Modeling Geographical Disease Dynamics Through Spatial Connectivity Networks to Characterize and Combat Epidemics

Human behavior is a critical driver of infectious disease dynamics, shaping encounter probabilities, exposure patterns, and the spread of pathogens across local and global scales. Long-range movements facilitate pathogen importation, while short-range mobility and local contact networks amplify transmission, underscoring the complex interplay between spatial connectivity and disease propagation. Understanding mobility patterns and social interactions across diverse scales is essential to uncovering the mechanisms behind disease emergence and spread, enabling the design of targeted prevention and control strategies.

The COVID-19 pandemic marked a transformative moment in data sharing, with network operators and technology giants like Google, Apple, and Meta providing real-time aggregated mobility data derived from mobile phone activity. These datasets revolutionized epidemiological research, driving the development of mathematical and computational models that integrate high-resolution mobility networks for retrospective analyses and real-time epidemic monitoring. In this talk, I will explore how we leverage large-scale high-resolution mobility networks and mathematical modeling to unravel the mechanisms underlying disease spatial dynamics and address critical public health challenges, including the COVID-19 pandemic and the seasonal patterns of influenza.