptomatic transmission. First, we established a purely biological baseline, thus independent of the reproduction number (\n                    <jats:italic toggle=\"yes\">R<\/jats:italic>\n                    ), from the within-host model, estimating a generation time of 6.3 d for symptomatic individuals and 43.1% presymptomatic transmission. Then, using the full model incorporating social contacts, we found a shorter generation time (5.4 d at\n                    <jats:italic toggle=\"yes\">R<\/jats:italic>\n                    = 3.0) and an increase in presymptomatic transmission (52.8% at\n                    <jats:italic toggle=\"yes\">R<\/jats:italic>\n                    = 3.0), disentangling the impact of social drivers from a purely biological baseline. We further show that as pathogen transmissibility increases (\n                    <jats:italic toggle=\"yes\">R<\/jats:italic>\n                    from 1.3 to 6), competition among infectious individuals shortens the generation time and serial interval by up to 21% and 13%, respectively. Conversely, a social intervention, like isolation, increases the proportion of presymptomatic transmission by about 30%. Our framework also estimates metrics that are challenging to obtain empirically, such as the generation time for asymptomatic individuals (5.6 d; 95%CI: 5.1 to 6.0 at\n                    <jats:italic toggle=\"yes\">R<\/jats:italic>\n                    = 1.3). Our findings establish multiscale modeling as a powerful tool for mechanistically quantifying how pathogen biology and human social behavior shape epidemic dynamics as well as for assessing public health interventions.\n                  <\/jats:p>","DOI":"10.1073\/pnas.2523055123","type":"journal-article","created":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T17:17:27Z","timestamp":1774545447000},"update-policy":"https:\/\/doi.org\/10.1073\/pnas.cm10313","source":"Crossref","is-referenced-by-count":3,"title":["VIBES: A multiscale modeling approach integrating within-host and between-hosts dynamics in epidemics"],"prefix":"10.1073","volume":"123","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8441-9359","authenticated-orcid":false,"given":"Paulo Cesar","family":"Ventura","sequence":"first","affiliation":[{"name":"Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public 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Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6335-5648","authenticated-orcid":false,"given":"Hongjie","family":"Yu","sequence":"additional","affiliation":[{"name":"Shanghai Institute of Infectious Disease and Biosecurity, Fudan University","place":["Shanghai, China"]},{"name":"School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education","place":["Shanghai, China"]},{"name":"Department of Infectious Diseases, Huashan Hospital, Fudan University","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5117-0611","authenticated-orcid":false,"given":"Stefano","family":"Merler","sequence":"additional","affiliation":[{"name":"Center for Health Emergencies, Bruno Kessler Foundation","place":["Trento, 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