Introduction to Computational Thinking with Julia, with Applications to Modeling the COVID-19 Pandemic

Course Materials

All of the materials linked on this page are available on the original GitHub site developed for the course.

Lectures	Slides	videos	jupyter notebooks	other resources
1. Analyzing COVID-19 Data (March 30, 2020)	Welcome Slides Lecture 1 Slides	Welcome Video Data Analysis	Exploring Data on Covid-19	Video: 3Blue1Brown:Simulating an Epidemic  Video: How to Tell If We’re Beating COVID-19 Blog: COVID-19 in Denmark
2. Modelling Exponential Growth (April 1, 2020)	Lecture 2 Slides	Lecture 2 Video	Exponential and Logistic Growth	Covid-19 Trajectory xkcd comic “Scenario 4”
3. Probability (April 6, 2020)	Lecture 3 Slides	Lecture 3 Video	Modelling Recovery with Probability	<none>
4. Random Walk Models (April 8, 2020)	Lecture 4 Slides	Lecture 4 Video	Random Walks	<none>
5. Characterizing Variability (April 13, 2020)	Lecture 5 Slides	Lecture 5 Video	Variability and Custom Types	Video: Covid-19 3 Blue 1 Brown
6. User-defined Types (April 15, 2020)	Lecture 6 Slides	Lecture 6 Video	Defining New Types	Epidemic Calculator
7. Markov Chains and Continuous Random Variables (April 22, 2020)	Lecture 7 Slides	Lecture 7 Video	Markov Chains and Continuous Random Variables	<none>
8. Continuous Time (April 27, 2020)	Lecture 8 Slides	Lecture 8 Video	Continuous Time	<none>
9. Exponential Distribution (April 29, 2020)	<none>	Lecture 9 Video	Exponential Distribution	<none>
10. Differential Equations (May 4, 2020)	Lecture 10 Slides	Lecture 10 Video	<none>	<none>
11. Optimization and Fitting to Data (May 6, 2020)	Lecture 11 Slides	Lecture 11 Video	<none>	<none>
12. Networks in Epidemic Modelling (May 11, 2020)	Lecture 12 Slides	Lecture 12 Video	Networks	<none>

Code content is licensed under the MIT license.

Text content is licensed under the CC BY-NC-SA license.