{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,12]],"date-time":"2026-11-12T05:48:08Z","timestamp":1794462488540,"version":"4.9.1"},"reference-count":49,"publisher":"National Academy of Sciences","issue":"33","license":[{"start":{"date-parts":[[2025,8,13]],"date-time":"2025-08-13T00:00:00Z","timestamp":1755043200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000030","name":"HHS | Centers for Disease Control and Prevention","doi-asserted-by":"publisher","award":["200-2016-91779"],"award-info":[{"award-number":["200-2016-91779"]}],"id":[{"id":"10.13039\/100000030","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000030","name":"HHS | Centers for Disease Control and Prevention","doi-asserted-by":"publisher","award":["CDC-RFA-FT-23-0069"],"award-info":[{"award-number":["CDC-RFA-FT-23-0069"]}],"id":[{"id":"10.13039\/100000030","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000057","name":"HHS | NIH | National Institute of General Medical Sciences","doi-asserted-by":"publisher","award":["R01GM130668"],"award-info":[{"award-number":["R01GM130668"]}],"id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.pnas.org"],"crossmark-restriction":true},"short-container-title":["Proc. Natl. Acad. Sci. U.S.A."],"published-print":{"date-parts":[[2025,8,19]]},"abstract":"<jats:p>Dengue fever, a tropical vector-borne disease, is a leading cause of hospitalization and death in many parts of the world, especially in Asia and Latin America. Where timely dengue surveillance exists, decision-makers can better implement public health measures and allocate resources. Reliable near-term forecasts may help anticipate healthcare demands and promote preparedness. We propose ensemble modeling approaches combining mechanistic, statistical, and machine learning models to forecast dengue cases 1 to 3 mo ahead at the province level across multiple countries. We assess these models\u2019 predictive ability out-of-sample and retrospectively in over 180 locations worldwide, including provinces in Brazil, Colombia, Malaysia, Mexico, Thailand, plus Iquitos, Peru, and San Juan, Puerto Rico, during at least 2 to 3 y. We also evaluate ensemble approaches in a real-time, prospective dengue forecasting platform during 2022\u20132023, considering data availability limitations. Our ensemble modeling leads to an improvement to previous efforts that may help decision-making in the context of large uncertainties. This contrasts with the variable performance of individual component models across locations and time. No single model achieves optimal predictions across all scenarios, but while ensemble models may not always perform best in specific locations, they consistently rank among the top 3 performing models both retrospectively and prospectively.<\/jats:p>","DOI":"10.1073\/pnas.2422335122","type":"journal-article","created":{"date-parts":[[2025,8,13]],"date-time":"2025-08-13T17:38:58Z","timestamp":1755106738000},"update-policy":"https:\/\/doi.org\/10.1073\/pnas.cm10313","source":"Crossref","is-referenced-by-count":7,"title":["Ensemble approaches for short-term dengue fever forecasts: A global evaluation study"],"prefix":"10.1073","volume":"122","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4311-0421","authenticated-orcid":false,"given":"Skyler","family":"Wu","sequence":"first","affiliation":[{"name":"Department of Statistics","place":["Cambridge"]},{"name":"Harvard University","place":["Cambridge"]},{"name":"Machine Intelligence Group for the Betterment of Health and the Environment","place":["Boston"]},{"name":"Network Science Institute","place":["Boston"]},{"name":"Department of Physics","place":["Boston"]},{"name":"Northeastern University","place":["Boston"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0581-800X","authenticated-orcid":false,"given":"Austin G.","family":"Meyer","sequence":"additional","affiliation":[{"name":"Machine Intelligence Group for the Betterment of Health and the Environment","place":["Boston"]},{"name":"Network Science Institute","place":["Boston"]},{"name":"Department of Physics","place":["Boston"]},{"name":"Northeastern University","place":["Boston"]},{"name":"Division of Hospital Medicine","place":["Temple"]},{"name":"Department of Pediatrics","place":["Temple"]},{"name":"Baylor College of Medicine","place":["Temple"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leonardo","family":"Clemente","sequence":"additional","affiliation":[{"name":"Machine Intelligence Group for the Betterment of Health and the Environment","place":["Boston"]},{"name":"Network Science Institute","place":["Boston"]},{"name":"Department of Physics","place":["Boston"]},{"name":"Northeastern University","place":["Boston"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6313-0126","authenticated-orcid":false,"given":"Lucas M.","family":"Stolerman","sequence":"additional","affiliation":[{"name":"Department of Mathematics","place":["Stillwater"]},{"name":"Oklahoma State University","place":["Stillwater"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fred","family":"Lu","sequence":"additional","affiliation":[{"name":"Machine Intelligence Group for the Betterment of Health and the Environment","place":["Boston"]},{"name":"Network Science Institute","place":["Boston"]},{"name":"Department of Physics","place":["Boston"]},{"name":"Northeastern University","place":["Boston"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Atreyee","family":"Majumder","sequence":"additional","affiliation":[{"name":"Global Public Health","place":["Beerse, Belgium"]},{"name":"Janssen R&D","place":["Beerse, Belgium"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5445-6095","authenticated-orcid":false,"given":"Rudi","family":"Verbeeck","sequence":"additional","affiliation":[{"name":"Global Public Health","place":["Beerse, Belgium"]},{"name":"Janssen R&D","place":["Beerse, Belgium"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4296-1809","authenticated-orcid":false,"given":"Serge","family":"Masyn","sequence":"additional","affiliation":[{"name":"Global Public Health","place":["Beerse, Belgium"]},{"name":"Janssen R&D","place":["Beerse, Belgium"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4206-418X","authenticated-orcid":false,"given":"Mauricio","family":"Santillana","sequence":"additional","affiliation":[{"name":"Machine Intelligence Group for the Betterment of Health and the Environment","place":["Boston"]},{"name":"Network Science Institute","place":["Boston"]},{"name":"Department of Physics","place":["Boston"]},{"name":"Northeastern University","place":["Boston"]},{"name":"Department of Epidemiology","place":["Boston"]},{"name":"Harvard T.H. Chan School of Public Health","place":["Boston"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"341","published-online":{"date-parts":[[2025,8,13]]},"reference":[{"key":"e_1_3_4_1_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1005607"},{"key":"e_1_3_4_2_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pntd.0001760"},{"key":"e_1_3_4_3_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-79438-0"},{"key":"e_1_3_4_4_2","doi-asserted-by":"publisher","DOI":"10.1038\/nature12060"},{"key":"e_1_3_4_5_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pntd.0002713"},{"key":"e_1_3_4_6_2","doi-asserted-by":"publisher","DOI":"10.4269\/ajtmh.13-0321"},{"key":"e_1_3_4_7_2","unstructured":"World Health Organization Dengue and severe dengue. https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/dengue-and-severe-dengue. Accessed 25 July 2025."},{"key":"e_1_3_4_8_2","doi-asserted-by":"publisher","DOI":"10.2149\/tmh.2011-S10"},{"key":"e_1_3_4_9_2","unstructured":"C. Kusiak Real-time Dengue forecasting in Thailand: A comparison of penalized regression approaches using internet search data Master\u2019s Thesis University of California Berkeley CA (2018)."},{"key":"e_1_3_4_10_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1010218"},{"key":"e_1_3_4_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3129997"},{"key":"e_1_3_4_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.vaccine.2011.06.042"},{"key":"e_1_3_4_13_2","doi-asserted-by":"publisher","DOI":"10.1098\/rsif.2012.0921"},{"key":"e_1_3_4_14_2","doi-asserted-by":"crossref","unstructured":"Z. R. D. Omadlao \u201cMachine learning-based Dengue forecasting system for Irisan Baguio City Philippines\u201d in AIP Conference Proceedings (AIP Publishing LLC 2022) vol. 2472 p. 040019.","DOI":"10.1063\/5.0092930"},{"key":"e_1_3_4_15_2","doi-asserted-by":"publisher","DOI":"10.1098\/rsif.2020.1006"},{"key":"e_1_3_4_16_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0220106"},{"key":"e_1_3_4_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.116324"},{"key":"e_1_3_4_18_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0195065"},{"key":"e_1_3_4_19_2","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1909865116"},{"key":"e_1_3_4_20_2","doi-asserted-by":"publisher","DOI":"10.3390\/pr10112454"},{"key":"e_1_3_4_21_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-84176-y"},{"key":"e_1_3_4_22_2","first-page":"11","article-title":"Exponential smoothing on forecasting Dengue cases in Colombo","volume":"11","author":"Attanayake A.","year":"2020","unstructured":"A. Attanayake, S. Perera, U. Liyanage, Exponential smoothing on forecasting Dengue cases in Colombo. Sri Lanka. J. Sci. 11, 11\u201322 (2020).","journal-title":"Sri Lanka. J. Sci."