Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US
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 (<jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://covid19forecasthub.org/">https://covid19forecasthub.org/</jats:ext-link>) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multi-model ensemble forecast that combined predictions from dozens of different research 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-week horizon 3-5 times larger than when predicting at a 1-week 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. Significance Statement This 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 US. 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..
| Medienart: |
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Preprint| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
bioRxiv.org - (2022) vom: 24. Feb. Zur Gesamtaufnahme - year:2022 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
Cramer, Estee Y [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| doi: |
10.1101/2021.02.03.21250974 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
XBI035326158 |
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| 100 | 1 | |a Cramer, Estee Y |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US |
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| 520 | |a 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 (<jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://covid19forecasthub.org/">https://covid19forecasthub.org/</jats:ext-link>) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multi-model ensemble forecast that combined predictions from dozens of different research 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-week horizon 3-5 times larger than when predicting at a 1-week 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. Significance Statement This 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 US. 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. | ||
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| 700 | 1 | |a Ladd, Mary A |e verfasserin |4 aut | |
| 700 | 1 | |a Linas, Benjamin P |e verfasserin |4 aut | |
| 700 | 1 | |a Mueller, Peter |e verfasserin |4 aut | |
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| 700 | 1 | |a Wang, Qinxia |e verfasserin |4 aut | |
| 700 | 1 | |a Xie, Shanghong |e verfasserin |4 aut | |
| 700 | 1 | |a Zeng, Donglin |e verfasserin |4 aut | |
| 700 | 1 | |a Green, Alden |e verfasserin |4 aut | |
| 700 | 1 | |a Bien, Jacob |e verfasserin |4 aut | |
| 700 | 1 | |a Brooks, Logan |e verfasserin |4 aut | |
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| 700 | 1 | |a Politsch, Collin |e verfasserin |4 aut | |
| 700 | 1 | |a Rajanala, Samyak |e verfasserin |4 aut | |
| 700 | 1 | |a Rumack, Aaron |e verfasserin |4 aut | |
| 700 | 1 | |a Simon, Noah |e verfasserin |4 aut | |
| 700 | 1 | |a Tibshirani, Ryan J |e verfasserin |4 aut | |
| 700 | 1 | |a Tibshirani, Rob |e verfasserin |4 aut | |
| 700 | 1 | |a Ventura, Valerie |e verfasserin |4 aut | |
| 700 | 1 | |a Wasserman, Larry |e verfasserin |4 aut | |
| 700 | 1 | |a O’Dea, Eamon B |e verfasserin |4 aut | |
| 700 | 1 | |a Drake, John M |e verfasserin |4 aut | |
| 700 | 1 | |a Pagano, Robert |e verfasserin |4 aut | |
| 700 | 1 | |a Tran, Quoc T |e verfasserin |4 aut | |
| 700 | 1 | |a Tung Ho, Lam Si |e verfasserin |4 aut | |
| 700 | 1 | |a Huynh, Huong |e verfasserin |4 aut | |
| 700 | 1 | |a Walker, Jo W |e verfasserin |4 aut | |
| 700 | 1 | |a Slayton, Rachel B |e verfasserin |4 aut | |
| 700 | 1 | |a Johansson, Michael A |e verfasserin |4 aut | |
| 700 | 1 | |a Biggerstaff, Matthew |e verfasserin |4 aut | |
| 700 | 1 | |a Reich, Nicholas G |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t bioRxiv.org |g (2022) vom: 24. Feb. |
| 773 | 1 | 8 | |g year:2022 |g day:24 |g month:02 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1101/2021.02.03.21250974 |z kostenfrei |3 Volltext |
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