SUMMARY
The data science revolution is finally enabling the development of large-scale data-driven models that provide real- or near-real-time forecasts and risk analysis for infectious disease threats. These models also provide rationales and quantitative analysis to support policy-making decisions and intervention plans. At the same time, the non-incremental advance of the field presents a broad range of challenges: algorithmic (multiscale constitutive equations, scalability, parallelization), real-time integration of novel digital data streams (social networks, participatory platform, human mobility etc.). I will review and discuss recent results and challenges in the area, and focus on ongoing work aimed at responding to the COVID-19 pandemic.

SPEAKER
Alessandro Vespignani is Sternberg Family Distinguished University Professor at the Northeastern University in Boston. He is the founding director of the Network Science Institute and leads the Laboratory for the Modeling of Biological and Socio-technical Systems. In May 2017 he became member of the External Faculty of CSH. His research interest is the interdisciplinary application of statistical and numerical simulation methods to study biological, social and technological networks. His recent work focuses on modeling the spatial spread of epidemics, including the realistic and data-driven computational modeling of emerging infectious diseases, the resilience of complex networks, and the collective behavior of techno-social systems.

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