Evaluating epidemic forecasts in an interval format.
For practical reasons, many forecasts of case, hospitalization, and death counts in the context of the current Coronavirus Disease 2019 (COVID-19) pandemic are issued in the form of central predictive intervals at various levels. This is also the case for the forecasts collected in the COVID-19 Fore...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2021-02-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1008618 |
| _version_ | 1811344958525800448 |
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| author | Johannes Bracher Evan L Ray Tilmann Gneiting Nicholas G Reich |
| author_facet | Johannes Bracher Evan L Ray Tilmann Gneiting Nicholas G Reich |
| author_sort | Johannes Bracher |
| collection | DOAJ |
| description | For practical reasons, many forecasts of case, hospitalization, and death counts in the context of the current Coronavirus Disease 2019 (COVID-19) pandemic are issued in the form of central predictive intervals at various levels. This is also the case for the forecasts collected in the COVID-19 Forecast Hub (https://covid19forecasthub.org/). Forecast evaluation metrics like the logarithmic score, which has been applied in several infectious disease forecasting challenges, are then not available as they require full predictive distributions. This article provides an overview of how established methods for the evaluation of quantile and interval forecasts can be applied to epidemic forecasts in this format. Specifically, we discuss the computation and interpretation of the weighted interval score, which is a proper score that approximates the continuous ranked probability score. It can be interpreted as a generalization of the absolute error to probabilistic forecasts and allows for a decomposition into a measure of sharpness and penalties for over- and underprediction. |
| first_indexed | 2024-04-13T19:55:51Z |
| format | Article |
| id | doaj.art-ca7ae5c7d221495d83bd6ed87855f369 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-04-13T19:55:51Z |
| publishDate | 2021-02-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-ca7ae5c7d221495d83bd6ed87855f3692022-12-22T02:32:21ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582021-02-01172e100861810.1371/journal.pcbi.1008618Evaluating epidemic forecasts in an interval format.Johannes BracherEvan L RayTilmann GneitingNicholas G ReichFor practical reasons, many forecasts of case, hospitalization, and death counts in the context of the current Coronavirus Disease 2019 (COVID-19) pandemic are issued in the form of central predictive intervals at various levels. This is also the case for the forecasts collected in the COVID-19 Forecast Hub (https://covid19forecasthub.org/). Forecast evaluation metrics like the logarithmic score, which has been applied in several infectious disease forecasting challenges, are then not available as they require full predictive distributions. This article provides an overview of how established methods for the evaluation of quantile and interval forecasts can be applied to epidemic forecasts in this format. Specifically, we discuss the computation and interpretation of the weighted interval score, which is a proper score that approximates the continuous ranked probability score. It can be interpreted as a generalization of the absolute error to probabilistic forecasts and allows for a decomposition into a measure of sharpness and penalties for over- and underprediction.https://doi.org/10.1371/journal.pcbi.1008618 |
| spellingShingle | Johannes Bracher Evan L Ray Tilmann Gneiting Nicholas G Reich Evaluating epidemic forecasts in an interval format. PLoS Computational Biology |
| title | Evaluating epidemic forecasts in an interval format. |
| title_full | Evaluating epidemic forecasts in an interval format. |
| title_fullStr | Evaluating epidemic forecasts in an interval format. |
| title_full_unstemmed | Evaluating epidemic forecasts in an interval format. |
| title_short | Evaluating epidemic forecasts in an interval format. |
| title_sort | evaluating epidemic forecasts in an interval format |
| url | https://doi.org/10.1371/journal.pcbi.1008618 |
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