COVID-19 patient data from a study in Singapore curated for input into an in silico infection model

Within-host models of COVID-19 infection dynamics enable the merits of
different forms of antiviral therapy to be assessed in individual
patients. A stochastic agent-based model of COVID-19 intracellular
dynamics is introduced here, that incorporates essential steps of the
viral life cycle targeted by treatment options. Integration of model
predictions with an intercellular ODE model of
within-host infection dynamics, fitted to patient data, generates a
generic profile of disease progression in patients that have recovered in
the absence of treatment. This is contrasted with the profiles obtained
after variation of model parameters pertinent to the immune response, such
as effector cell and antibody proliferation rates, mimicking disease
progression in immunocompromised patients. These profiles are then
compared with disease progression in the presence of antiviral and
convalescent plasma therapy against COVID-19 infections. The model reveals
that using both therapies in combination can be very effective in reducing
the length of infection, but these synergistic effects decline with a
delayed treatment start. Conversely, early treatment with either therapy
alone can actually increase the duration of infection, with infectious
virions still present after the decline of other markers of infection.
This suggests that usage of these treatments should remain carefully
controlled in a clinical environment.