Matteo Chinazzi

Matteo Chinazzi is a Research Associate Professor at Northeastern University, affiliated with the Roux Institute and serving as core faculty at the Network Science Institute. His interdisciplinary work bridges network science, data science, epidemiology, economics, and artificial intelligence to develop data-driven frameworks for public health decision-making and policy evaluation.

His academic credentials include:

• PhD in Economics from Sant’Anna School of Advanced Studies (Pisa, Italy)
• M.Sc. in Economics and Social Sciences from Bocconi University
• Bachelor’s degree in Economics (CLEMIT) from Bocconi University

Chinazzi’s research centers on computational epidemiology and network-based pandemic modeling. He develops large-scale frameworks integrating human mobility data, contact patterns, and policy interventions to forecast infectious disease spread. Key methodologies include agent-based modeling, mechanistic-ML hybrid systems, and real-time epidemic intelligence platforms. His work critically examines socio-economic determinants of health, vaccine equity, and the health-economy trade-offs during pandemics, with direct applications to COVID-19 response strategies.

Recent publications reveal a pronounced shift toward multi-fidelity modeling, Bayesian calibration techniques, and ensemble forecasting systems for pandemic scenarios. His work increasingly focuses on integrating wastewater surveillance, aircraft mobility networks, and variant dynamics into operational forecasting tools while addressing equity dimensions in intervention impacts. The consistent use of high-resolution mobility data and machine learning underscores his approach to bridging theoretical models with real-world public health needs.

Chinazzi leads the Global Epidemic and Mobility model (GLEAM) project and coordinates the Network Science Institute COVID-19 Mobility initiative, which provide foundational tools for real-time pandemic monitoring. These efforts involve cross-institutional collaborations analyzing de-identified location data to quantify mobility changes, physical distancing compliance, and policy effectiveness during health emergencies.