Statistics and Modeling with Novel Data Streams
Meeting Times:
- Monday, July 22, 8:30 AM – 5:00 PM
- Tuesday July 23, 8:30 AM – 5:00 PM
- Wednesday July 24, 8:30 AM – 12:00 PM
Classroom: Randall Rollins Building (RR 226)
Module Summary:
This module focuses on digital data sources and novel data streams such as geo-localized population and mobility data, wearable devices, web participatory platforms and web search data or social media updates. We will provide an introduction to different digital data sources and technical challenges in their collection, storage, and analysis. We will review the integration of digital data sources with statistical and mechanistic modeling of infectious diseases.
The course will provide an introduction to the use of novel data streams time series for epidemic forecasting. We will describe the construction of synthetic populations and the calibration of highly detailed individual based models.
Prerequisites:
This module assumes knowledge of probability and inference covered in an introductory statistical course. This module assumes knowledge of the material in Module: Mathematical Models of Infectious Diseases, though not necessarily from taking that module. Familiarity with a programming language is expected (Python, R, Matlab or other).
Module Content:
Instructors
Mauricio Santillana, PhD
Professor, Department of Physics, Northeastern University
Mauricio Santillana, PhD, MSc is the director of the Machine Intelligence Group for the betterment of Health and the Environment (MIGHTE) at the Network Science Institute at Northeastern University. He is a Professor at both the Physics and Electrical and Computer Engineering Departments at Northeastern University, and an Adjunct Professor at the Department of Epidemiology, T.H. Chan Harvard School of Public Health.
Alessandro Vespignani, PhD
Sternberg Family Distinguished Professor, Department of Physics, Northeastern University
Alessandro Vespignani research activity is focused on the study of “techno-social” systems, where infrastructures composed of different technological layers are interoperating within the social component that drives their use and development. In this context we aim at understanding how the very same elements assembled in large numbers can give rise – according to the various forces and elements at play – to different macroscopic and dynamical behaviors, opening the path to quantitative computational approaches and forecasting power.
Dr. Vespignani is Director of the Network Science Institute at Northeastern University.
Required Software:
Recommended Reading:
Primary research and tutorial articles will be provided for additional reading.
