Statistics

Consensus forecast

Probabilistic forecasting

Mathematical analysis

Econometrics

Generalization

Context (archaeology)

Interval (graph theory)

Confidence interval

Combinatorics

Archaeology

Infectious disease (medical specialty)

Prediction interval

Disease

Coronavirus disease 2019 (COVID-19)

Mathematics

Geography

Probabilistic logic

Credible interval

Computer science

Pathology

Quantile

Medicine

PLoS Computational Biology

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.

Evaluating epidemic forecasts in an interval format