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

Media type: Text; E-Article

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

Contributor: Cramer, Estee Y. [Author]; Ray, Evan L. [Author]; Lopez, Velma K. [Author]; Bracher, Johannes [Author]; Brennen, Andrea [Author]; Castro Rivadeneira, Alvaro J. [Author]; Gerding, Aaron [Author]; Gneiting, Tilmann [Author]; House, Katie H. [Author]; Huang, Yuxin [Author]; Jayawardena, Dasuni [Author]; Kanji, Abdul H. [Author]; Khandelwal, Ayush [Author]; Le, Khoa [Author]; Mühlemann, Anja [Author]; Niemi, Jarad [Author]; Shah, Apurv [Author]; Stark, Ariane [Author]; Wang, Yijin [Author]; Wattanachit, Nutcha [Author]; Zorn, Martha W. [Author]; Gu, Youyang [Author]; Jain, Sansiddh [Author]; Bannur, Nayana [Author]; [...]

Published: National Academy of Sciences, 2022-05-04

Language: English

DOI: https://doi.org/10.5445/IR/1000145738; https://doi.org/10.1073/pnas.2113561119

ISSN: 0027-8424; 1091-6490

Keywords: Mathematics

Origination:

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Description: 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.

Access State: Open Access

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