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Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States.
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.
  • Cramer EY; Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003.
  • Ray EL; Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003.
  • Lopez VK; COVID-19 Response, Centers for Disease Control and Prevention; Atlanta, GA 30333.
  • Bracher J; Chair of Econometrics and Statistics, Karlsruhe Institute of Technology, 76185 Karlsruhe, Germany.
  • Brennen A; Computational Statistics Group, Heidelberg Institute for Theoretical Studies, 69118 Heidelberg, Germany.
  • Castro Rivadeneira AJ; IQT Labs, In-Q-Tel, Waltham, MA 02451.
  • Gerding A; Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003.
  • Gneiting T; Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003.
  • House KH; Computational Statistics Group, Heidelberg Institute for Theoretical Studies, 69118 Heidelberg, Germany.
  • Huang Y; Institute of Stochastics, Karlsruhe Institute of Technology, 69118 Karlsruhe, Germany.
  • Jayawardena D; Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003.
  • Kanji AH; Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003.
  • Khandelwal A; Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003.
  • Le K; Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003.
  • Mühlemann A; Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003.
  • Niemi J; Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003.
  • Shah A; Institute of Mathematical Statistics and Actuarial Science, University of Bern, CH-3012 Bern, Switzerland.
  • Stark A; Department of Statistics, Iowa State University, Ames, IA 50011.
  • Wang Y; Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003.
  • Wattanachit N; Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003.
  • Zorn MW; Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003.
  • Gu Y; Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003.
  • Jain S; Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA 01003.
  • Bannur N; Unaffiliated, New York, NY 10016.
  • Deva A; Wadhwani Institute of Artificial Intelligence, Andheri East, Mumbai, 400093, India.
  • Kulkarni M; Wadhwani Institute of Artificial Intelligence, Andheri East, Mumbai, 400093, India.
  • Merugu S; Wadhwani Institute of Artificial Intelligence, Andheri East, Mumbai, 400093, India.
  • Raval A; Wadhwani Institute of Artificial Intelligence, Andheri East, Mumbai, 400093, India.
  • Shingi S; Wadhwani Institute of Artificial Intelligence, Andheri East, Mumbai, 400093, India.
  • Tiwari A; Wadhwani Institute of Artificial Intelligence, Andheri East, Mumbai, 400093, India.
  • White J; Wadhwani Institute of Artificial Intelligence, Andheri East, Mumbai, 400093, India.
  • Abernethy NF; Wadhwani Institute of Artificial Intelligence, Andheri East, Mumbai, 400093, India.
  • Woody S; Wadhwani Institute of Artificial Intelligence, Andheri East, Mumbai, 400093, India.
  • Dahan M; University of Washington, Seattle, WA 98109.
  • Fox S; Department of Integrative Biology, University of Texas at Austin, Austin, TX 78712.
  • Gaither K; Texas Advanced Computing Center, Austin, TX 78758.
  • Lachmann M; Department of Integrative Biology, University of Texas at Austin, Austin, TX 78712.
  • Meyers LA; Texas Advanced Computing Center, Austin, TX 78758.
  • Scott JG; Santa Fe Institute, Santa Fe, NM 87501.
  • Tec M; Department of Integrative Biology, University of Texas at Austin, Austin, TX 78712.
  • Srivastava A; Department of Information, Risk, and Operations Management, University of Texas at Austin, Austin, TX 78712.
  • George GE; Department of Statistics and Data Sciences, University of Texas at Austin, Austin, TX 78712.
  • Cegan JC; Ming Hsieh Department of Computer and Electrical Engineering, University of Southern California, Los Angeles, CA 90089.
  • Dettwiller ID; US Army Engineer Research and Development Center, Vicksburg, MS 39180.
  • England WP; US Army Engineer Research and Development Center, Concord, MA 01742.
  • Farthing MW; US Army Engineer Research and Development Center, Vicksburg, MS 39180.
  • Hunter RH; US Army Engineer Research and Development Center, Vicksburg, MS 39180.
  • Lafferty B; US Army Engineer Research and Development Center, Vicksburg, MS 39180.
  • Linkov I; US Army Engineer Research and Development Center, Vicksburg, MS 39180.
  • Mayo ML; US Army Engineer Research and Development Center, Vicksburg, MS 39180.
Proc Natl Acad Sci U S A ; 119(15): e2113561119, 2022 04 12.
Article in English | MEDLINE | ID: covidwho-1784075
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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Experimental Studies / Observational study / Prognostic study / Randomized controlled trials Limits: Humans Country/Region as subject: North America Language: English Journal: Proc Natl Acad Sci U S A Year: 2022 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Experimental Studies / Observational study / Prognostic study / Randomized controlled trials Limits: Humans Country/Region as subject: North America Language: English Journal: Proc Natl Acad Sci U S A Year: 2022 Document Type: Article