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.

Basic information

Original name

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

Authors

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 Czech Republic, guarantor, belonging to the institution), Andrea KRAUS (703 Slovakia, belonging to the institution), 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 and NG REICH

Edition

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

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

10103 Statistics and probability

Country of publisher

United States of America

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

Impact factor

Impact factor: 11.100

RIV identification code

RIV/00216224:14310/22:00126280

Organization unit

Faculty of Science

UT WoS

000819659900005

Keywords in English

forecasting; COVID-19; ensemble forecast; model evaluation

Tags

Tags

International impact, Reviewed
Změněno: 1/12/2022 17:07, Mgr. Marie Šípková, DiS.

Abstract

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.