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; ... (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 - PNAS, 119(15), e2113561119. National Academy of Sciences NAS 10.1073/pnas.2113561119

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SignificanceThis paper compares the probabilistic accuracy of short-term forecasts of reported deaths due to COVID-19 during the first year and a half of the pandemic in the United States. Results show high variation in accuracy between and within stand-alone models and more consistent accuracy from an ensemble model that combined forecasts from all eligible models. This demonstrates that an ensemble model provided a reliable and comparatively accurate means of forecasting deaths during the COVID-19 pandemic that exceeded the performance of all of the models that contributed to it. This work strengthens the evidence base for synthesizing multiple models to support public-health action.

Item Type:

Journal Article (Original Article)

Division/Institute:

08 Faculty of Science > Department of Mathematics and Statistics > Institute of Mathematical Statistics and Actuarial Science

UniBE Contributor:

Mühlemann, Anja Tamina

Subjects:

300 Social sciences, sociology & anthropology > 360 Social problems & social services
500 Science > 510 Mathematics

ISSN:

0027-8424

Publisher:

National Academy of Sciences NAS

Language:

English

Submitter:

Pubmed Import

Date Deposited:

11 Apr 2022 08:52

Last Modified:

11 Apr 2022 09:00

Publisher DOI:

10.1073/pnas.2113561119

PubMed ID:

35394862

Uncontrolled Keywords:

COVID-19 ensemble forecast forecasting model evaluation

BORIS DOI:

10.48350/169178

URI:

https://boris.unibe.ch/id/eprint/169178

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