Postdoctoral Researcher | University of California, Los Angeles (UCLA)
Eugenio is a computational epidemiologist. He uses mathematical and computational models, embedded in a complex networks formalism, to study the spread of infectious diseases in human and animal populations.
He wants to understand what makes them vulnerable to disease introductions, and what we can do to prevent and stop epidemic outbreaks. He got is MSc in theoretical physics from University of Turin, Italy, and his PhD in epidemiology from Université Pierre era Marie Curie, Paris, France. He is now a postdoctoral researcher at UCLA.
Computational models to predict when diseases turn epidemic
Infectious diseases affecting humans and animals still represent a major burden for our health, society and economy. For these reason, it is crucial to build synthetic models that can tell us what happens when a new pathogen appears. Will it go extinct on its own? Will it cause a widespread epidemics? What is the best way to intervene?
The biggest challenge in making that happen is integrating the simulation of the disease and the data describing the contacts and mobility patterns along which it spreads. And Python is the best for doing just that. Focusing on different case studies, I will show you how to build Python-based libraries that handle input data, perform simulations, analyze the output and make predictions.
These libraries can also be turned into a sharable, standardized tool, that can be used by data holders and public health officials, without requiring advanced technical knowledge.