Cross-Country Learning for National Infectious Disease Forecasting Using European Data

2026-01-28

AI Summary

This study addresses the challenge of constructing accurate epidemic forecasting models in countries with limited and low-variability historical outbreak data. To overcome this data scarcity, the authors propose a cross-national learning framework that integrates infectious disease time series from multiple European countries. By leveraging shared epidemiological dynamics across nations and employing data augmentation strategies, the framework trains a unified multi-step prediction model. Experimental results demonstrate that this approach significantly improves forecasting accuracy for target countries such as Cyprus compared to baseline methods that rely solely on domestic data, thereby validating its effectiveness and generalizability in data-scarce settings.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Search and Optimization: Learning to Search
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Accurate forecasting of infectious disease incidence is critical for public health planning and timely intervention. While most data-driven forecasting approaches rely primarily on historical data from a single country, such data are often limited in length and variability, restricting the performance of machine learning (ML) models. In this work, we investigate a cross-country learning approach for infectious disease forecasting, in which a single model is trained on time series data from multiple countries and evaluated on a country of interest. This setting enables the model to exploit shared epidemic dynamics across countries and to benefit from an enlarged training set. We examine this approach through a case study on COVID-19 case forecasting in Cyprus, using surveillance data from European countries. We evaluate multiple ML models and analyse the impact of the lookback window length and cross-country `data augmentation'on multi-step forecasting performance. Our results show that incorporating data from other countries can lead to consistent improvements over models trained solely on national data. Although the empirical focus is on Cyprus and COVID-19, the proposed framework and findings are applicable to infectious disease forecasting more broadly, particularly in settings with limited national historical data.

Problem

• infectious disease forecasting
• cross-country learning
• limited national data
• machine learning
• epidemic dynamics

Innovation

• cross-country learning
• infectious disease forecasting
• data augmentation
• multi-step forecasting
• machine learning