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...

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Bibliographic Details
Published in:PLoS computational biology 2021-02, Vol.17 (2), p.e1008618-e1008618
Main Authors: Bracher, Johannes, Ray, Evan L, Gneiting, Tilmann, Reich, Nicholas G
Format: Article
Language:English
Subjects:
Communicable Diseases - epidemiology
Coronaviruses
COVID-19
COVID-19 - epidemiology
COVID-19 - virology
Data collection
Decision analysis
Disease control
Epidemics
Epidemiology
Fines & penalties
Forecasting
Hospitalization
Humans
Infectious diseases
Medicine and Health Sciences
Pandemics
People and places
Physical Sciences
Probability
Public health
Research and Analysis Methods
SARS-CoV-2 - isolation & purification
Situational awareness
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Summary: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.
ISSN:1553-7358
1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1008618