An Epidemiological Simulation of COVID-19

By Miles Spence

Faculty Mentor: Professor Jennifer Polack

This project is a cross-section of multiple disciplines in mathematics as well as computer science. In particular, it incorporates differential equations and data analysis to create a model, in the Python coding language, to estimate the spread of COVID-19. The model used for the simulation is a self-generated SIRDSV deterministic compartmental model. The presenter explains the SIR model and how it has been altered to fit the current context with COVID-19. The presenter also explains where data is found and how it is manipulated to fit in the model. In addition, the presenter demos the program that gathers the data, manipulates the data, checks the accuracy of the model, estimates the spread of COVID-19 in the next thirty days, and then finally estimates the spread of COVID-19 in one hundred days with decreased stringency. The model proves to be accurate in some cases and only breaks down over a long period of time and with changing rates and probabilities.