{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T21:26:13Z","timestamp":1769808373367,"version":"3.49.0"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1013820","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T00:00:00Z","timestamp":1769644800000}}],"reference-count":31,"publisher":"Public Library of Science (PLoS)","issue":"1","license":[{"start":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T00:00:00Z","timestamp":1768953600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001459","name":"Ministry of Education - Singapore","doi-asserted-by":"publisher","award":["Ministry of Education Start-Up Grant"],"award-info":[{"award-number":["Ministry of Education Start-Up Grant"]}],"id":[{"id":"10.13039\/501100001459","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001459","name":"Ministry of Education - Singapore","doi-asserted-by":"publisher","award":["RT4\/22"],"award-info":[{"award-number":["RT4\/22"]}],"id":[{"id":"10.13039\/501100001459","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001459","name":"Ministry of Education - Singapore","doi-asserted-by":"publisher","award":["RS04\/22"],"award-info":[{"award-number":["RS04\/22"]}],"id":[{"id":"10.13039\/501100001459","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"\n Accurate real-time estimation of the effective reproduction number (R\n t<\/jats:sub>\n ) is critical for infectious disease surveillance and response. In vector-borne diseases like dengue, temperature strongly influences disease transmission by affecting generation times. However, most existing R\n t<\/jats:sub>\n estimation methods assume a fixed generation interval, leading to biased estimates and unreliable assessments of transmission risk in settings with fluctuating temperatures. In this study, we proposed and evaluated a novel framework to estimate a temperature-dependent reproduction number (td-R\n t<\/jats:sub>\n ) that dynamically updates the generation interval distribution based on observed temperature data. We obtained real-time estimates of td-R\n t<\/jats:sub>\n through an adapted Bayesian recursive filtering process. Using real and simulated data for a temperature-sensitive disease (dengue), we evaluated the performance of td-R\n t<\/jats:sub>\n against the typically used temperature-independent reproduction number (ti-R\n t<\/jats:sub>\n ) and angular reproduction number (\n \n \n \n \n \n \n \n \u03a9<\/mml:mi>\n <\/mml:mrow>\n \n \n t<\/mml:mi>\n <\/mml:mrow>\n <\/mml:mrow>\n <\/mml:msub>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n ), which does not require specification of the generation interval. Simulated data was generated under varying patterns of underlying R\n t<\/jats:sub>\n and temperature datasets. Performance was evaluated by classification accuracy, defined by the proportion of instances where estimated R\n t<\/jats:sub>\n correctly identified whether the true R\n t<\/jats:sub>\n was above or below 1. We found that td-R\n t<\/jats:sub>\n generally outperformed ti-R\n t<\/jats:sub>\n and\n \n \n \n \n \n \n \n \u03a9<\/mml:mi>\n <\/mml:mrow>\n \n \n t<\/mml:mi>\n <\/mml:mrow>\n <\/mml:mrow>\n <\/mml:msub>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n in classifying periods of epidemic growth. td-R\n t<\/jats:sub>\n achieved the highest classification accuracy in 54 of 72 simulation scenarios, with accuracy ranging from 37.1%\u201395.9%. td-R\n t<\/jats:sub>\n accuracy was highest in scenarios with greater temperature variability, surpassing other methods by up to 20%. With Singapore dengue case data, td-R\n t<\/jats:sub>\n and\n \n \n \n \n \n \n \n \u03a9<\/mml:mi>\n <\/mml:mrow>\n \n \n t<\/mml:mi>\n <\/mml:mrow>\n <\/mml:mrow>\n <\/mml:msub>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n signals showed 75% similarity, highlighting\n \n \n \n \n \n \n \n \u03a9<\/mml:mi>\n <\/mml:mrow>\n \n \n t<\/mml:mi>\n <\/mml:mrow>\n <\/mml:mrow>\n <\/mml:msub>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n \u2019s potential as a complementary measure that is less sensitive to model assumptions. These findings highlight the importance of accounting for temperature in real-time transmissibility estimates, as temperature-driven variations in generation time can introduce model misspecification and bias. Incorporating temperature is especially crucial for climate-sensitive diseases like dengue. Future work could extend this framework to other pathogens and additional transmission-relevant covariates.