Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States
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CRAMER EY RAY EL LOPEZ VK BRACHER J. BRENNEN A. RIVADENEIRA AJC GERDING A. GNEITING T. HOUSE KH HUANG YX JAYAWARDENA D. KANJI AH KHANDELWAL A. LE K. MUHLEMANN A. NIEMI J. SHAH A. STARK A. WANG YJ WATTANACHIT N. ZORN MW YY Gu JAIN S. BANNUR N. DEVA A. KULKARNI M. MERUGU S. RAVAL A. SHINGI S. TIWARI A. WHITE J. ABERNETHY NF WOODY S. DAHAN M. FOX S. GAITHER K. LACHMANN M. MEYERS LA SCOTT JG TEC M. SRIVASTAVA A. GEORGE GE CEGAN JC DETTWILLER ID ENGLAND WP FARTHING MW HUNTER RH LAFFERTY B. LINKOV I. MAYO ML PARNO MD ROWLAND MA TRUMP BD ZHANG-JAMES Y. CHEN S. FARAONE SV HESS J. MORLEY CP SALEKIN A. WANG DL CORSETTI SM BAER TM EISENBERG MC FALB K. HUANG YT ET Martin MCCAULEY E. MYERS RL SCHWARZ T. SHELDON D. GIBSON GC YU R. GAO LY MA Y. WU DX YAN XF JIN XY WANG YX CHEN YQ GUO LH ZHAO YT GU QQ CHEN JH WANG LX XU P. ZHANG WT ZOU DF BIEGEL H. LEGA J. MCCONNELL S. NAGRAJ VP GUERTIN SL HULME-LOWE C. TURNER SD SHI YF BAN XG WALRAVEN R. HONG QJ KONG S. VAN DE WALLE A. TURTLE JA BEN-NUN M. RILEY S. RILEY P. KOYLUOGLU U. DESROCHES D. FORLI P. HAMORY B. KYRIAKIDES C. LEIS H. MILLIKEN J. MOLONEY M. MORGAN J. NIRGUDKAR N. OZCAN G. PIWONKA N. RAVI M. SCHRADER C. SHAKHNOVICH E. SIEGEL D. SPATZ R. STIEFELING C. WILKINSON B. WONG A. CAVANY S. ESPANA G. MOORE S. OIDTMAN R. PERKINS A. KRAUS David KRAUS Andrea GAO ZF BIAN J. CAO W. FERRES JL LI CZ LIU TY XIE X. ZHANG S. ZHENG S. VESPIGNANI A. CHINAZZI M. DAVIS JT MU K. PIONTTI APY XIONG XY ZHENG A. BAEK J. FARIAS V. GEORGESCU A. LEVI R. SINHA D. WILDE J. PERAKIS G. BENNOUNA MA NZE-NDONG D. SINGHVI D. SPANTIDAKIS I. THAYAPARAN L. TSIOURVAS A. SARKER A. JADBABAIE A. SHAH D. DELLA PENNA N. CELI LA SUNDAR S. WOLFINGER R. OSTHUS D. CASTRO L. FAIRCHILD G. MICHAUD I. KARLEN D. KINSEY M. MULLANY LC RAINWATER-LOVETT K. SHIN L. TALLAKSEN K. WILSON S. LEE EC DENT J. GRANTZ KH HILL AL KAMINSKY J. KAMINSKY K. KEEGAN LT LAUER SA LEMAITRE JC LESSLER J. MEREDITH HR PEREZ-SAEZ J. SHAH S. SMITH CP TRUELOVE SA WILLS J. MARSHALL M. GARDNER L. NIXON K. BURANT JC WANG L. GAO L. ZL Gu KIM M. LI XY WANG GN WANG YY YU S. REINER RC BARBER R. GAKIDOU E. SI Hay LIM S. MURRAY C. PIGOTT D. GURUNG HL BACCAM P. STAGE SA SUCHOSKI BT PRAKASH BA ADHIKARI B. CUI JM RODRIGUEZ A. TABASSUM A. XIE JJ KESKINOCAK P. ASPLUND J. BAXTER A. ORUC BE SERBAN N. ARIK SO DUSENBERRY M. EPSHTEYN A. KANAL E. LT Le LI CL PFISTER T. SAVA D. SINHA R. TSAI T. YODER N. YOON J. ZHANG LY ABBOTT S. BOSSE NI FUNK S. HELLEWELL J. MEAKIN SR SHERRATT K. ZHOU MY KALANTARI R. YAMANA TK PEI S. SHAMAN J. LI ML BERTSIMAS D. LAMI OS SONI S. BOUARDI HT AYER T. ADEE M. CHHATWAL J. DALGIC OO LADD MA LINAS BP MUELLER P. XIAO J. WANG YJ WANG QX XIE SH ZENG DL GREEN A. BIEN J. BROOKS L. HU AJ JAHJA M. MCDONALD D. NARASIMHAN B. POLITSCH C. RAJANALA S. RUMACK A. SIMON N. TIBSHIRANI RJ TIBSHIRANI R. VENTURA V. WASSERMAN L. O'DEA EB DRAKE JM PAGANO R. TRAN QT HO LST HUYNH H. WALKER JW SLAYTON RB JOHANSSON MA BIGGERSTAFF M. REICH NG |
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| Rok publikování | 2022 |
| Druh | Článek v odborném periodiku |
| Časopis / Zdroj | Proceedings of the National Academy of Sciences of the United States of America |
| Fakulta / Pracoviště MU | |
| Citace | |
| www | http://dx.doi.org/10.1073/pnas.2113561119 |
| Doi | http://dx.doi.org/10.1073/pnas.2113561119 |
| Klíčová slova | forecasting; COVID-19; ensemble forecast; model evaluation |
| Popis | Short-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 multimodel ensemble forecast that combined predictions from dozens of 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 naive baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk 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. |