Human mobility is a critical driver of epidemics by substantially altering the probability of encounters, patterns of exposure, and the likelihood of disease propagation. While long-range movements may shape patterns of pathogen importation, short-range mobility and contact structures amplify local epidemics. Characterizing mobility patterns and social mixing across scales is therefore essential for understanding why and how epidemics emerge and spread, as well as for developing effective prevention and control strategies. The COVID-19 crisis, sparked a data-sharing revolution, with network operators such as Orange and Telefonica, along with tech giants like Google, Apple, and Facebook, providing real-time aggregated mobility data from mobile phone traces to track human mobility and help fight the pandemic. Epidemiological research is now focused on developing novel mathematical and computational frameworks to integrate high-resolution mobility data into models, enabling both retrospective analyses and real-time epidemic monitoring. In my talk, I will discuss how we utilized these data during the early stages of COVID-19 in France to capture the dynamic shifts in social mixing caused by mobility interventions and address critical public health questions. Additionally, I will present a retrospective theoretical study that characterizes the mobility factors shaping geographical diffusion across scales in the United States and demonstrates a model designed to optimize reliability for outbreak response while balancing mobility data requirements.
Dr. Giulia Pullano is a postdoctoral fellow at Georgetown University in Washington, DC, USA, working in the Bansal Lab within the Biology Department. Her research focuses on developing mathematical and computational models to understand the geographical dynamics of human-to-human diseases and inform public health policies. She is particularly interested in characterizing seasonal patterns in human behavior and disruptions during epidemics or extreme events to integrate them into epidemic models and optimize public health interventions. From 2020 to 2022, Dr. Pullano has been actively involved in the COVID-19 pandemic response, advising French public health agencies and government authorities. Dr. Pullano earned her PhD in Biomathematics and Public Health from the French National Institute of Health and Medical Research (INSERM), Sorbonne University, and Orange S.A., under the supervision of Dr. Vittoria Colizza. She obtained a Master’s degree in Physics of Complex Systems from Università degli Studi di Torino in 2016 and a Bachelor's degree in Physics from Università degli Studi di Roma La Sapienza in 2014.
Dr. Giulia Pullano is a postdoctoral fellow at Georgetown University in Washington, DC, USA, working in the Bansal Lab within the Biology Department. Her research focuses on developing mathematical and computational models to understand the geographical dynamics of human-to-human diseases and inform public health policies. She is particularly interested in characterizing seasonal patterns in human behavior and disruptions during epidemics or extreme events to integrate them into epidemic models and optimize public health interventions. From 2020 to 2022, Dr. Pullano has been actively involved in the COVID-19 pandemic response, advising French public health agencies and government authorities. Dr. Pullano earned her PhD in Biomathematics and Public Health from the French National Institute of Health and Medical Research (INSERM), Sorbonne University, and Orange S.A., under the supervision of Dr. Vittoria Colizza. She obtained a Master’s degree in Physics of Complex Systems from Università degli Studi di Torino in 2016 and a Bachelor's degree in Physics from Università degli Studi di Roma La Sapienza in 2014.
