The recent COVID-19 pandemic has demonstrated how in an interconnected world, a local outbreak can become an international concern with potentially long lasting impact on the society and economy of many countries. Moreover, from a scientific standpoint, it has also revealed that while epidemic modeling can be useful in assisting decision makers and public health officials, contributions from computational scientists (including network scientists) have played a central role in making that a reality. In this talk, Dr. Chinazzi will discuss what it means to model a pandemic in real time and how realistic, large-scale, epidemic models have been used to simulate, forecast, and project the spreading of SARS-CoV-2 and to assess its impact at a global and national scale.
Matteo Chinazzi is a Senior Research Scientist at the Laboratory for the Modeling of Biological and Socio-Technical Systems (MOBS Lab). He holds a PhD in Economics from Sant’Anna School of Advanced Studies (Pisa, Italy) and his work focuses primarily 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 a variety of public and private contexts. His research interests include: a) the development of computational and analytical models to study and forecast the spatial spread of infectious diseases; b) the development of agent-based models to create realistic representations of population dynamics; c) the study of human mobility and contact patterns using high-resolution large-scale de-identified location data; d) the development of computational frameworks that combine mechanistic epidemic models with machine learning/deep learning approaches; and e) the study of the evolution and structure of science and innovation.
Matteo Chinazzi is a Senior Research Scientist at the Laboratory for the Modeling of Biological and Socio-Technical Systems (MOBS Lab). He holds a PhD in Economics from Sant’Anna School of Advanced Studies (Pisa, Italy) and his work focuses primarily 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 a variety of public and private contexts. His research interests include: a) the development of computational and analytical models to study and forecast the spatial spread of infectious diseases; b) the development of agent-based models to create realistic representations of population dynamics; c) the study of human mobility and contact patterns using high-resolution large-scale de-identified location data; d) the development of computational frameworks that combine mechanistic epidemic models with machine learning/deep learning approaches; and e) the study of the evolution and structure of science and innovation.
