This course on Computational Epidemiology aims to teach students the role of big data and pervasive informatics in understanding and combating epidemics. The learning outcomes include understanding mass action compartmental models, networked epidemiology, graphical dynamical systems, and disease progression models. The course covers the pros and cons of different epidemiological approaches and introduces Simdemics, a computing environment for real-time networked epidemiology. The teaching method involves lectures, case studies, and discussions on topics such as ILI prediction pipelines and vaccine allocation strategies. This course is intended for individuals interested in epidemiology, public health, data science, and computational modeling.
https://www.classcentral.com/course/youtube-stanford-seminar-computational-epidemiology-the-role-of-big-data-and-pervasive-informatics-110044
Leave a Reply