Tootfinder

Forecasting Seasonal Influenza Epidemics with Physics-Informed Neural Networks
Martina Rama, Gabriele Santin, Giulia Cencetti, Michele Tizzoni, Bruno Lepri
https://arxiv.org/abs/2506.03897

Forecasting Seasonal Influenza Epidemics with Physics-Informed Neural Networks
Accurate epidemic forecasting is critical for informing public health decisions and timely interventions. While Physics-Informed Neural Networks (PINNs) have shown promise in various scientific domains, their application to real-time epidemic forecasting remains limited. The reasons are mainly due to the intrinsic difficulty of the task and the tendency to fully leveraging their learning and inference potential, which, however, often results in non-optimal forecasting frameworks. Here, we prese…

Sensitivity analysis of epidemic forecasting and spreading on networks with probability generating functions
Mariah C. Boudreau, William H. W. Thompson, Christopher M. Danforth, Jean-Gabriel Young, Laurent H\'ebert-Dufresne
https://arxiv.org/abs/2506.24103

Sensitivity analysis of epidemic forecasting and spreading on networks with probability generating functions
Epidemic forecasting tools embrace the stochasticity and heterogeneity of disease spread to predict the growth and size of outbreaks. Conceptually, stochasticity and heterogeneity are often modeled as branching processes or as percolation on contact networks. Mathematically, probability generating functions provide a flexible and efficient tool to describe these models and quickly produce forecasts. While their predictions are probabilistic-i.e., distributions of outcome-they depend determinist…