Explore computational epidemiology and its role in addressing global health challenges through this Stanford seminar. Delve into the evolution of epidemic modeling, from mass action compartmental models to networked epidemiology. Examine the use of Graphical Dynamical Systems (GDS) and the Simdemics computing environment for real-time epidemic analysis. Investigate case studies on influenza-like illness prediction and vaccine allocation strategies. Gain insights into the intersection of big data, pervasive informatics, and public health, with a focus on recent outbreaks like Ebola in Africa. Learn about the advantages and limitations of various epidemiological approaches, and discover how high-performance computing simulations contribute to understanding disease progression and developing targeted interventions.
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
