Previously epidemiologists have not relied on topological data analysis to model disease spread. Instead, they have used variants of the SIR compartmental model from classic epidemiology theory and stochastic models to analyse disease spread and prevention methods. These are often applied to obtain estimates of the basic reproduction number, a metric of how infectious a disease is. However, these frameworks require reliable estimates of epidemiological parameters such as the vector disease transmission rate, reporting rates and vector abundance. Time series models such as ARIMA are another class of models that are often used by researchers to study the spread of vector-borne diseases, however acquiring granular time-series data over extended periods can be difficult. This project will focus on conducting a controlled analysis of topological data analysis in epidemiology and study the topological features of disease data. By using techniques from topological data analysis, it is envisioned that better predictions will be made about how disease will spread.

It is hoped that this project will show how topological data analysis is useful and effective at helping to overcome the issues faced in more traditional modelling methods and provide better predictions about how disease will spread. This project will hopefully show how topological data analysis can be considered for use as an extra tool in epidemiology.