A Framework for Evaluating Epidemic Forecasts

TL;DR

This paper introduces a comprehensive evaluation framework for epidemic forecasting methods, enabling systematic comparison of models based on multiple features and error measures, with a focus on long-term influenza predictions in the US.

Contribution

The paper presents a flexible evaluation framework that combines various features, error measures, and ranking schemas to assess epidemic forecast accuracy.

Findings

Different error measures produce different model rankings.

No single forecasting method outperforms others across all features.

Consensus rankings help summarize multiple evaluation criteria.

Abstract

Background: Over the past few decades, numerous forecasting methods have been proposed in the field of epidemic forecasting. Such methods can be classified into different categories such as deterministic vs. probabilistic, comparative methods vs. generative methods, and so on. In some of the more popular comparative methods, researchers compare observed epidemiological data from early stages of an outbreak with the output of proposed models to forecast the future trend and prevalence of the pandemic. A significant problem in this area is the lack of standard well-defined evaluation measures to select the best algorithm among different ones, as well as for selecting the best possible configuration for a particular algorithm. Results: In this paper, we present an evaluation framework which allows for combining different features, error measures, and ranking schema to evaluate forecasts. We describe the various epidemic features (Epi-features) included to characterize the output of forecasting methods and provide suitable error measures that could be used to evaluate the accuracy of the methods with respect to these Epi-features. We focus on long-term predictions rather than short-term forecasting and demonstrate the utility of the framework by evaluating six forecasting methods for predicting influenza in the United States. Our results demonstrate that different error measures lead to different rankings even for a single Epi-feature. Further, our experimental analyses show that no single method dominates the rest in predicting all Epi-features, when evaluated across error measures. As an alternative, we provide various consensus ranking schema that summarizes individual rankings, thus accounting for different error measures. We believe that a comprehensive evaluation framework, as presented in this paper, will add value to the computational epidemiology community.