Graduation Year


Document Type

Campus Only Senior Thesis

Degree Name

Bachelor of Arts



Reader 1

Jo Hardin

Reader 2

Christina Edholm

Rights Information

© 2021 Anne L Cohen


This thesis is an exploratory analysis of an SEIRMD epidemic model created by professors at the Claremont Colleges and applied to Los Angeles county COVID-19 data. The paper investigates the accuracy of predictions given different techniques for parameter optimization and decisions throughout the modeling process. The research also explores how noise affects the estimations of COVID-19 cases, deaths, and hospitalizations through simulation. Visualizations are used to compare the differential equation solutions with actual data, as well as plot the parameter values over time. The research finds that predictions are most accurate when the time interval of the predicted data is close to the data with which the parameters were fit, likely due to the variability of the COVID-19 data causing parameter values to change over time. However, while larger trends affect the predictions, random noise in the data does not appear to have a large effect on the model predictions.

This thesis is restricted to the Claremont Colleges current faculty, students, and staff.