{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T20:05:38Z","timestamp":1792181138865,"version":"4.4.0"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1014746","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T00:00:00Z","timestamp":1789084800000}}],"reference-count":50,"publisher":"Public Library of Science (PLoS)","issue":"9","license":[{"start":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T00:00:00Z","timestamp":1788393600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"American Meteorological Society (AMS)\u2013Simons Travel Grant","award":["Not specified"],"award-info":[{"award-number":["Not specified"]}]},{"name":"Centers for Disease Control and Prevention (CDC), Center for Forecasting and Outbreak Analytics","award":["CDC-RFA-FT-23-0069"],"award-info":[{"award-number":["CDC-RFA-FT-23-0069"]}]},{"name":"Northeastern University London"}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"\n During epidemic outbreaks, populations adapt their behavior in response to disease burden, fundamentally altering transmission dynamics. Despite this, most compartmental models assume constant contact rates throughout outbreaks. To quantify biases from this assumption, we fitted a baseline SEIRD model with constant transmission and three behavioral variants\u2014incorporating mortality-driven transmission reduction via exponential, rational, and mixed functional forms\u2014to COVID-19 mortality data from 20 selected US locations during the first pandemic wave (March\u2013July 2020). All three behavioral models achieved a lower median normalized sum of squared error in at least 18 of 20 locations, and Bayesian model selection favored them in at least 18 of 20 locations. More importantly, we identified systematic biases when behavioral responses are ignored: the baseline model consistently underestimated the basic reproduction number (\n \n \n \n \n \n \n \u211b<\/mml:mi>\n 0<\/mml:mn>\n <\/mml:msub>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n ) while paradoxically overestimating the final epidemic size. Median\n \n \n \n \n \n \n \u211b<\/mml:mi>\n 0<\/mml:mn>\n <\/mml:msub>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n estimates from the behavioral models exceeded the baseline estimates across all 20 locations, yet baseline models predicted larger cumulative infection burdens. Controlled synthetic experiments\u2014where mortality trajectories were generated from behavioral models with known parameters\u2014confirmed these biases result from model misspecification rather than data quality or stochastic variation. We prove analytically that for any fixed\n \n \n \n \n \n \n \u211b<\/mml:mi>\n 0<\/mml:mn>\n <\/mml:msub>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n , the baseline model overestimates cumulative infections compared to behavioral models where mortality reduces transmission, regardless of functional form. This dual bias has potential implications for pandemic response: standard models may simultaneously underestimate pathogen contagiousness, which could contribute to delayed or insufficient early interventions while overestimating infection burden, which could bias planning for later epidemic phases. Our findings across 20 geographically diverse locations demonstrate that incorporating behavioral change substantially improves both model fit and estimation of epidemiological parameters relevant for public health policy.\n <\/jats:p>","DOI":"10.1371\/journal.pcbi.1014746","type":"journal-article","created":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T17:50:12Z","timestamp":1788457812000},"page":"e1014746","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":1,"title":["The paradox of neglecting changes in behavior: How standard epidemic models misestimate both transmissibility and final epidemic size"],"prefix":"10.1371","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8619-1861","authenticated-orcid":true,"given":"Binod","family":"Pant","sequence":"first","affiliation":[{"name":"Mississippi State University","place":["Jackson, Mississippi, United States of America"],"department":["Department of Mathematics and Statistics"]},{"name":"Machine Intelligence Group for the Betterment of Health and the Environment, Northeastern University","place":["Boston, Massachusetts, United States of America"]},{"name":"Network Science Institute, Northeastern University","place":["Boston, Massachusetts, United States of America"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marko","family":"Lalovic","sequence":"additional","affiliation":[{"name":"Network Science Institute, Northeastern University London","place":["London, United Kingdom"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Istv\u00e1n Z.","family":"Kiss","sequence":"additional","affiliation":[{"name":"Network Science Institute, Northeastern University London","place":["London, United