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

Citation

Fatehi, Farzad et al. (2021), COVID-19 patient data from a study in Singapore curated for input into an in silico infection model, Dryad, Dataset, https://doi.org/10.5061/dryad.sn02v6x38

Abstract

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.

Methods

Viral load (V) values for 12 patients from a COVID-19 study in Singapore (reported in 10.1001/jama.2020.3204) were curated for input into our in silico infection model. Viral load values in this study are measured in Cycle threshold (Ct) values and presented in a table (eTable 2). In order to use these data as input, we converted Ct values to the number of free virus particles per ml (V) using the formula log10(V)=−0.3231*Ct+14.11, and then used those V values for our analysis. We provide here the file Curated_Patient_Data.txt, that we used as input for our algorithm. This file contains the following information, adapted from the above-mentioned study: The first column gives the patient ID and ranges from 1 to 12; the second column indicates the time of the measurement after the onset of symptoms, considering the onset of symptoms as day 0; the third column gives the measurement from (10.1001/jama.2020.3204) in Ct values; the fourth and fifth columns indicate the log10(V) and V values, where V is the number of free virus particles per ml. Note that columns four and five are calculated by converting Ct values as described above.

Funding

Wellcome Trust, Award: 110145

Wellcome Trust, Award: 110146

Engineering and Physical Sciences Research Council, Award: EP/R023204/1

Royal Society, Award: RSWF/R1/180009