\n <\/jats:p>","DOI":"10.1371\/journal.pcbi.1013820","type":"journal-article","created":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T18:41:56Z","timestamp":1769020916000},"page":"e1013820","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Measuring real-time disease transmissibility with temperature-dependent generation intervals"],"prefix":"10.1371","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3165-8895","authenticated-orcid":true,"given":"Esther Li Wen","family":"Choo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kris V.","family":"Parag","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jo Yi","family":"Chow","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jue Tao","family":"Lim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2026,1,21]]},"reference":[{"issue":"6","key":"pcbi.1013820.ref001","doi-asserted-by":"crossref","first-page":"405","DOI":"10.3961\/jpmph.20.288","article-title":"Interpretation of the basic and effective reproduction number","volume":"53","author":"J-S Lim","year":"2020","journal-title":"J Prev Med Public Health"},{"issue":"6","key":"pcbi.1013820.ref002","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1093\/aje\/kwh255","article-title":"Different epidemic curves for severe acute respiratory syndrome reveal similar impacts of control measures","volume":"160","author":"J Wallinga","year":"2004","journal-title":"Am J Epidemiol"},{"key":"pcbi.1013820.ref003","doi-asserted-by":"crossref","first-page":"112","DOI":"10.12688\/wellcomeopenres.16006.2","article-title":"Estimating the time-varying reproduction number of SARS-CoV-2 using national and subnational case counts","volume":"5","author":"S Abbott","year":"2020","journal-title":"Wellcome Open Res"},{"issue":"9","key":"pcbi.1013820.ref004","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1009347","article-title":"Improved estimation of time-varying reproduction numbers at low case incidence and between epidemic waves","volume":"17","author":"KV Parag","year":"2021","journal-title":"PLoS Comput Biol"},{"issue":"8","key":"pcbi.1013820.ref005","article-title":"Estimating the epidemic reproduction number from temporally aggregated incidence data: a statistical modelling approach and software tool","volume":"19","author":"RK Nash","year":"2023","journal-title":"PLoS Comput Biol"},{"issue":"2007","key":"pcbi.1013820.ref006","doi-asserted-by":"crossref","first-page":"20231664","DOI":"10.1098\/rspb.2023.1664","article-title":"Angular reproduction numbers improve estimates of transmissibility when disease generation times are misspecified or time-varying","volume":"290","author":"KV Parag","year":"2023","journal-title":"Proc R Soc B Biol Sci"},{"issue":"6507","key":"pcbi.1013820.ref007","doi-asserted-by":"crossref","first-page":"1106","DOI":"10.1126\/science.abc9004","article-title":"Serial interval of SARS-CoV-2 was shortened over time by nonpharmaceutical interventions","volume":"369","author":"ST Ali","year":"2020","journal-title":"Science"},{"key":"pcbi.1013820.ref008","article-title":"Renewal equations for vector-borne diseases [Internet]","author":"C Mills","year":"2024","journal-title":"arXiv"},{"key":"pcbi.1013820.ref009","doi-asserted-by":"crossref","first-page":"100382","DOI":"10.1016\/j.epidem.2019.100382","article-title":"Temperature-dependent variation in the extrinsic incubation period elevates the risk of vector-borne disease emergence","volume":"30","author":"T Kamiya","year":"2020","journal-title":"Epidemics"},{"issue":"11","key":"pcbi.1013820.ref010","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0050972","article-title":"The incubation periods of Dengue viruses","volume":"7","author":"M Chan","year":"2012","journal-title":"PLoS One"},{"key":"pcbi.1013820.ref011","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.epidem.2018.05.011","article-title":"Estimating the effective reproduction number of dengue considering temperature-dependent generation intervals","volume":"25","author":"CT Code\u00e7o","year":"2018","journal-title":"Epidemics"},{"key":"pcbi.1013820.ref012","unstructured":"Meteorological Service Singapore. 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