- (5mins)
Welcome and house rules

Integrating traditional surveillance with real-time data streams – such as genomic sequencing, mobility patterns, climate data, and social behaviour – has enhanced our ability to model pathogen spread and anticipate epidemic trajectories. Advances in machine learning, network science, and statistical inference are refining predictive 2025 Science Breakthrough Radar – Anticipation Workshop – Pathogen Biology models, allowing for early detection of transmission hotspots, identification of superspreading events, and evaluation of intervention strategies. As the world faces increasingly complex and interconnected health threats, predictive epidemiology is vital for informing public health responses, guiding vaccine distribution, and improving pandemic preparedness. However, challenges include creating integrated, global surveillance systems as well as equitable data sharing. Here we will discuss how anticipated scientific and technological breakthroughs over the next 5, 10 and 25 years are likely to significantly impact our ability to model and predict pathogen spread, and why.