Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States.
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Evan L Ray ;
Velma K Lopez ;
Johannes Bracher ;
Andrea Brennen;
Alvaro J Castro Rivadeneira;
Aaron Gerding;
Tilmann Gneiting ;
Katie H House;
Yuxin Huang;
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Dasuni Jayawardena;
Abdul H Kanji;
Ayush Khandelwal;
Khoa Le;
Anja Mühlemann;
Jarad Niemi ;
Apurv Shah;
Ariane Stark;
Yijin Wang;
Nutcha Wattanachit;
Martha W Zorn;
Youyang Gu;
Sansiddh Jain;
Nayana Bannur;
Ayush Deva;
Mihir Kulkarni;
Srujana Merugu;
Alpan Raval;
Siddhant Shingi;
Avtansh Tiwari;
Jerome White ;
Neil F Abernethy;
Spencer Woody ;
Maytal Dahan;
Spencer Fox ;
Kelly Gaither ;
Michael Lachmann;
Lauren Ancel Meyers ;
James G Scott;
Mauricio Tec ;
Ajitesh Srivastava;
Glover E George ;
Jeffrey C Cegan ;
Ian D Dettwiller;
William P England;
Matthew W Farthing;
Robert H Hunter ;
Brandon Lafferty ;
Igor Linkov;
Michael L Mayo ;
Matthew D Parno;
Michael A Rowland ;
Benjamin D Trump;
Yanli Zhang-James;
Samuel Chen ;
Stephen V Faraone;
Jonathan Hess;
Christopher P Morley;
Asif Salekin ;
Dongliang Wang;
Sabrina M Corsetti ;
Thomas M Baer;
Marisa C Eisenberg;
Karl Falb ;
Yitao Huang ;
Emily T Martin;
Ella McCauley;
Robert L Myers;
Tom Schwarz;
Daniel Sheldon ;
Graham Casey Gibson;
Rose Yu;
Liyao Gao;
Yian Ma;
Dongxia Wu;
Xifeng Yan;
Xiaoyong Jin;
Yu-Xiang Wang;
YangQuan Chen;
Lihong Guo ;
Yanting Zhao;
Quanquan Gu ;
Jinghui Chen;
Lingxiao Wang;
Pan Xu ;
Weitong Zhang;
Difan Zou;
Hannah Biegel;
Joceline Lega ;
Steve McConnell ;
VP Nagraj;
Stephanie L Guertin;
Christopher Hulme-Lowe;
Stephen D Turner ;
Yunfeng Shi ;
Xuegang Ban;
Robert Walraven ;
Qi-Jun Hong;
Stanley Kong;
Axel van de Walle ;
James A Turtle ;
Michal Ben-Nun ;
Steven Riley ;
Pete Riley;
Ugur Koyluoglu ;
David DesRoches;
Pedro Forli;
Bruce Hamory;
Christina Kyriakides;
Helen Leis;
John Milliken;
Michael Moloney;
James Morgan;
Ninad Nirgudkar;
Gokce Ozcan;
Noah Piwonka;
Matt Ravi;
Chris Schrader;
Elizabeth Shakhnovich;
Daniel Siegel;
Ryan Spatz;
Chris Stiefeling;
Barrie Wilkinson;
Alexander Wong;
Sean Cavany ;
Guido España ;
Sean Moore ;
Rachel Oidtman ;
Alex Perkins ;
David Kraus ;
Andrea Kraus;
Zhifeng Gao;
Jiang Bian;
Wei Cao ;
Juan Lavista Ferres ;
Chaozhuo Li;
Tie-Yan Liu;
Xing Xie;
Shun Zhang;
Shun Zheng;
Alessandro Vespignani ;
Matteo Chinazzi;
Jessica T Davis;
Kunpeng Mu;
Ana Pastore Y Piontti;
Xinyue Xiong;
Andrew Zheng;
Jackie Baek;
Vivek Farias;
Andreea Georgescu;
Retsef Levi;
Deeksha Sinha ;
Joshua Wilde;
Georgia Perakis ;
Mohammed Amine Bennouna ;
