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.
Stanford Seminar - Computational Epidemiology: The Role of Big Data and Pervasive Informatics
Stanford University via YouTube
Overview
Syllabus
Introduction.
Acknowledgements.
Objectives for today's lecture.
What is computational epidemiology.
Epidemics in history.
Recent example: Ebola outbreak in Africa.
Goal: Real-time epidemic science.
Mass action compartmental models.
Pros and cons of compartmental models.
An alternative approach: Networked Epidemiology.
Amathematical framework: Graphical Dynamical Systems (GDS).
Epidemiological problems reduce to reasoning over the phase space P(G,F).
Pros and cons of networked epidemiology.
Simdemics: A computing environment for real- time networked epidemiology.
Elements of networked epidemiology.
Realistic synthetic contact networks.
Big-data challenge.
Networks are dynamic & relational.
Disease progression models.
HPC simulations.
Selected case studies.
ILI prediction pipeline: Data driven statistical models.
Vaccine allocation.
Strategies for targeted vaccination.
Performance of group based strategies.
Summary and key insights.
References.
Taught by
Stanford Online
