Understanding viral infections using mathematical models and statistical analysis

There are a range of viruses that exist, that not only infect humans, but also a wide range of other species. These viruses not only present a threat to human health but also to economies, in particular, less economically developed countries. Mathematical models and statistical analysis provide ways to understand viral dynamics and immune responses better and test new hypotheses using information gathered through biological experimentation. In Chapter 3, I will analyse two cohort studies from viral outbreaks. The first study is on data from the 2014 West Africa Ebola outbreak collected by Public Health England (now known as the United Kingdom Health Security Agency) to understand the longitudinal antibody and T-cell response of survivors from the epidemic. St Jude Children’s Research Hospital undertook the second study to understand possible associations between common coronaviruses and SARS-CoV-2, using data collected within the hospital. Chapter 4 introduces three potential stochastic models to investigate type I interferon (IFN) antagonism, a tactic employed by several viruses, including SARS-CoV-2, Ebola and Crimean Congo Haemorrhagic fever to avoid immune responses. Here Ebola virus is investigated as a case study. Finally, in Chapter 5, I examine two mathematical models for viral infections. The first model explores defective interfering particles as a potential therapeutic in SARS-CoV- 2 infections. The second model investigates foot and mouth disease infections in vitro to examine differences between three strains, including determining each strain’s time to infection and basic reproduction number.