COVID-19 virtual patient cohort suggests immune mechanisms driving disease outcomes
Fig 2
Viral dynamics model fit to human viral data from hospitalized patients in Singapore and Germany.
A reduced version of the full model (all cytokine and immune cells set to 0, Eqs 6–9) was fit to data from hospitalized patients, after initial estimation from viral loads in macaques [47] (S6 Fig) to estimate preliminary viral kinetic parameters. A) Virus (V) infects susceptible cells (S) making infected epithelial cells (I) which then die to produce dead cells (D) and new virus. B) Viral load data (log10(copies/mL) from eight human patients (three from Singapore S5, S6 and S18, and five from Germany, G1, G2, G5, G6, G7) were digitized from previous results [50], and parameters from the viral dynamics submodel were estimated using a non-linear squares optimization routine. β, dI, V0 and dV were estimated from the reduced viral dynamics model in A) (see Methods and S1 Table). Individual patient measurements are depicted by coloured circles. Solid black line: average model prediction; grey shaded region: predicted standard deviation from average. S (time axis) indicates the day of symptom onset.