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

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Date
2022-04
Authors
Cramer, Estee Y
Ray, Evan L
Lopez, Velma K
Bracher, Johannes
Brennen, Andrea
Castro Rivadeneira, Alvaro J
Gerding, Aaron
Gneiting, Tilmann
House, Katie H
Huang, Yuxin
Jayawardena, Dasuni
Kanji, Abdul H
Khandelwal, Ayush
Le, Khoa
Mühlemann, Anja
Niemi, Jarad
Shah, Apurv
Stark, Ariane
Wang, Yijin
Wattanachit, Nutcha
Zorn, Martha W
Gu, Youyang
Jain, Sansiddh
Bannur, Nayana
Deva, Ayush
Kulkarni, Mihir
Merugu, Srujana
Raval, Alpan
Shingi, Siddhant
Tiwari, Avtansh
White, Jerome
Abernethy, Neil F
Woody, Spencer
Dahan, Maytal
Fox, Spencer
Gaither, Kelly
Lachmann, Michael
Meyers, Lauren Ancel
Scott, James G
Tec, Mauricio
Srivastava, Ajitesh
George, Glover E
Cegan, Jeffrey C
Dettwiller, Ian D
England, William P
Farthing, Matthew W
Hunter, Robert H
Lafferty, Brandon
Linkov, Igor
Mayo, Michael L
Parno, Matthew D
Rowland, Michael A
Trump, Benjamin D
Zhang-James, Yanli
Chen, Samuel
Faraone, Stephen V
Hess, Jonathan
Morley, Christopher P
Salekin, Asif
Wang, Dongliang
Corsetti, Sabrina M
Baer, Thomas M
Eisenberg, Marisa C
Falb, Karl
Huang, Yitao
Martin, Emily T
McCauley, Ella
Myers, Robert L
Schwarz, Tom
Sheldon, Daniel
Gibson, Graham Casey
Yu, Rose
Gao, Liyao
Ma, Yian
Wu, Dongxia
Yan, Xifeng
Jin, Xiaoyong
Wang, Yu-Xiang
Chen, YangQuan
Guo, Lihong
Zhao, Yanting
Gu, Quanquan
Chen, Jinghui
Wang, Lingxiao
Xu, Pan
Zhang, Weitong
Zou, Difan
Biegel, Hannah
Lega, Joceline
McConnell, Steve
Nagraj, VP
Guertin, Stephanie L
Hulme-Lowe, Christopher
Turner, Stephen D
Shi, Yunfeng
Ban, Xuegang
Walraven, Robert
Hong, Qi-Jun
Kong, Stanley
van de Walle, Axel
Turtle, James A
Ben-Nun, Michal
Riley, Steven
Riley, Pete
Koyluoglu, Ugur
DesRoches, David
Forli, Pedro
Hamory, Bruce
Kyriakides, Christina
Leis, Helen
Milliken, John
Moloney, Michael
Morgan, James
Nirgudkar, Ninad
Ozcan, Gokce
Piwonka, Noah
Ravi, Matt
Schrader, Chris
Shakhnovich, Elizabeth
Siegel, Daniel
Spatz, Ryan
Stiefeling, Chris
Wilkinson, Barrie
Wong, Alexander
Cavany, Sean
España, Guido
Moore, Sean
Oidtman, Rachel
Perkins, Alex
Kraus, David
Kraus, Andrea
Gao, Zhifeng
Bian, Jiang
Cao, Wei
Lavista Ferres, Juan
Li, Chaozhuo
Liu, Tie-Yan
Xie, Xing
Zhang, Shun
Zheng, Shun
Vespignani, Alessandro
Chinazzi, Matteo
Davis, Jessica T
Mu, Kunpeng
Pastore Y Piontti, Ana
Xiong, Xinyue
Zheng, Andrew
Baek, Jackie
Farias, Vivek
Georgescu, Andreea
Levi, Retsef
Sinha, Deeksha
Wilde, Joshua
Perakis, Georgia
Bennouna, Mohammed Amine
Nze-Ndong, David
Singhvi, Divya
Spantidakis, Ioannis
Thayaparan, Leann
Tsiourvas, Asterios
Sarker, Arnab
Jadbabaie, Ali
Shah, Devavrat
Della Penna, Nicolas
Celi, Leo A
Sundar, Saketh
Wolfinger, Russ
Osthus, Dave
Castro, Lauren
Fairchild, Geoffrey
Michaud, Isaac
Karlen, Dean
Kinsey, Matt
Mullany, Luke C
Rainwater-Lovett, Kaitlin
Shin, Lauren
Tallaksen, Katharine
Wilson, Shelby
Lee, Elizabeth C
Dent, Juan
Grantz, Kyra H
Hill, Alison L
Kaminsky, Joshua
Kaminsky, Kathryn
Keegan, Lindsay T
Lauer, Stephen A
Lemaitre, Joseph C
Lessler, Justin