},{"key":"e_1_3_4_23_2","doi-asserted-by":"publisher","DOI":"10.3390\/biology11020169"},{"key":"e_1_3_4_24_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pntd.0010071"},{"key":"e_1_3_4_25_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1007518"},{"key":"e_1_3_4_26_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.scitotenv.2018.08.044"},{"key":"e_1_3_4_27_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pntd.0005729"},{"key":"e_1_3_4_28_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-021-25695-0"},{"key":"e_1_3_4_29_2","unstructured":"K. Kempfert Time series methods and ensemble models to nowcast Dengue at the state level in Brazil. arXiv [Preprint] (2020). https:\/\/arxiv.org\/abs\/2006.02483 (Accessed 1 January 2025)."},{"key":"e_1_3_4_30_2","first-page":"822","article-title":"An ensemble model for forecasting infectious diseases in India","volume":"36","author":"Shashvat K.","year":"2019","unstructured":"K. Shashvat, R. Basu, P. Bhondekar, A. Kaur, An ensemble model for forecasting infectious diseases in India. Trop Biomed 36, 822\u2013832 (2019).","journal-title":"Trop Biomed"},{"key":"e_1_3_4_31_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0189988"},{"key":"e_1_3_4_32_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pmed.1003542"},{"key":"e_1_3_4_33_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2019.121266"},{"key":"e_1_3_4_34_2","doi-asserted-by":"publisher","DOI":"10.3390\/math10101714"},{"key":"e_1_3_4_35_2","doi-asserted-by":"crossref","unstructured":"N. Kerdprasop K. Kerdorasop P. Chuaybamroong \u201cA multi-criteria scheme to build model ensemble for Dengue infection case estimation\u201d in 2020 International Conference on Decision Aid Sciences and Application (DASA) (IEEE 2020) pp. 214\u2013218.","DOI":"10.1109\/DASA51403.2020.9317204"},{"key":"e_1_3_4_36_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-024-50601-9"},{"key":"e_1_3_4_37_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1009570"},{"key":"e_1_3_4_38_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-018-08082-0"},{"key":"e_1_3_4_39_2","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.2207537120"},{"key":"e_1_3_4_40_2","doi-asserted-by":"publisher","DOI":"10.3389\/fpubh.2018.00336"},{"key":"e_1_3_4_41_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1007735"},{"key":"e_1_3_4_42_2","doi-asserted-by":"crossref","unstructured":"D. Gamerman M. O. Prates T. Paiva V. D. Mayrink Building a Platform for Data-Driven Pandemic Prediction: From Data Modelling to Visualisation - the CovidLP Project (CRC Press 2021).","DOI":"10.1201\/9781003148883"},{"key":"e_1_3_4_43_2","unstructured":"H. Kamarthi A. Rodr\u00edguez B. A. Prakash Back2future: Leveraging backfill dynamics for improving real-time predictions in future. arXiv [Preprint] (2021). https:\/\/arxiv.org\/abs\/2106.04420 (Accessed 1 January 2025)."},{"key":"e_1_3_4_44_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1006599"},{"key":"e_1_3_4_45_2","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1515373112"},{"key":"e_1_3_4_46_2","unstructured":"W. Nicholson D. Matteson J. Bien BigVAR: Tools for modeling sparse high-dimensional multivariate time series. arXiv [Preprint] (2017). http:\/\/arxiv.org\/abs\/1702.07094 (Accessed 1 January 2025)."},{"key":"e_1_3_4_47_2","doi-asserted-by":"publisher","DOI":"10.1038\/srep33707"},{"key":"e_1_3_4_48_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pntd.0008056"},{"key":"e_1_3_4_49_2","doi-asserted-by":"publisher","DOI":"10.1126\/sciadv.abb1237"}],"container-title":["Proceedings of the National Academy of Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/pnas.org\/doi\/pdf\/10.1073\/pnas.2422335122","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,13]],"date-time":"2025-08-13T17:39:14Z","timestamp":1755106754000},"score":1,"resource":{"primary":{"URL":"https:\/\/pnas.org\/doi\/10.1073\/pnas.2422335122"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,13]]},"references-count":49,"journal-issue":{"issue":"33","published-print":{"date-parts":[[2025,8,19]]}},"alternative-id":["10.1073\/pnas.2422335122"],"URL":"https:\/\/doi.org\/10.1073\/pnas.2422335122","relation":{},"ISSN":["0027-8424","1091-6490"],"issn-type":[{"value":"0027-8424","type":"print"},{"value":"1091-6490","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,13]]},"assertion":[{"value":"2024-11-12","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-06-27","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-08-13","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e2422335122"}}