{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T14:34:01Z","timestamp":1787063641208,"version":"build-2736575974"},"reference-count":96,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2025,7,12]],"date-time":"2025-07-12T00:00:00Z","timestamp":1752278400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"DOI":"10.13039\/100000001","name":"U.S. National Science Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Division of Mathematical Sciences and Division of Social and Economic Sciences","award":["2229819"],"award-info":[{"award-number":["2229819"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,12,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Human behavior influences the spread of infectious diseases, making it essential to integrate behavioral processes into epidemiological models. This became particularly evident during the COVID-19 pandemic, as many models did not incorporate behavior in response to policies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We reviewed modeling analyses of population dynamics in response to interventions intended to mitigate the spread of COVID-19 from February 2020 to February 2023. Key characteristics of each study were extracted, including the behavioral aspects integrated within the models and utilized databases.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We analyzed 276 COVID-19 modeling studies. Among them, only 38% attempted to incorporate human behavior. Even within this subset, behavioral integration was typically narrow, often limited to a single factor like compliance or mobility. We synthesized the identified behavioral factors into six categories. The majority (92%) of these studies employed a mechanistic modeling approach. Furthermore, only 34% of these studies used a database to model behavior.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Our review highlights a substantial gap in the incorporation of behavioral components into COVID-19 modeling studies. Limited models rely on databases, potentially compromising accuracy in reflecting the dynamic nature of human behavior. Our findings emphasize the necessity for future models to engage more deeply with behavioral sciences to enhance epidemiological modeling.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/pubmed\/fdaf082","type":"journal-article","created":{"date-parts":[[2025,7,12]],"date-time":"2025-07-12T13:12:49Z","timestamp":1752325969000},"page":"e568-e576","source":"Crossref","is-referenced-by-count":4,"title":["Review of human behavior integration in COVID-19 modeling studies"],"prefix":"10.1093","volume":"47","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-8803-0694","authenticated-orcid":false,"given":"Hannah","family":"Lee","sequence":"first","affiliation":[{"name":"MGH Institute for Technology Assessment, Harvard Medical School , Boston, MA ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hesam","family":"Mahmoudi","sequence":"additional","affiliation":[{"name":"MGH Institute for Technology Assessment, Harvard Medical School , Boston, MA 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