Epidemics Over Networks
Learning Objective:After completing this course, you will be able to:
- Differentiate between distinct networked compartmental models for epidemics (SI, SIS, SIR, etc.) in order to identify the best model for a given scenario.
- Analyze the limiting behavior of networked models for epidemic processes by identifying the different possible equilibria of the models and specifying conditions for converging to different equilibria.
- Estimate model parameters from data for the different networked epidemic models.
- Employ the estimated model parameters to forecast the spread of an outbreak.
- Choose the best networked model for a given scenario/dataset by employing their knowledge of the epidemic models and by comparing the fit from the estimated parameters and the forecast accuracy.
- Develop and implement mitigation algorithms for the different models of epidemic processes.
- Leverage the advantages and disadvantages of using networked models of epidemics processes versus group models in a given situation.
This course presents a class of epidemic models from a network science, control theoretic, and data science perspective. Networked epidemiological ideas will be explored combined with probability theory and systems theoretic ideas to be able to capture spread behavior, learn the behavior from data, and design mitigation techniques.
The course consists of four modules: 1) Group Virus Models, 2) Solutions and Limiting Behavior, 3) Model Parameter Identification, and 4) Mitigation Algorithms.
Topics Covered:Automatic Control (AC)
Prerequisites:ECE 695, Introduction to Mathematical Fundamentals for Systems & Control Theory (C- or higher)
ECE 695, Epidemic Processes (C- or higher)