J 2022

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

CRAMER, EY, EL RAY, VK LOPEZ, J. BRACHER, A. BRENNEN et. al.

Základní údaje

Originální název

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

Autoři

CRAMER, EY, EL RAY, VK LOPEZ, J. BRACHER, A. BRENNEN, AJC RIVADENEIRA, A. GERDING, T. GNEITING, KH HOUSE, YX HUANG, D. JAYAWARDENA, AH KANJI, A. KHANDELWAL, K. LE, A. MUHLEMANN, J. NIEMI, A. SHAH, A. STARK, YJ WANG, N. WATTANACHIT, MW ZORN, Gu YY, S. JAIN, N. BANNUR, A. DEVA, M. KULKARNI, S. MERUGU, A. RAVAL, S. SHINGI, A. TIWARI, J. WHITE, NF ABERNETHY, S. WOODY, M. DAHAN, S. FOX, K. GAITHER, M. LACHMANN, LA MEYERS, JG SCOTT, M. TEC, A. SRIVASTAVA, GE GEORGE, JC CEGAN, ID DETTWILLER, WP ENGLAND, MW FARTHING, RH HUNTER, B. LAFFERTY, I. LINKOV, ML MAYO, MD PARNO, MA ROWLAND, BD TRUMP, Y. ZHANG-JAMES, S. CHEN, SV FARAONE, J. HESS, CP MORLEY, A. SALEKIN, DL WANG, SM CORSETTI, TM BAER, MC EISENBERG, K. FALB, YT HUANG, Martin ET, E. MCCAULEY, RL MYERS, T. SCHWARZ, D. SHELDON, GC GIBSON, R. YU, LY GAO, Y. MA, DX WU, XF YAN, XY JIN, YX WANG, YQ CHEN, LH GUO, YT ZHAO, QQ GU, JH CHEN, LX WANG, P. XU, WT ZHANG, DF ZOU, H. BIEGEL, J. LEGA, S. MCCONNELL, VP NAGRAJ, SL GUERTIN, C. HULME-LOWE, SD TURNER, YF SHI, XG BAN, R. WALRAVEN, QJ HONG, S. KONG, A. VAN DE WALLE, JA TURTLE, M. BEN-NUN, S. RILEY, P. RILEY, U. KOYLUOGLU, D. DESROCHES, P. FORLI, B. HAMORY, C. KYRIAKIDES, H. LEIS, J. MILLIKEN, M. MOLONEY, J. MORGAN, N. NIRGUDKAR, G. OZCAN, N. PIWONKA, M. RAVI, C. SCHRADER, E. SHAKHNOVICH, D. SIEGEL, R. SPATZ, C. STIEFELING, B. WILKINSON, A. WONG, S. CAVANY, G. ESPANA, S. MOORE, R. OIDTMAN, A. PERKINS, David KRAUS (203 Česká republika, garant, domácí), Andrea KRAUS (703 Slovensko, domácí), ZF GAO, J. BIAN, W. CAO, JL FERRES, CZ LI, TY LIU, X. XIE, S. ZHANG, S. ZHENG, A. VESPIGNANI, M. CHINAZZI, JT DAVIS, K. MU, APY PIONTTI, XY XIONG, A. ZHENG, J. BAEK, V. FARIAS, A. GEORGESCU, R. LEVI, D. SINHA, J. WILDE, G. PERAKIS, MA BENNOUNA, D. NZE-NDONG, D. SINGHVI, I. SPANTIDAKIS, L. THAYAPARAN, A. TSIOURVAS, A. SARKER, A. JADBABAIE, D. SHAH, N. DELLA PENNA, LA CELI, S. SUNDAR, R. WOLFINGER, D. OSTHUS, L. CASTRO, G. FAIRCHILD, I. MICHAUD, D. KARLEN, M. KINSEY, LC MULLANY, K. RAINWATER-LOVETT, L. SHIN, K. TALLAKSEN, S. WILSON, EC LEE, J. DENT, KH GRANTZ, AL HILL, J. KAMINSKY, K. KAMINSKY, LT KEEGAN, SA LAUER, JC LEMAITRE, J. LESSLER, HR MEREDITH, J. PEREZ-SAEZ, S. SHAH, CP SMITH, SA TRUELOVE, J. WILLS, M. MARSHALL, L. GARDNER, K. NIXON, JC BURANT, L. WANG, L. GAO, Gu ZL, M. KIM, XY LI, GN WANG, YY WANG, S. YU, RC REINER, R. BARBER, E. GAKIDOU, Hay SI, S. LIM, C. MURRAY, D. PIGOTT, HL GURUNG, P. BACCAM, SA STAGE, BT SUCHOSKI, BA PRAKASH, B. ADHIKARI, JM CUI, A. RODRIGUEZ, A. TABASSUM, JJ XIE, P. KESKINOCAK, J. ASPLUND, A. BAXTER, BE ORUC, N. SERBAN, SO ARIK, M. DUSENBERRY, A. EPSHTEYN, E. KANAL, Le LT, CL LI, T. PFISTER, D. SAVA, R. SINHA, T. TSAI, N. YODER, J. YOON, LY ZHANG, S. ABBOTT, NI BOSSE, S. FUNK, J. HELLEWELL, SR MEAKIN, K. SHERRATT, MY ZHOU, R. KALANTARI, TK YAMANA, S. PEI, J. SHAMAN, ML LI, D. BERTSIMAS, OS LAMI, S. SONI, HT BOUARDI, T. AYER, M. ADEE, J. CHHATWAL, OO DALGIC, MA LADD, BP LINAS, P. MUELLER, J. XIAO, YJ WANG, QX WANG, SH XIE, DL ZENG, A. GREEN, J. BIEN, L. BROOKS, AJ HU, M. JAHJA, D. MCDONALD, B. NARASIMHAN, C. POLITSCH, S. RAJANALA, A. RUMACK, N. SIMON, RJ TIBSHIRANI, R. TIBSHIRANI, V. VENTURA, L. WASSERMAN, EB O'DEA, JM DRAKE, R. PAGANO, QT TRAN, LST HO, H. HUYNH, JW WALKER, RB SLAYTON, MA JOHANSSON, M. BIGGERSTAFF a NG REICH

Vydání

Proceedings of the National Academy of Sciences of the United States of America, WASHINGTON, National Academy of Sciences, 2022, 0027-8424

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10103 Statistics and probability

Stát vydavatele

Spojené státy

Utajení

není předmětem státního či obchodního tajemství

Odkazy

Impakt faktor

Impact factor: 11.100

Kód RIV

RIV/00216224:14310/22:00126280

Organizační jednotka

Přírodovědecká fakulta

UT WoS

000819659900005

Klíčová slova anglicky

forecasting; COVID-19; ensemble forecast; model evaluation

Štítky

Příznaky

Mezinárodní význam, Recenzováno
Změněno: 1. 12. 2022 17:07, Mgr. Marie Šípková, DiS.

Anotace

V originále

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.