MfPH Shared Graduate Course: Machine Learning Statistical Methods for Disease Transmission Modelling
Instructor: Prof. Nathaniel Osgood
Email: nathaniel.d.osgood@gmail.com
Course Dates: January 7th - April 8th, 2022
Registration Deadline: January 17th, 2022
Lecture Times: Mondays and Fridays | 10:30 am - 12:00 pm (CST)
Office Hours: Mondays and Fridays | 12:00 - 1:00 pm (CST)
Course Overview
In recent decades, public health and health care decision making with respect to communicable disease has increasingly been impacted by two versatile and deep computational modeling traditions: Systems Science (particularly via transmission modeling) and Data Science (particularly via machine learning & computational statistics, and increasingly with aspects of “Big Data”). While both Systems Science and Data Science constitute cutting-edge computational traditions offering great promise for fine-temporal grained longitudinal understanding across multiple pathways of complex systems, these two traditions are often pursued in parallel rather than in a joint manner. This course systematically characterizes the theory and practice of cross-leveraging transmission modeling and machine learning and computational statistics, and demonstrates how such approaches support leveraging and informing transmission modeling with both traditional data sources and “big data” characterized by high volume, velocity, variety and veracity.
The course's recorded lectures can accessed through the following playlist.
