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However, current modeling approaches have limitations that reduce their practical utility, such as the exclusion of human behavioral change in response to the epidemic or ignoring the presence of undetected infectious individuals in the population. These limitations became particularly evident during the COVID\u201019 pandemic, underscoring the need for more accurate and informative models. To address these challenges, we develop a novel Bayesian epidemic modeling framework to better capture the complexities of disease spread by incorporating behavioral responses and undetected infections. In particular, our framework makes three contributions: (1) leveraging additional data on hospitalizations and deaths in modeling the disease dynamics, (2) accounting for data uncertainty arising from the large presence of asymptomatic and undetected infections, and (3) allowing the population behavioral change to be dynamically influenced by multiple data sources (cases and deaths). We thoroughly investigate the properties of the proposed model via simulation, and illustrate its utility on COVID\u201019 data from Montr\u00e9al and Miami.<\/jats:p>","DOI":"10.1002\/sim.70709","type":"journal-article","created":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T01:59:16Z","timestamp":1786931956000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multivariable Behavioral Change Modeling of Epidemics in the Presence of Undetected Infections"],"prefix":"10.1002","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0806-0222","authenticated-orcid":false,"given":"Caitlin","family":"Ward","sequence":"first","affiliation":[{"name":"Division of Biostatistics and Health Data Science University of Minnesota  Minneapolis Minnesota USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0151-6028","authenticated-orcid":false,"given":"Rob","family":"Deardon","sequence":"additional","affiliation":[{"name":"Faculty of Veterinary Medicine University of Calgary  Calgary Alberta Canada"},{"name":"Department of Mathematics and Statistics University of Calgary  Calgary Alberta Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6448-6367","authenticated-orcid":false,"given":"Alexandra M.","family":"Schmidt","sequence":"additional","affiliation":[{"name":"Department of Epidemiology, Biostatistics, and Occupational Health McGill University  Qu\u00e9bec Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,8,16]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1098\/rstb.2016.0454"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.mbs.2024.109368"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1098\/rspa.1927.0118"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.epidem.2014.09.006"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1111\/1467-985X.00125"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1541-0420.2006.00609.x"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1098\/rsif.2010.0142"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1098\/rsif.2016.0820"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1093\/pubmed\/fdaf082"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1010100"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.dialog.2024.100179"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.idm.2023.08.002"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.1002\/cjs.11828"},{"key":"e_1_2_10_15_1","volume-title":"The Health Belief Model","author":"Champion V. 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