Evaluating epidemic forecasts in an interval format

TL;DR

This paper discusses methods for evaluating epidemic forecasts presented as predictive intervals, focusing on the weighted interval score to assess forecast accuracy and calibration in the context of COVID-19 data.

Contribution

It introduces a practical approach to evaluate interval forecasts in epidemic modeling, bridging the gap where full predictive distributions are unavailable.

Findings

Weighted interval score effectively evaluates probabilistic forecasts.

The score decomposes forecast quality into sharpness and calibration.

Application to COVID-19 forecasts demonstrates its utility.

Abstract

For practical reasons, many forecasts of case, hospitalization and death counts in the context of the current 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.