Imperial College London MEng Final year project Uncertainty Quantification of Epidemic Phenomena and the Parallel Simulator Tool

Technological and industrial advances allows for biological and non-biological epidemics to spread faster than the world has ever seen. The analysis of epidemiological models and uncertainty quantification represents one of the best strategies for the control and management of infectious diseases. The main contributions of this project are a comparison between simulated and analytically derived measures (mean, variance, skewness) regarding the infected counts of an epidemic and a parallelised tool that can provide the user with a quick visualisations of the particularities of their chosen compartmental model. The report will detail the approach taken in deriving both analytical and simulated measures along with a discussion regarding the implementation of the Parallel Simulator Tool. This project provides valuable insight regarding the potential of using analytically derived measures to accurately characterise compartmental models. However, the methods described have limitations due to the approximations made while deriving the mathematical formulas of the aforementioned analytical measures. Further work in this area could provide a considerable reduction in the computational costs currently associated with epidemiological analysis. In addition, the Parallel Simulator Tool can be improved to further assist these investigations.