In response to the ongoing COVID-19 outbreak, we extended the Global Epidemic and Mobility model (GLEAM) to incorporate the effects of travel restrictions, non-pharmaceutical interventions, age-structured contact patterns, and vaccination campaigns to study, project, and forecast the evolution of the COVID-19 pandemic. Read more..
We combine traditional epidemic modeling approaches with state-of-the-art machine learning and deep learning methods to improve forecasts, accelerate large-scale stochastic simulations, and reconstruct the early stages of an epidemic. Read more..
We introduce a multiscale modeling approach to study the diffusion and impact of SARS-CoV-2 at both global and local scale combining epidemic models that work at different geographical resolutions. Read more..
The Global Epidemic and Mobility project, GLEAM, combines real-world data on populations and human mobility with elaborate stochastic models of disease transmission to deliver analytic and forecasting power to address the challenges faced in developing intervention strategies that minimize the impact of potentially devastating epidemics. Read more..
We characterize collective physical distancing—mobility reductions, minimization of contacts, shortening of contact duration—in response to the COVID-19 pandemic by analyzing de-identified location data for a panel of over 5.5 million anonymized, opted-in U.S. devices. Read more..
Scientific discoveries do not occur in vacuum but rather by connecting existing pieces of knowledge in new and creative ways. Mapping the relation and structure of scientific knowledge is therefore central to our understanding of the dynamics of scientific production. In this project we introduce a new approach to generate scientific knowledge maps based on a machine learning approach. Read more..
In this project we use a data-driven global epidemic model to project the spatio-temporal spread of Zika virus in the Americas during the 2015-2016 epidemic. Read more..