1 credit
Fall 2025 Lecture Distance Learning
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. Namely, the course consists of three modules: 1) Networked Virus Models, 2) Limiting Behavior of Networked Virus Models, and 3) Parameter Identification & Mitigation Algorithms. Prerequisites: ECE 60281 and ECE 60282.
Learning Outcomes1Differentiate between distinct networked compartmental models for epidemics (SI, SIS, SIR, etc.) in order to identify the best model for a given scenario.
2Analyze 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.
3Estimate model parameters from data for the different networked epidemic models.
4Employ the estimated model parameters to forecast the spread of an outbreak.
5Choose 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.
6Develop and implement mitigation algorithms for the different models of epidemic processes.
7Leverage the advantages and disadvantages of using networked models of epidemics processes versus group models in a given situation.
Restrictions
made by Purdue students.