| Title: |
Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States. |
| 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 |
| Publisher Information: |
Proceedings of the National Academy of Sciences |
| Publication Year: |
2023 |
| Subject Terms: |
Humans; Probability; Public Health; Forecasting; United States; Pandemics; Data Accuracy; COVID-19; demo; envir |
| 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. |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| Relation: |
https://hdl.handle.net/10161/26494 |
| Availability: |
https://hdl.handle.net/10161/26494 |
| Rights: |
undefined |
| Accession Number: |
edsbas.7FA99750 |
| Database: |
BASE |