COVID-19 Data Science Urban Epidemic Modelling in Python

In this lecture, you will obtain a good intuition behind the maths of the spatial epidemic models.

In this lecture, I will walk you through the equations of the spatial epidemiological model, explaining every detail step-by-step, to make sure you completely understand. After this lecture, the seemingly complicated equations will seem a piece of cake for you!

In this lecture, you will set up the Jupyter Notebook for coding the equations and will conduct a quick exploratory data analysis of the spatio-temporal Origin-Destination matrices and population densities so that you have a firm understanding of the structure of the arrays containing them. This will enable you to use them in the epidemic modelling to capture spatio-temporal urban mobility.

In this video, I will teach you how to initialize the variables to be used in the spatial epidemic equations and how to randomly insert infections into the city!

In this lecture, you will be introduced to coding the lockdown strength and learn why normalising matrices by rows can often be very useful!

In this video, you will learn the solution to the previous exercise and learn important Numpy tricks and tips for writing more efficient code!

In this lecture, you will code the epidemic equations themselves, understanding how the spatio-temporal movements in a city can be represented with efficient Numpy operations.

In this video, you will finish up coding the SEIR equations and will get ready for using the script you have created to run simulations in the next section!

In this video, you will start with the Jupyter Notebook for the spatial simulations of the epidemic in a city, and will learn how to set up the model with the correct parameters corresponding to Covid-19.

In this lecture, you will run the simulation of the Covid-19 epidemic spreading in the city and will visualise the first aggregated results by plotting the epidemiological curves.

You will also gain a more in-depth understanding of the model parameter specifying the lockdown strength and see why it's tricky.

In this video, you will start preparing everything for the animated spatial visualisation. In particular, you will load the city grid file and change its coordinate reference system for plotting. I will also walk you step-by-step through understanding how the Numpy arrays storing the simulation results are structured so you have a clear idea of how to use them in your visualisations!

In this video, you will start writing the code for the spatio-temporal visualisation of the urban epidemic! You will learn how to set up the initial plotting parameters and how to make sure the colors in the final animation are anchored to the same color intensities!

In this video, you will learn how to use Matplotlib methods to create custom colormaps that fade into transparency for nice visualisations to obtain a smooth and nice effect in the final animated map.

In this video, you will continue writing the code for plotting the epidemic dynamics. You will learn how to use the Contextily library to add a nice neutral map in the background.

In this video, you will start building an inset plot in the corner of the animated map for tracking the evolution of the epidemiological curves over time.

In this video, you will improve the inset plot by adding small graphical elements to make the curves look nicer!

In this video, you will finish the code for the animated spatial visualisation and start collecting the image frames for making the final animation in the next video!

In this final video, you will learn how to use the imageio Python library to create animated gifs from a collection of images and will thus obtain the final spatio-temporal visualisation of the Covid-19 epidemic spreading in a city!

In this extra video, you will be introduced to the field of urban data science, get a taste of what kind of problems it addresses, see some visualisations, and be encouraged to stay tuned for future courses on these topics. Enjoy!