AI in Public Health: From Mechanistic and Surrogate Models to Data-Driven Models
AI in Public Health: From Mechanistic and Surrogate Models to Data-Driven Models features three experts discussing advancements in disease monitoring and forecasting. The session will describe data-driven machine learning methodologies that leverage Internet-based information from search engines, introduce the field of infectious disease forecasting and scenario analysis, and demonstrate how artificial intelligence (AI) can accelerate epidemic simulations by using surrogate models of large-scale mechanistic epidemic models.
PRESENTED BY:
Alessandro Vespignani
Director and Sternberg Family Distinguished Professor
College of Engineering
Northeastern University
Alessandro Vespignani research activity is focused on the study of “techno-social” systems, where infrastructures composed of different technological layers are interoperating within the social component that drives their use and development. In this context we aim at understanding how the very same elements assembled in large number can give rise – according to the various forces and elements at play – to different macroscopic and dynamical behaviors, opening the path to quantitative computational approaches and forecasting power.
Maurico Santillana
Professor
College of Engineering
Northeastern University
Mauricio Santillana, PhD, MSc is the director of the Machine Intelligence Group for the betterment of Health and the Environment (MIGHTE) at the Network Science Institute at Northeastern University. He is a Professor at both the Physics and Electrical and Computer Engineering Departments at Northeastern University, and an Adjunct Professor at the Department of Epidemiology, T.H. Chan Harvard School of Public Health.
Matteo Chinazzi
Research Associate Professor
The Roux Institute
Northeastern University
Matteo Chinazzi is a research associate professor at the Roux Institute and core faculty at the Network Science Institute. His research lies at the intersection of network science, data science, epidemiology, economics, and artificial intelligence. His work primarily focuses on improving the modeling of socio-technical-economic systems by leveraging large-scale, data-driven, behaviorally informed, analytical, and computational frameworks to assist policy and decision-making in various public and private contexts. Chinazzi holds a PhD in economics from Sant’Anna School of Advanced Studies (Pisa, Italy), an M.Sc. in economics and social sciences from Bocconi University, and an undergraduate degree in economics (CLEMIT) from Bocconi University.