Monitoring and forecasting the dynamics of disease outbreaks using multiple mathematical approaches, and novel Internet-based data sources. Lessons learned during the COVID-19 pandemic. Mauricio Santillana, Northeastern University/Harvard T.H. Chan School of Public Health Host: Lucas Stolerman
Abstract: I will describe data-driven statistical and machine learning methodologies that leverage Internet-based information from search engines (clinicians and general public), Twitter microblogs, crowd-sourced disease surveillance systems, news alerts, electronic medical records, waste water, 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 communicable disease outbreaks including COVID-19 outbreaks.
To add/edit talks, please log in on the department web page, then return to Announce. Alternatively if you know the Announce
username/password, click the link below: