faculty - research|
Research Associate Professor
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.
Chinazzi’s most recent work focuses on developing large-scale, data-driven computational models to study and forecast the spatial spread of infectious diseases; building near real-time epidemic intelligence solutions that combine mechanistic modeling approaches and deep learning frameworks; and analyzing individual level data to study the social determinants of health and the interplay between public health policies and economic outcomes. Furthermore, 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).
Areas of Expertise
- Computational Epidemiology
- Network & Data Science
- 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
- 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
- Nande, A., Sheen, J., Walters, E.L. et al. The effect of eviction moratoria on the transmission of SARS-CoV-2. Nature Communications, 12, 2274 (2021). https://doi.org/10.1038/s41467-021-22521-5
- Wu, D., Chinazzi, M., Vespignani, A., Ma, Y., and Yu, R. Multi-fidelity Hierarchical Neural Processes. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD ’22 (2022). https://doi.org/10.1145/3534678.3539364
- 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). https://doi.org/10.1073/pnas.2112182119
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