Kingdom"]},{"name":"Northeastern University","place":["Boston, Massachusetts, United States of America"],"department":["Department of Mathematics"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4206-418X","authenticated-orcid":true,"given":"Mauricio","family":"Santillana","sequence":"additional","affiliation":[{"name":"Machine Intelligence Group for the Betterment of Health and the Environment, Northeastern University","place":["Boston, Massachusetts, United States of America"]},{"name":"Network Science Institute, Northeastern University","place":["Boston, Massachusetts, United States of America"]},{"name":"Harvard T.H. Chan School of Public Health","place":["Boston, Massachusetts, United States of America"],"department":["Department of Epidemiology"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,9,3]]},"reference":[{"issue":"2","key":"pcbi.1014746.ref001","first-page":"191","article-title":"To mask or not to mask: a note on the 1918 Spanish influenza epidemic in Tucson","volume":"25","author":"B Luckingham","year":"1984","journal-title":"J Ariz Hist"},{"issue":"19","key":"pcbi.1014746.ref002","article-title":"Unmasking history: Who was behind the anti-mask league protests during the 1918 influenza epidemic in San Francisco?","volume":"5","author":"B Dolan","year":"2020","journal-title":"Perspect Med Humanit"},{"key":"pcbi.1014746.ref003","doi-asserted-by":"crossref","first-page":"91","DOI":"10.12688\/gatesopenres.13278.2","article-title":"The impact of condom use on the HIV epidemic","volume":"5","author":"J Stover","year":"2022","journal-title":"Gates Open Res"},{"issue":"6","key":"pcbi.1014746.ref004","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0252315","article-title":"Association between COVID-19 outcomes and mask mandates, adherence, and attitudes","volume":"16","author":"D Adjodah","year":"2021","journal-title":"PLoS One"},{"issue":"1","key":"pcbi.1014746.ref005","doi-asserted-by":"crossref","first-page":"3737","DOI":"10.1038\/s41467-021-24115-7","article-title":"Association of social distancing and face mask use with risk of COVID-19","volume":"12","author":"S Kwon","year":"2021","journal-title":"Nat Commun"},{"issue":"37","key":"pcbi.1014746.ref006","doi-asserted-by":"crossref","DOI":"10.1073\/pnas.2500655122","article-title":"Characterizing population-level changes in human behavior during the COVID-19 pandemic in the United States","volume":"122","author":"T Urmi","year":"2025","journal-title":"Proc Natl Acad Sci U S A"},{"key":"pcbi.1014746.ref007","volume-title":"Report 9: Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand, vol. 16","author":"NM Ferguson","year":"2020"},{"issue":"8","key":"pcbi.1014746.ref008","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1007\/s11538-024-01324-x","article-title":"Mathematical assessment of the role of human behavior changes on SARS-CoV-2 transmission dynamics in the United States","volume":"86","author":"B Pant","year":"2024","journal-title":"Bull Math Biol"},{"issue":"2","key":"pcbi.1014746.ref009","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1016\/j.mbs.2005.03.006","article-title":"Effects of behavioral changes in a smallpox attack model","volume":"195","author":"S Del Valle","year":"2005","journal-title":"Math Biosci"},{"issue":"8","key":"pcbi.1014746.ref010","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0023084","article-title":"Towards a characterization of behavior-disease models","volume":"6","author":"N Perra","year":"2011","journal-title":"PLoS One"},{"issue":"5","key":"pcbi.1014746.ref011","doi-asserted-by":"crossref","DOI":"10.1093\/pnasnexus\/pgaf145","article-title":"Adaptive human behavior and delays in information availability autonomously modulate epidemic waves","volume":"4","author":"MS Mahmud","year":"2025","journal-title":"PNAS Nexus"},{"issue":"4","key":"pcbi.1014746.ref012","first-page":"1055","article-title":"Modeling the impact of hospitalization-induced behavioral changes on the spread of COVID-19 in New York City","volume":"10","author":"A Oveson","year":"2025","journal-title":"Infect Dis Model"},{"issue":"51","key":"pcbi.1014746.ref013","doi-asserted-by":"crossref","first-page":"32764","DOI":"10.1073\/pnas.2009911117","article-title":"Awareness-driven behavior changes can shift the shape of epidemics away from peaks and toward plateaus, shoulders, and oscillations","volume":"117","author":"JS Weitz","year":"2020","journal-title":"Proc Natl Acad Sci U S A"},{"issue":"24","key":"pcbi.1014746.ref014","doi-asserted-by":"crossref","DOI":"10.1073\/pnas.2421993122","article-title":"Comparative evaluation of behavioral epidemic models using COVID-19 data","volume":"122","author":"N Gozzi","year":"2025","journal-title":"Proc Natl Acad Sci U S A"},{"key":"pcbi.1014746.ref015","unstructured":"Jo Y, Sharbayta SS, Buonomo B. 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