Multi-scale modelling of COVID-19 transmission, with application to new and on-going vaccination strategies

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The work I intend to undertake will be related to the mathematical sciences area of research, as the work studies biological systems through the lens of mathematical equations. Conclusions of the project could affect the development of roll out of future vaccines. The core concept is to use mathematical modelling to determine the short term evolution of individual's antibody repertoires through multiple SARS-CoV-2 infection and vaccinations. The mathematical models used will be parsimonious and intuitive, with a focus on parameter identifiability. The current idea is to use a predator-prey model structure for studying the development of antibody repertoires with the main competition being between naive and memory B-cells in lymphoid tissue. This model will be informed by detailed antibody data collected from individuals with a known infection history. As the project progresses I intend to consider the order of infection and vaccination, as well as which variants an individual is infected with. Further, the hypothesis of cross reactive antibodies already present in the population will be tested. The results of these individual level models will be used to inform population level models that aim to understand how repeat infection and vaccination protect the population from disease and infection. The intention of this analysis is to gain information about the longevity of protection from SARS-CoV-2 and to test hypothesis about whether, and in what form, it will be endemic. The novelty of this research stems from the methods being employed and their use of a wealth of available data. In the context of past research into SARS-CoV-2 infections no within-host model has considered the interplay between the competitive B-cell development in lymphoid tissue and the subsequent retrieval of virus by the antibodies the B-cells go onto produce. Further, the application of existing within-host models have mostly been to understand disease progression and risk of hospitalisation so focus damage to the host. As a consequence existing models often do not explicitly model immune process, but only their effects of virus and host cells, or if they do model immune process they are the ones that are related to cell damage, such as cytokine and cytotoxic t cells. Older models that are not specific to SARS-CoV-2 have tried to capture the process of the B-cell development. However, these models neglect the site of infection and make inaccurate assumption about the dynamics outside the lymphoid tissues. Further, these older models do not usually validate their conclusions with data. Where as, my parsimonious model captures both the competition in lymphoid tissue and relevant dynamics at the site of infection, and is observable and so can be validated by data. These properties make it more useful for understanding the effects of vaccination and informing the future vaccination campaigns.