EpiCastBench: Datasets and Benchmarks for Multivariate Epidemic Forecasting

TL;DR

EpiCastBench introduces a comprehensive benchmarking framework with diverse multivariate epidemic datasets and standardized evaluation protocols to advance epidemic forecasting research.

Contribution

This work provides the first large-scale, publicly available multivariate epidemic datasets and a standardized benchmarking framework for fair model comparison.

Findings

Evaluated 15 forecasting models across diverse datasets.

Identified key structural patterns in epidemic data.

Provided reproducible benchmarks for future research.

Abstract

The increasing adoption of data-driven decision-making in public health has established epidemic forecasting as a critical area of research. Recent advances in multivariate forecasting models better capture complex temporal dependencies than conventional univariate approaches, which model individual series independently. Despite this potential, the development of robust epidemic forecasting methods is constrained by the lack of high-quality benchmarks comprising diverse multivariate datasets across infectious diseases and geographical regions. To address this gap, we present EpiCastBench, a large-scale benchmarking framework featuring 40 curated (correlated) multivariate epidemic datasets. These publicly available datasets span a wide range of infectious diseases and exhibit diverse characteristics in terms of temporal granularity, series length, and sparsity. We analyze these datasets to identify their global features and structural patterns. To ensure reproducibility and fair comparison, we establish standardized evaluation settings, including a unified forecasting horizon, consistent preprocessing pipelines, diverse performance metrics, and statistical significance testing. By leveraging this framework, we conduct a comprehensive evaluation of 15 multivariate forecasting models spanning statistical baselines to state-of-the-art deep learning and foundation models. All datasets and code are publicly available on Kaggle (https://www.kaggle.com/datasets/aimltsf/epicastbench) and GitHub (https://github.com/aimltsf/EpiCastBench).