Meredith, Hannah R
Perez-Saez, Javier
Shah, Sam
Smith, Claire P
Truelove, Shaun A
Wills, Josh
Marshall, Maximilian
Gardner, Lauren
Nixon, Kristen
Burant, John C
Wang, Lily
Gao, Lei
Gu, Zhiling
Kim, Myungjin
Li, Xinyi
Wang, Guannan
Wang, Yueying
Yu, Shan
Reiner, Robert C
Barber, Ryan
Gakidou, Emmanuela
Hay, Simon I
Lim, Steve
Murray, Chris
Pigott, David
Gurung, Heidi L
Baccam, Prasith
Stage, Steven A
Suchoski, Bradley T
Prakash, B Aditya
Adhikari, Bijaya
Cui, Jiaming
Rodríguez, Alexander
Tabassum, Anika
Xie, Jiajia
Keskinocak, Pinar
Asplund, John
Baxter, Arden
Oruc, Buse Eylul
Serban, Nicoleta
Arik, Sercan O
Dusenberry, Mike
Epshteyn, Arkady
Kanal, Elli
Le, Long T
Li, Chun-Liang
Pfister, Tomas
Sava, Dario
Sinha, Rajarishi
Tsai, Thomas
Yoder, Nate
Yoon, Jinsung
Zhang, Leyou
Abbott, Sam
Bosse, Nikos I
Funk, Sebastian
Hellewell, Joel
Meakin, Sophie R
Sherratt, Katharine
Zhou, Mingyuan
Kalantari, Rahi
Yamana, Teresa K
Pei, Sen
Shaman, Jeffrey
Li, Michael L
Bertsimas, Dimitris
Skali Lami, Omar
Soni, Saksham
Tazi Bouardi, Hamza
Ayer, Turgay
Adee, Madeline
Chhatwal, Jagpreet
Dalgic, Ozden O
Ladd, Mary A
Linas, Benjamin P
Mueller, Peter
Xiao, Jade
Wang, Yuanjia
Wang, Qinxia
Xie, Shanghong
Zeng, Donglin
Green, Alden
Bien, Jacob
Brooks, Logan
Hu, Addison J
Jahja, Maria
McDonald, Daniel
Narasimhan, Balasubramanian
Politsch, Collin
Rajanala, Samyak
Rumack, Aaron
Simon, Noah
Tibshirani, Ryan J
Tibshirani, Rob
Ventura, Valerie
Wasserman, Larry
O'Dea, Eamon B
Drake, John M
Pagano, Robert
Tran, Quoc T
Ho, Lam Si Tung
Huynh, Huong
Walker, Jo W
Slayton, Rachel B
Johansson, Michael A
Biggerstaff, Matthew
Reich, Nicholas G
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Abstract
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.
Type
Journal article
Subject
Humans
Probability
Public Health
Forecasting
United States
Pandemics
Data Accuracy
COVID-19
Permalink
https://hdl.handle.net/10161/26494
Published Version (Please cite this version)
10.1073/pnas.2113561119
Publication Info
Cramer, Estee Y; Ray, Evan L; Lopez, Velma K; Bracher, Johannes; Brennen, Andrea; Castro Rivadeneira, Alvaro J; ... Reich, Nicholas G (2022). Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States. Proceedings of the National Academy of Sciences of the United States of America, 119(15). pp. e2113561119. 10.1073/pnas.2113561119. Retrieved from https://hdl.handle.net/10161/26494.
This is constructed from limited available data and may be imprecise. To cite this article, please review & use the official citation provided by the journal.
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Scholars@Duke

Xu

Pan Xu

Assistant Professor of Biostatistics & Bioinformatics
Pan mainly works on Machine Learning, which spans the areas of Artificial Intelligence, Data Science, Optimization, Reinforcement Learning, and High Dimensional Statistics. His research goal is to develop computationally- and data-efficient machine learning algorithms with both strong empirical performance and theoretical guarantees.
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