Public health decision-making is increasingly challenged by emerging infectious diseases, climate change, demographic shifts, and rapidly evolving global mobility patterns. Recent experience with COVID-19 has demonstrated both the power and the limitations of current modelling and forecasting tools. At the same time, advances in artificial intelligence (AI), mathematical modelling, data science and their integration offer unprecedented opportunities to transform how we detect, characterize, and respond to health threats.

This conference will:

• Showcase new mathematical and AI methodologies for rapid epidemiological characterization and forecasting;
• Present results from collaborative projects on influenza–COVID 9 (and other viral infections such as RSV) co-circulation and vector-borne disease range expansion under climate change;
• Foster dialogue between mathematicians, AI researchers, epidemiologists, public health agencies, and industry partners;
• Train and mentor a new generation of mathematical scientists working at the mathematics–AI–policy interface.

The Canada–China–Hungary (C2H) Network of Mathematics for Innovation in Public Health is an international collaborative initiative established to advance mathematical and AI-driven innovation in public health. The network connects leading research clusters connected with York University (Canada), Fudan University (China), and University of Szeged (Hungary) to build a sustainable, interdisciplinary platform at the interface of mathematical sciences, artificial intelligence, and public health policy. The initiative operates in partnership with the Centre of Excellence in Artificial Intelligence for Public Health Advancement (AiPHA), the Fields Institute for Research in Mathematical Sciences, the Research Institute of Intelligent Complex Systems (Fudan University), the Shanghai Institute of Infectious Diseases and Biosecurity, the Bolyai Institute (University of Szeged), and the National Laboratory for Health Security.