Forecasting Seasonal Influenza Epidemics with Physics-Informed Neural Networks

Abstract

Accurate epidemic forecasting is critical for informing public health decisions and timely interventions. While Physics-Informed Neural Networks have shown promise in various scientific domains, their potential application to real-time epidemic forecasting remains underexplored. Here, we present SIR-INN, a hybrid forecasting framework that integrates the mechanistic structure of the classical Susceptible-Infectious-Recovered (SIR) model into a neural network architecture. Trained once on synthetic epidemic scenarios, the model is able to generalize across epidemic conditions without retraining. From limited and noisy observations, SIR-INN infers key transmission parameters via Markov chain Monte Carlo, generating probabilistic short- and long-term forecasts. We validate SIR-INN using national influenza data from the Italian National Institute of Health in the 2023-2024 and 2024-2025 seasons. The model performs competitively with current state-of-the-art approaches, particularly in terms of Weighted Interval Score. It shows accurate predictive performance in nearly all phases of the outbreak, with improved accuracy observed for the 2024-2025 influenza season. Credible uncertainty intervals are consistently maintained, while coverage metrics highlight room for improvement in uncertainty calibration. SIR-INN offers a computationally efficient, transparent, and generalizable solution for epidemic forecasting, appropriately leveraging the framework's hybrid design. Its ability to provide real-time predictions of epidemic dynamics, together with uncertainty quantification, makes it a promising tool for real-world epidemic forecasting.