Case Study
Pandemic Data Analytics and Risk Assessment
The Challenge
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. From a scientific standpoint, it has also revealed that while epidemic modeling can be useful in assisting decision makers and public health officials, it is still far from being a perfect tool for policy planning.
Visualization of coronavirus multiplying with a background of people at a train station concourse.
We are developing the next generation of forecasting and outbreak analytics tools to help us respond to future epidemic threats by leveraging large-scale, data-driven, epidemic modeling frameworks that combine traditional epidemiological approaches with state-of-the-art artificial intelligence techniques.”
Matteo Chinazzi
Research Associate Professor
Network Science Institute at Northeastern University
The Partnership
Northeastern Sternberg Family Distinguished University Professor Alessandro Vespignani and Research Associate Professor Matteo Chinazzi have been working on improving epidemic modeling by developing large-scale, data-driven, behaviorally informed, analytical and computational frameworks to study and forecast the spatial spread of infectious diseases while also assessing the impact that outbreaks and alternative policy interventions have on the broader socio-economic systems they interact with.
Their work has helped inform public health strategies and provided timely projections and forecasts during the recent 2015-2016 Zika virus epidemic, the COVID-19 pandemic, and the 2022 Monkeypox outbreak. Their research includes collaborations and partnerships with national and international agencies, non-profit organizations, and policy makers.
The Goal
Stochastic modeling of complex physical and social systems, regardless of the field of application (economics, public health, fluid dynamics, or climate modeling) are computationally expensive and time consuming. Nevertheless, generating timely model projections is critical in contexts such as pandemic preparedness and disease outbreak analytics. As part of their future research agenda, Vespignani and Chinazzi are working on developing new solutions that combine the development of large-scale, high-performance, cloud-based computational infrastructures with the creation of new methodological frameworks that employ state-of-the-art deep learning approaches and physics-guided mechanistic models.
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