Data-driven machine learning approaches to monitor and forecast the dynamics of disease outbreaks

This event will feature Mauricio Santillana and be hosted by New York University’s Center for Urban Science and Progress. Learn more about the event here. Register here.

I will describe data-driven machine learning methodologies that leverage Internet-based information from search engines, Twitter microblogs, crowd-sourced disease surveillance systems, electronic medical records, and weather information to successfully monitor and forecast disease outbreaks in multiple locations around the globe in near real-time. I will present how these approaches can be used to build early warning systems to anticipate COVID-19 outbreaks.