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Soc. Open Sci."],"published-print":{"date-parts":[[2025,6]]},"abstract":"<jats:p>During infectious disease outbreaks, humans often base their decision to adhere to an intervention strategy on individual choices and opinions. However, due to data limitations and inference challenges, infectious disease models usually omit these variables. We constructed a compartmental, deterministic Susceptible-Exposed-Infectious-Recovered (SEIR) disease model that includes a behavioural function with parameters influencing intervention uptake. The behavioural function accounted for an initial subpopulation opinion towards an intervention, their outbreak information awareness sensitivity and the extent to which they are swayed by the real-time intervention effectiveness information. Applying the model to vaccination uptake and three human pathogens\u2014pandemic influenza, SARS-CoV-2 and Ebola virus\u2014we explored through model simulation how these intervention adherence decision parameters and behavioural heterogeneity impacted epidemiological outcomes. From our model simulations, we found that in some pathogen systems, different types of outbreak information awareness at different outbreak stages may be more informative to an information-sensitive population and may lead to less severe epidemic outcomes. Incorporating behavioural functions that modify infection control intervention adherence into epidemiological models can aid our understanding of adherence dynamics during outbreaks. Ultimately, by parameterizing models with what we know about human behaviour towards vaccination adherence, such models can help assist decision-makers during outbreaks.<\/jats:p>","DOI":"10.1098\/rsos.241964","type":"journal-article","created":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:06:48Z","timestamp":1750187208000},"update-policy":"https:\/\/doi.org\/10.1098\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Unifying human infectious disease models and real-time awareness of population- and subpopulation-level intervention effectiveness"],"prefix":"10.1098","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1125-7210","authenticated-orcid":true,"given":"Rachel L.","family":"Seibel","sequence":"first","affiliation":[{"name":"EPSRC & MRC Centre for Doctoral Training in Mathematics for Real-World Systems, Mathematics Institute, University of Warwick","place":["Coventry, UK"]},{"name":"Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, Mathematics Institute and School of Life Sciences, University of Warwick","place":["Coventry, UK"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6875-7232","authenticated-orcid":true,"given":"Michael J.","family":"Tildesley","sequence":"additional","affiliation":[{"name":"Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, Mathematics Institute and School of Life Sciences, University of Warwick","place":["Coventry, UK"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2992-2004","authenticated-orcid":true,"given":"Edward M.","family":"Hill","sequence":"additional","affiliation":[{"name":"Civic Health Innovation Labs and Institute of Population Health, University of Liverpool","place":["Liverpool, UK"]},{"name":"NIHR Health Protection Research Unit in Gastrointestinal Infections, University of Liverpool","place":["Liverpool, UK"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"175","published-online":{"date-parts":[[2025,6,18]]},"reference":[{"key":"e_1_3_7_2_2","article-title":"Contacts in context: large-scale setting-specific social mixing matrices from the BBC Pandemic project","author":"Klepac P","year":"2020","unstructured":"Klepac P, Kucharski AJ, Conlan AJ, Kissler S, Tang ML, Fry H, Gog JR. 2020 Contacts in context: large-scale setting-specific social mixing matrices from the BBC Pandemic project. medRxiv. 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