Background: Metapopulation models, which consider epidemic spread across interconnected regions, can provide more accurate epidemic predictions compared to isolated models for the corresponding regions. Still, their added complexity and data requirements raise questions about their tangible benefits over simpler, localized models.
Methods: We develop and validate two networked compartmental metapopulation models for predicting influenza-like illness across Europe: a detailed network-based model, including international travel dynamics, and a simpler mean-field model, aggregating average regional data. The network is constructed using public mobility data and complemented with population densities at border regions. Incidence data of influenza-like illnesses from 28 countries are integrated using an Extended Kalman filter.
Results: We show that networked epidemic models effectively capture epidemic dynamics across regions and epidemic phases. The models enable accurate forecasts, missing data imputation, and actionable insights: network models outperform isolated models in forecasting epidemic progression, particularly during critical periods such as wave onsets and peaks, and maintain reliability in scenarios with missing data.
Conclusions: The findings unveil and quantify the advantages of metapopulation models for epidemic forecasting in interconnected regions, and pave the way to the integration of mobility and epidemic surveillance to improve the monitoring and prediction of spreading diseases.
Flu and other respiratory illnesses spread quickly across countries, especially in regions like Europe where people travel frequently. To better understand and predict these patterns, we built two mathematical models that predict how flu-like illnesses move between countries using information about travel dynamics and national illness reports. These models were tested using data from 28 European countries and found that they can track and forecast outbreaks more accurately than models that look at each country on its own. The models were especially good at predicting when new waves of illness would start and peak, and they could also fill in gaps when some countries had missing or delayed data. These results show that combining mobility and health data can improve how we monitor and anticipate disease spread, helping public health agencies respond faster and more effectively.
© 2026. The Author(s).