Title:

Cross-Model Parameter Estimation in Epidemiology

In light of the COVID-19 pandemic, the topics of epidemics and epidemic modelling has become popular to both scientists and the public alike. However, many individuals lack knowledge about matters of epidemic modelling or disease transmission, so they may blindly accept unsubstantiated information. Due to this misinformation, the epidemiological terms R0 and exponential growth rate are used interchangeably, and are often conflated. This thesis is aimed toward investigating whether an exponential function can be used to predict the R0 of an SIR model, and if a functional relationship can be found which connects the parameters of these two different modelling techniques. By simulating SIR models and fitting their initial growth periods exponentially, it is possible to use the exponential growth rate to attempt to calculate R0 using several estimates. None of the estimates obtained an accurate R0 value through the wide variety of SIR parameters tested. Some estimates produced consistently inaccurate results, while the estimate which modeled the direct functional relationship between R0 and the exponential growth rate was unpredictable, and imprecise. Thus, we found it is unreasonable to assume that an estimation of initial growth can be equated to R0, or used to find a reasonable estimate. This result can provide insight into the independent nature of epidemic modelling techniques.

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