Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States.
;
Evan L Ray ;
Velma K Lopez ;
Johannes Bracher ;
AndreaBrennen;
Alvaro JCastro Rivadeneira;
AaronGerding;
Tilmann Gneiting ;
Katie HHouse;
YuxinHuang;
+285 more...
DasuniJayawardena;
Abdul HKanji;
AyushKhandelwal;
KhoaLe;
AnjaMühlemann;
Jarad Niemi ;
ApurvShah;
ArianeStark;
YijinWang;
NutchaWattanachit;
Martha WZorn;
YouyangGu;
SansiddhJain;
NayanaBannur;
AyushDeva;
MihirKulkarni;
SrujanaMerugu;
AlpanRaval;
SiddhantShingi;
AvtanshTiwari;
Jerome White ;
Neil FAbernethy;
Spencer Woody ;
MaytalDahan;
Spencer Fox ;
Kelly Gaither ;
MichaelLachmann;
Lauren Ancel Meyers ;
James GScott;
Mauricio Tec ;
AjiteshSrivastava;
Glover E George ;
Jeffrey C Cegan ;
Ian DDettwiller;
William PEngland;
Matthew WFarthing;
Robert H Hunter ;
Brandon Lafferty ;
IgorLinkov;
Michael L Mayo ;
Matthew DParno;
Michael A Rowland ;
Benjamin DTrump;
YanliZhang-James;
Samuel Chen ;
Stephen VFaraone;
JonathanHess;
Christopher PMorley;
Asif Salekin ;
DongliangWang;
Sabrina M Corsetti ;
Thomas MBaer;
Marisa CEisenberg;
Karl Falb ;
Yitao Huang ;
Emily TMartin;
EllaMcCauley;
Robert LMyers;
TomSchwarz;
Daniel Sheldon ;
Graham CaseyGibson;
RoseYu;
LiyaoGao;
YianMa;
DongxiaWu;
XifengYan;
XiaoyongJin;
Yu-XiangWang;
YangQuanChen;
Lihong Guo ;
YantingZhao;
Quanquan Gu ;
JinghuiChen;
LingxiaoWang;
Pan Xu ;
WeitongZhang;
DifanZou;
HannahBiegel;
Joceline Lega ;
Steve McConnell ;
VPNagraj;
Stephanie LGuertin;
ChristopherHulme-Lowe;
Stephen D Turner ;
Yunfeng Shi ;
XuegangBan;
Robert Walraven ;
Qi-JunHong;
StanleyKong;
Axel van de Walle ;
James A Turtle ;
Michal Ben-Nun ;
Steven Riley ;
PeteRiley;
Ugur Koyluoglu ;
DavidDesRoches;
PedroForli;
BruceHamory;
ChristinaKyriakides;
HelenLeis;
JohnMilliken;
MichaelMoloney;
JamesMorgan;
NinadNirgudkar;
GokceOzcan;
NoahPiwonka;
MattRavi;
ChrisSchrader;
ElizabethShakhnovich;
DanielSiegel;
RyanSpatz;
ChrisStiefeling;
BarrieWilkinson;
AlexanderWong;
Sean Cavany ;
Guido España ;
Sean Moore ;
Rachel Oidtman ;
Alex Perkins ;
David Kraus ;
AndreaKraus;
ZhifengGao;
JiangBian;
Wei Cao ;
Juan Lavista Ferres ;
ChaozhuoLi;
Tie-YanLiu;
XingXie;
ShunZhang;
ShunZheng;
Alessandro Vespignani ;
MatteoChinazzi;
Jessica TDavis;
KunpengMu;
AnaPastore Y Piontti;
XinyueXiong;
AndrewZheng;
JackieBaek;
VivekFarias;
AndreeaGeorgescu;
RetsefLevi;
Deeksha Sinha ;
JoshuaWilde;
Georgia Perakis ;
Mohammed Amine Bennouna ;
DavidNze-Ndong;
DivyaSinghvi;
Ioannis Spantidakis ;
LeannThayaparan;
Asterios Tsiourvas ;
Arnab Sarker ;
Ali Jadbabaie ;
Devavrat Shah ;
NicolasDella Penna;
Leo A Celi ;
SakethSundar;
RussWolfinger;
Dave Osthus ;
LaurenCastro;
Geoffrey Fairchild ;
IsaacMichaud;
DeanKarlen;
MattKinsey;
Luke C Mullany ;
Kaitlin Rainwater-Lovett ;
LaurenShin;
KatharineTallaksen;
ShelbyWilson;
Elizabeth C Lee ;
Juan Dent ;
Kyra HGrantz;
Alison L Hill ;
JoshuaKaminsky;
KathrynKaminsky;
Lindsay T Keegan ;
Stephen ALauer;
Joseph C Lemaitre ;
JustinLessler;
Hannah RMeredith;
JavierPerez-Saez;
SamShah;
Claire PSmith;
Shaun A Truelove ;
Josh Wills ;
MaximilianMarshall;
LaurenGardner;
KristenNixon;
John CBurant;
LilyWang;
Lei Gao ;
Zhiling Gu ;
MyungjinKim;
XinyiLi;
GuannanWang;
YueyingWang;
Shan Yu ;
Robert CReiner;
RyanBarber;
EmmanuelaGakidou;
Simon I Hay ;
SteveLim;
Chris Murray ;
DavidPigott;
Heidi LGurung;
PrasithBaccam;
Steven A Stage ;
Bradley TSuchoski;
B Aditya Prakash ;
BijayaAdhikari;
JiamingCui;
Alexander Rodríguez ;
AnikaTabassum;
Jiajia Xie ;
Pinar Keskinocak ;
JohnAsplund;
Arden Baxter ;
Buse Eylul Oruc ;
NicoletaSerban;
Sercan OArik;
MikeDusenberry;
ArkadyEpshteyn;
ElliKanal;
Long TLe;
Chun-LiangLi;
TomasPfister;
DarioSava;
Rajarishi Sinha ;
ThomasTsai;
Nate Yoder ;
JinsungYoon;
Leyou Zhang ;
SamAbbott;
Nikos IBosse;
Sebastian Funk ;
JoelHellewell;
Sophie R Meakin ;
Katharine Sherratt ;
MingyuanZhou;
RahiKalantari;
Teresa K Yamana ;
Sen Pei ;
Jeffrey Shaman ;
Michael L Li ;
Dimitris Bertsimas ;
Omar Skali Lami ;
Saksham Soni ;
Hamza Tazi Bouardi ;
TurgayAyer;
MadelineAdee;
JagpreetChhatwal;
Ozden ODalgic;
Mary ALadd;
Benjamin PLinas;
PeterMueller;
JadeXiao;
Yuanjia Wang ;
QinxiaWang;
ShanghongXie;
DonglinZeng;
AldenGreen;
JacobBien;
LoganBrooks;
Addison JHu;
MariaJahja;
Daniel McDonald ;
BalasubramanianNarasimhan;
Collin Politsch ;
Samyak Rajanala ;
Aaron Rumack ;
NoahSimon;
Ryan J Tibshirani ;
RobTibshirani;
ValerieVentura;
LarryWasserman;
Eamon BO'Dea;
John M Drake ;
RobertPagano;
Quoc TTran;
Lam Si Tung Ho ;
HuongHuynh;
Jo WWalker;
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.
-
picture_as_pdf - Cramer_etal_2021_Evaluation-of-individual-and-ensemble.pdf
-
subject - Published Version
-
- Available under Creative Commons: 4.0
Explore Further
Read more research from the creator(s):
Find work associated with the faculties and division(s):
Find work associated with the research centre(s):
Find work from this publication: