Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States
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Date
2022-04-08
Authors
Cramer, Estee
Lopez, Velma
Niemi, Jarad
George, Glover
Cegan, Jeffrey
Dettwiller, Ian
England, William
Farthing, Matthew
Hunter, Robert
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Niemi, Jarad
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Finance
The Department of Finance seeks to provide knowledge of the descriptive, behavioral, and analytical background of financial management, in preparation for positions in sales management, marketing research, retail, etc.
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The Department of Finance was formed in 1984 in the College of Business Administration (later College of Business).
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1984–present
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Statistics
As leaders in statistical research, collaboration, and education, the Department of Statistics at Iowa State University offers students an education like no other. We are committed to our mission of developing and applying statistical methods, and proud of our award-winning students and faculty.
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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.
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This article is published as Cramer, Estee Y., Evan L. Ray, Velma K. Lopez, Johannes Bracher, Andrea Brennen, Alvaro J. Castro Rivadeneira, Aaron Gerding et al. "Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States." Proceedings of the National Academy of Sciences 119, no. 15 (2022): e2113561119. doi:10.1073/pnas.2113561119.
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