An open challenge to advance probabilistic forecasting for dengue epidemics

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dc.contributor.authorJohansson, Michael A.en
dc.contributor.authorApfeldorf, Karyn M.en
dc.contributor.authorDobson, Scotten
dc.contributor.authorDevita, Jasonen
dc.contributor.authorBuczak, Anna L.en
dc.contributor.authorBaugher, Benjaminen
dc.contributor.authorMoniz, Linda J.en
dc.contributor.authorBagley, Thomasen
dc.contributor.authorBabin, Steven M.en
dc.contributor.authorGuven, Erhanen
dc.contributor.authorYamana, Teresa K.en
dc.contributor.authorShaman, Jeffreyen
dc.contributor.authorMoschou, Terryen
dc.contributor.authorLothian, Nicken
dc.contributor.authorLane, Aaronen
dc.contributor.authorOsborne, Granten
dc.contributor.authorJiang, Gaoen
dc.contributor.authorBrooks, Logan C.en
dc.contributor.authorFarrow, David C.en
dc.contributor.authorHyun, Sangwonen
dc.contributor.authorTibshirani, Ryan J.en
dc.contributor.authorRosenfeld, Ronien
dc.contributor.authorLessler, Justinen
dc.contributor.authorReich, Nicholas G.en
dc.contributor.authorCummings, Derek AT T.en
dc.contributor.authorLauer, Stephen A.en
dc.contributor.authorMoore, Sean M.en
dc.contributor.authorClapham, Hannah E.en
dc.contributor.authorLowe, Rachelen
dc.contributor.authorBailey, Trevor C.en
dc.contributor.authorGarcia-Diez, Markelen
dc.contributor.authorCarvalho, Marilia Saen
dc.contributor.authorRodo, Xavieren
dc.contributor.authorSardar, Tridipen
dc.contributor.authorPaul, Richarden
dc.contributor.authorRay, Evan L.en
dc.contributor.authorSakrejda, Krzysztofen
dc.contributor.authorBrown, Alexandria C.en
dc.contributor.authorMeng, Xien
dc.contributor.authorOsoba, Osondeen
dc.contributor.authorVardavas, Raffaeleen
dc.contributor.authorManheim, Daviden
dc.contributor.authorMoore, Melindaen
dc.contributor.authorRao, Dhananjai M.en
dc.contributor.authorPorco, Travis C.en
dc.contributor.authorAckley, Sarahen
dc.contributor.authorLiu, Fengchenen
dc.contributor.authorWorden, Leeen
dc.contributor.authorConvertino, Matteoen
dc.contributor.authorLiu, Yangen
dc.contributor.authorReddy, Abrahamen
dc.contributor.authorOrtiz, Eloyen
dc.contributor.authorRivero, Jorgeen
dc.contributor.authorBrito, Humbertoen
dc.contributor.authorJuarrero, Aliciaen
dc.contributor.authorJohnson, Leah R.en
dc.contributor.authorGramacy, Robert B.en
dc.contributor.authorCohen, Jeremy M.en
dc.contributor.authorMordecai, Erin A.en
dc.contributor.authorMurdock, Courtney C.en
dc.contributor.authorRohr, Jason R.en
dc.contributor.authorRyan, Sadie J.en
dc.contributor.authorStewart-Ibarra, Anna M.en
dc.contributor.authorWeikel, Daniel P.en
dc.contributor.authorJutla, Antarpreeten
dc.contributor.authorKhan, Rakibulen
dc.contributor.authorPoultney, Marissaen
dc.contributor.authorColwell, Rita R.en
dc.contributor.authorRivera-Garcia, Brendaen
dc.contributor.authorBarker, Christopher M.en
dc.contributor.authorBell, Jesse E.en
dc.contributor.authorBiggerstaff, Matthewen
dc.contributor.authorSwerdlow, Daviden
dc.contributor.authorMier-y-Teran-Romero, Luisen
dc.contributor.authorForshey, Brett M.en
dc.contributor.authorTrtanj, Julien
dc.contributor.authorAsher, Jasonen
dc.contributor.authorClay, Matten
dc.contributor.authorMargolis, Harold S.en
dc.contributor.authorHebbeler, Andrew M.en
dc.contributor.authorGeorge, Dylanen
dc.contributor.authorChretien, Jean-Paulen
dc.date.accessioned2021-10-07T13:57:59Zen
dc.date.available2021-10-07T13:57:59Zen
dc.date.issued2019-11-26en
dc.date.updated2021-10-07T13:57:56Zen
dc.description.abstractA wide range of research has promised new tools for forecasting infectious disease dynamics, but little of that research is currently being applied in practice, because tools do not address key public health needs, do not produce probabilistic forecasts, have not been evaluated on external data, or do not provide sufficient forecast skill to be useful. We developed an open collaborative forecasting challenge to assess probabilistic forecasts for seasonal epidemics of dengue, a major global public health problem. Sixteen teams used a variety of methods and data to generate forecasts for 3 epidemiological targets (peak incidence, the week of the peak, and total incidence) over 8 dengue seasons in Iquitos, Peru and San Juan, Puerto Rico. Forecast skill was highly variable across teams and targets. While numerous forecasts showed high skill for midseason situational awareness, early season skill was low, and skill was generally lowest for high incidence seasons, those for which forecasts would be most valuable. A comparison of modeling approaches revealed that average forecast skill was lower for models including biologically meaningful data and mechanisms and that both multimodel and multiteam ensemble forecasts consistently outperformed individual model forecasts. Leveraging these insights, data, and the forecasting framework will be critical to improve forecast skill and the application of forecasts in real time for epidemic preparedness and response. Moreover, key components of this project-integration with public health needs, a common forecasting framework, shared and standardized data, and open participation-can help advance infectious disease forecasting beyond dengue.en
dc.description.versionPublished versionen
dc.format.extentPages 24268-24274en
dc.format.extent7 page(s)en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1073/pnas.1909865116en
dc.identifier.eissn1091-6490en
dc.identifier.issn0027-8424en
dc.identifier.issue48en
dc.identifier.orcidJohnson, Leah [0000-0002-9922-579X]en
dc.identifier.other1909865116 (PII)en
dc.identifier.pmid31712420en
dc.identifier.urihttp://hdl.handle.net/10919/105196en
dc.identifier.volume116en
dc.language.isoenen
dc.publisherNational Academy of Sciencesen
dc.relation.urihttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000499101100058&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=930d57c9ac61a043676db62af60056c1en
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectforecasten
dc.subjectdengueen
dc.subjectepidemicen
dc.subjectPeruen
dc.subjectPuerto Ricoen
dc.subjectDEPENDENT ENHANCEMENTen
dc.subjectBURDENen
dc.subjectPeruen
dc.subjectPuerto Ricoen
dc.subjectdengueen
dc.subjectepidemicen
dc.subjectforecasten
dc.subject.meshHumansen
dc.subject.meshDengueen
dc.subject.meshEpidemiologic Methodsen
dc.subject.meshIncidenceen
dc.subject.meshModels, Statisticalen
dc.subject.meshDisease Outbreaksen
dc.subject.meshPuerto Ricoen
dc.subject.meshPeruen
dc.subject.meshEpidemicsen
dc.titleAn open challenge to advance probabilistic forecasting for dengue epidemicsen
dc.title.serialProceedings of the National Academy of Sciences of the United States of Americaen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.otherArticleen
dc.type.otherJournalen
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/Scienceen
pubs.organisational-group/Virginia Tech/Science/Statisticsen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Science/COS T&R Facultyen

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