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Evaluating epidemic forecasts in an interval format

Bracher, Johannes 1; Ray, Evan L.; Gneiting, Tilmann 2; Reich, Nicholas G.; Pitzer, Virginia E. [Hrsg.]
1 Karlsruher Institut für Technologie (KIT)
2 Institut für Stochastik (STOCH), Karlsruher Institut für Technologie (KIT)


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 ( 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.

Verlagsausgabe §
DOI: 10.5445/IR/1000135014
Veröffentlicht am 06.07.2021
DOI: 10.1371/JOURNAL.PCBI.1008618
Zitationen: 89
Zitationen: 160
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Stochastik (STOCH)
Institut für Volkswirtschaftslehre (ECON)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2021
Sprache Englisch
Identifikator ISSN: 1553-7358
KITopen-ID: 1000135014
Erschienen in PLoS Computational Biology
Verlag Public Library of Science (PLoS)
Band 17
Heft 2
Seiten Art.Nr. e1008618
Vorab online veröffentlicht am 12.02.2021
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