Modelling the dynamics of viral load to reveal mechanisms of protection in COVID-19

Variation in the severity of COVID-19 may be due to differences in how well the immune system fights the virus. The most severe cases have the highest levels of virus, which may be driving such a strong immune response that it damages the body. The virus may have reached high levels because it grew very fast or because the immune system did not respond fast enough or in the right way. We can only find out which parts of the immune response are protective if we know how the levels of virus and immune response influence each other over time. We have previously shown that we can use data from lots of patients to develop a model of how the levels of virus and the immune response interact, and then use this as a framework to estimate how effectively the immune response of each individual is controlling the virus. This then allows us to identify which of the thousands of molecules involved of the immune response are actually responsible for protection. Using data which has already been collected in other studies to develop a similar model for COVID-19, we will identify the protective immune response, leading better treatments and vaccines.

Characterising the protective host-response in COVID-19 is a critical step towards developing effective treatments and vaccines. Increasing pathogen load stimulates the host response to infection, but identifying the protective components of the response in humans is challenging in cross-sectional studies. Variation occurs between individuals in both the dynamics of pathogen load over time and the relationship between pathogen load and magnitude of the host response, and this variation can be harnessed to identify correlates of protection. A mathematical model of the relationships between pathogen load and host response can be developed using population data and then used to make quantitative estimates of the model parameters determining pathogen load for individual subjects. Importantly, we have shown that parameter estimates in individuals can then be correlated with measured host factors, to identify biological mechanisms controlling pathogen load. We propose to develop a within-host model of viral load dynamics in COVID-19 and use it to: i) quantify parameters of viral load control for individuals; ii) predict clinical outcome; iii) identify constitutive host characteristics associated with control of viral load; iv) identify components of the blood and mucosal immune responses which control viral load. We build on extensive clinical and biological data generated by ongoing studies, adding value to these projects and expediting their public health impact. Our approach will deliver a prioritized list of host factors which control viral load dynamics, underpinning development of more effective, and potentially personalised, treatment and vaccine strategies.