Statistics and Modeling with Novel Data Streams
Meeting Times:
- Monday, July 20, 9:00 AM – 5:00 PM
- Tuesday July 21, 9:00 AM – 5:00 PM
- Wednesday July 22, 9:00 AM – 12:00 PM
Classroom: TBA
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.
Shihao Yang, PhD
Harold E. Smalley Early Career Professor and Assistant Professor
H. Milton Stewart School of Industrial & Systems Engineering, Georgia institute of Technology
Dr. Shihao Yang is Harold E. Smalley Early Career Professor and Assistant Professor in Industrial & Systems Engineering at Georgia Tech. He completed his PhD in Statistics at Harvard University and post-doc in Biomedical Informatics at Harvard Medical School. Dr. Yang’s research focuses on data science, with special interest in time series, dynamical systems, and infectious disease transmission forecasting.
Required Software:
Recommended Reading:
Primary research and tutorial articles will be provided for additional reading.