David Nze-Ndong;
Divya Singhvi;
Ioannis Spantidakis ;
Leann Thayaparan;
Asterios Tsiourvas ;
Arnab Sarker ;
Ali Jadbabaie ;
Devavrat Shah ;
Nicolas Della Penna;
Leo A Celi ;
Saketh Sundar;
Russ Wolfinger;
Dave Osthus ;
Lauren Castro;
Geoffrey Fairchild ;
Isaac Michaud;
Dean Karlen;
Matt Kinsey;
Luke C Mullany ;
Kaitlin Rainwater-Lovett ;
Lauren Shin;
Katharine Tallaksen;
Shelby Wilson;
Elizabeth C Lee ;
Juan Dent ;
Kyra H Grantz;
Alison L Hill ;
Joshua Kaminsky;
Kathryn Kaminsky;
Lindsay T Keegan ;
Stephen A Lauer;
Joseph C Lemaitre ;
Justin Lessler;
Hannah R Meredith;
Javier Perez-Saez;
Sam Shah;
Claire P Smith;
Shaun A Truelove ;
Josh Wills ;
Maximilian Marshall;
Lauren Gardner;
Kristen Nixon;
John C Burant;
Lily Wang;
Lei Gao ;
Zhiling Gu ;
Myungjin Kim;
Xinyi Li;
Guannan Wang;
Yueying Wang;
Shan Yu ;
Robert C Reiner;
Ryan Barber;
Emmanuela Gakidou;
Simon I Hay ;
Steve Lim;
Chris Murray ;
David Pigott;
Heidi L Gurung;
Prasith Baccam;
Steven A Stage ;
Bradley T Suchoski;
B Aditya Prakash ;
Bijaya Adhikari;
Jiaming Cui;
Alexander Rodríguez ;
Anika Tabassum;
Jiajia Xie ;
Pinar Keskinocak ;
John Asplund;
Arden Baxter ;
Buse Eylul Oruc ;
Nicoleta Serban;
Sercan O Arik;
Mike Dusenberry;
Arkady Epshteyn;
Elli Kanal;
Long T Le;
Chun-Liang Li;
Tomas Pfister;
Dario Sava;
Rajarishi Sinha ;
Thomas Tsai;
Nate Yoder ;
Jinsung Yoon;
Leyou Zhang ;
Sam Abbott;
Nikos I Bosse;
Sebastian Funk ;
Joel Hellewell;
Sophie R Meakin ;
Katharine Sherratt ;
Mingyuan Zhou;
Rahi Kalantari;
Teresa K Yamana ;
Sen Pei ;
Jeffrey Shaman ;
Michael L Li ;
Dimitris Bertsimas ;
Omar Skali Lami ;
Saksham Soni ;
Hamza Tazi Bouardi ;
Turgay Ayer;
Madeline Adee;
Jagpreet Chhatwal;
Ozden O Dalgic;
Mary A Ladd;
Benjamin P Linas;
Peter Mueller;
Jade Xiao;
Yuanjia Wang ;
Qinxia Wang;
Shanghong Xie;
Donglin Zeng;
Alden Green;
Jacob Bien;
Logan Brooks;
Addison J Hu;
Maria Jahja;
Daniel McDonald ;
Balasubramanian Narasimhan;
Collin Politsch ;
Samyak Rajanala ;
Aaron Rumack ;
Noah Simon;
Ryan J Tibshirani ;
Rob Tibshirani;
Valerie Ventura;
Larry Wasserman;
Eamon B O'Dea;
John M Drake ;
Robert Pagano;
Quoc T Tran;
Lam Si Tung Ho ;
Huong Huynh;
Jo W Walker;
Rachel B Slayton ;
Michael A Johansson ;
Matthew Biggerstaff ;
Nicholas G Reich ;
(2022)
Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States.
Proceedings of the National Academy of Sciences, 119 (15).
e2113561119-.
ISSN 0027-8424
DOI: 10.1073/pnas.2113561119
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 naïve 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.
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