Author: Ashish Goyal; E. Fabian Cardozo-Ojeda; Joshua T Schiffer
Title: Potency and timing of antiviral therapy as determinants of duration of SARS CoV-2 shedding and intensity of inflammatory response Document date: 2020_4_14
ID: d7stppv5_66
Snippet: We fit different instances of our model in equation 1 to the SARS-COV-2 shedding data using a nonlinear mixed-effects modeling approach (38-40) (See Table S1 ). Briefly, we obtained a maximum likelihood estimation of the population median (fixed effects) and standard deviation (random effects) for each model parameter using the Stochastic Approximation Expectation Maximization (SAEM) algorithm embedded in the Monolix 2019R2 software (www.lixoft.e.....
Document: We fit different instances of our model in equation 1 to the SARS-COV-2 shedding data using a nonlinear mixed-effects modeling approach (38-40) (See Table S1 ). Briefly, we obtained a maximum likelihood estimation of the population median (fixed effects) and standard deviation (random effects) for each model parameter using the Stochastic Approximation Expectation Maximization (SAEM) algorithm embedded in the Monolix 2019R2 software (www.lixoft.eu). For a subset of parameters, random effects were specified, and the standard deviation values were estimated. Measurement error variance was also estimated assuming an additive error model for the logged . We simultaneously fit each model to the viral load data of 25 patients form the four data sets. The parameters associated with the effector cell compartment were only estimated for those study participants who cleared infection during the observed data.
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