Quantifying the Effect of Disease Characteristics on the Outcomes of Interventions Using Mathematical Modelling

Many emerging diseases have several common features in terms of their natural history; however, they differ in their quantifiable characteristics, such as transmissibility and infectiousness. These characteristics are crucial in determining whether there will be a local outbreak of the disease or if it has the potential to evolve into a global pandemic. Understanding these characteristics is essential in devising public health policies to prevent the repercussions of novel diseases, such as those seen during the COVID-19 pandemic. This thesis presents a general modeling framework for the transmission dynamics of influenza and SARS-CoV2, examining the impact of their characteristics on intervention outcomes. Simulations and sensitivity analysis show that the length and infectiousness profile during various stages of illness significantly affect intervention outcomes. The results suggest that the longer and more infectious pre-symptomatic stage of SARS-CoV-2 compared to influenza may explain the difference in school closure outcomes between the two diseases.