Mathematical model to simulate SARS-CoV-2 infection within-host

Mathematical models are vital in advising strategy when dealing with pandemics, from helping to develop individual treatment strategies to guiding the national public health approach. Current modelling efforts concentrate on transmission and do not focus on variation within the host. There is clear evidence that some subgroups of patients are likely to have more severe disease and poorer outcomes. The reasons for these associations are not clear.

We have developed a within-host mathematical and computational model of SARS-CoV-2 infection that is capable of simulating viral spread in lung cells. Preliminary results illustrate how our model is able to study, in isolation, particular immune dysfunctions associated with severe COVID-19. This is difficult to achieve with biological experiments. Our results have suggested that impairing the function of Natural Killer (NK) cells, important for combatting viral infections, skews the immune response in ways that cause severe disease. Additionally, our model shows that manipulating the levels of defence molecules that immune and infected cells produce to try and fight the infection can lead to severe viral infection, similar to that observed in severe COVID-19 patients.

We have laid important groundwork for future code development in this project; parameterisation and validation, and application. In terms of application, we intend to investigate the influence of initial (and continual) viral load deposition (amount of virus that initiates infection) on the spread of infection. Additionally, we aim to consider more in-depth models of the production of defence molecules, known as cytokines (in particular a cytokine known as type I interferon). We will investigate their dysfunction in their regulatory pathway and their impact on the spread of infection. The model will enhance our understanding of COVID-19 pathophysiology. In this project we will integrate mathematical models that simulate drug distribution in the body. This will allow us to test alternative treatment strategies, such as various drug scheduling and dosing intervals, and refine therapy for specific subsets of patients. Many people remain vulnerable to this infection, but greater knowledge of how to deliver successful treatment strategies will provide hope for those who become critically unwell. It will also diminish the suffering of those who experience non-critical, but nonetheless unpleasant, disease. Understanding gained from our model simulations may also lead to improved management of the long-term effects of COVID-19 (long COVID).

The nature of the model will allow us to investigate why different subgroups are at greater risk, and why they are perhaps most likely to become infected. This can inform public health strategy to protect the most vulnerable members of society. On completion of this project, there is scope to link our within-host model with population-level and environmental models. This could help us to understand more about the course of infectiousness in individuals, aiding guidance around self-isolation and ultimately helping to reduce transmission. It will also help to understand how and why there is heterogeneity in different subsets of patients' transmissibility.

Using our mathematical framework, we will also create a mathematical tool that will allow other infectious disease researchers to model the within-host dynamics of newly emerging pathogens in the future.