our-people | faculty - research


Matteo Chinazzi

Research Associate Professor

Matteo Chinazzi is a Research Associate Professor at Northeastern University (Department of Physics), Roux Institute member, and Core Faculty at the Network Science Institute. He conducts research at the intersection between network science, data science, epidemiology, economics, and artificial intelligence.

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 models; and e) the study of the evolution and structure of science and innovation. He is the lead developer of the current version of the Global Epidemic and Mobility model (GLEAM, https://www.gleamproject.org) and the coordinator of the Network Science Institute COVID-19 Mobility project (http://covid19.gleamproject.org/mobility).

You can find his personal website at matteochinazzi.com

Selected publications:

  • Aleta, A., et al. Quantifying the importance and location of SARS-CoV-2 transmission events in large metropolitan areas. Proceedings of the National Academy of Sciences 119 (26), e2112182119 (2022).
  • Davis, J.T., Chinazzi, M., Perra, N. et al. Cryptic transmission of SARS-CoV-2 and the first COVID-19 wave. Nature 600, 127–132 (2021). https://doi.org/10.1038/s41586-021-04130-w
  • Wu, D., Gao, L., Chinazzi, M., Xiong, X., Vespignani, A., Ma, Y., and Yu, R. Quantifying Uncertainty in Deep Spatiotemporal Forecasting. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (KDD ’21).

Chinazzi, M. et al. The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak. Science, 368(6489), 395-400 (2020). https://doi.org/10.1126/science.aba9757