Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US

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dc.creatorCramer, Estee
dc.creatorLopez, Velma
dc.creatorBracher, Johaness
dc.creatorBrennen, Andrea
dc.creatorCastro Rivadeneira, Alvaro J.
dc.creatorGerding, Aaron
dc.creatorGneiting, Tilmann
dc.creatorHouse, Katie H.
dc.creatorHuang, Yuxin
dc.creatorJayawardena, Dasuni
dc.creatorKanji, Abdul H.
dc.creatorKhandelwal, Ayush
dc.creatorLe, Khoa
dc.creatorMühlemann, Anja
dc.creatorNiemi, Jared
dc.creatorShah, Apurv
dc.creatorStark, Ariane
dc.creatorWang, Yijin
dc.creatorWattanachit, Nutcha
dc.creatorZorn, Martha W.
dc.creatorGu, Youyang
dc.creatorJain, Sansiddh
dc.creatorBannur, Nayana
dc.creatorDeva, Ayush
dc.creatorKulkarni, Mihir
dc.creatorMerugu, Srujana
dc.creatorRaval, Alpan
dc.creatorShingi, Siddhant
dc.creatorTiwari, Avtansh
dc.creatorWhite, Jerome
dc.creatorAbernethy, Neil F.
dc.creatorWoody, Spencer
dc.creatorDahan, Maytal
dc.creatorFox, Spencer J.
dc.creatorGaither, Kelly
dc.creatorLachmann, Michael
dc.creatorMeyers, Lauren, Ancel
dc.creatorScott, James
dc.creatorTec, Mauricio
dc.creatorSrivastava, Ajitesh
dc.creatorGeorge, Glover E.
dc.creatorCegan, Jeffrey C.
dc.creatorDettwiller, Ian D.
dc.creatorEngland, William P.
dc.creatorFarthing, Matthew W.
dc.creatorHunter, Robert H.
dc.creatorLafferty, Brandon
dc.creatorLinkov, Igor
dc.creatorMayo, Michael L.
dc.creatorParno, Matthew D.
dc.creatorRowland, Michael A.
dc.creatorTrump, Benjamin D.
dc.creatorZhang-James, Yanli
dc.creatorChen, Samuel
dc.creatorFaraone, Stephen V.
dc.creatorHess, Jonathan
dc.creatorMorley, Christopher P.
dc.creatorSalekin, Asif
dc.creatorWang, Dongliang
dc.creatorCorsetti, Sabrina M.
dc.date.accessioned2024-07-29T20:41:26Z
dc.date.available2024-07-29T20:41:26Z
dc.date.issued2022-04
dc.descriptionThis article contains supporting information online at http://www.pnas.org/lookup/suppl/doi:10.1073/pnas.2113561119/-/DCSupplemental.
dc.description.abstractShort-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multi-model ensemble forecast that combined predictions from dozens of different research groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-week horizon 3-5 times larger than when predicting at a 1-week horizon. This project underscores the role that collaboration and active coordination between governmental public health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.
dc.description.departmentIntegrative Biology
dc.identifier.doi10.1073/pnas.2113561119
dc.identifier.urihttps://hdl.handle.net/2152/126223
dc.identifier.urihttps://doi.org/10.26153/tsw/52760
dc.publisherPNAS
dc.relation.hasversionPre-print in TSW - https://hdl.handle.net/2152/126218
dc.relation.isversionofPre-print available at https://www.medrxiv.org/content/10.1101/2021.02.03.21250974v2
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectforecasting
dc.subjectCOVID-19
dc.subjectensemble forecast
dc.subjectmodel evaluation
dc.titleEvaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US
dc.typeJournalArticle

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