Learning Objective:After completing this course, you will be able to:
- Differentiate between distinct compartmental models for epidemics (SI, SIS, SIR, etc.) and identify the best model for a given scenario.
- Analyze the limiting behavior of 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 epidemic models.
- Employ the estimated model parameters to forecast the impact of an outbreak.
- Choose the best 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.
This course provides a control theory and data science approach to traditional epidemic models. Traditional epidemiological ideas will be explored and combined with probability theory and systems theoretic ideas to be able to capture spread behavior, learn 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.