|Talks|

Modeling Large-Scale Socio-technical-economic Systems: Pandemic response edition

Past Talk
Matteo Chinazzi
Senior Research Scientist, MOBS Lab
Apr 26, 2022
9:00 am
Apr 26, 2022
9:00 am
In-person
4 Thomas More St
London E1W 1YW, UK
The Roux Institute
Room
100 Fore Street
Portland, ME 04101
Network Science Institute
2nd floor
Network Science Institute
11th floor
177 Huntington Ave
Boston, MA 02115
Network Science Institute
2nd floor
Room
58 St Katharine's Way
London E1W 1LP, UK

Talk recording

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.

About the speaker
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.
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Apr 26, 2022