Table 1 Results of the ODE model fit parameters. Parameter \(c\)* was estimated using linear regression via scikit-learn’s least squares method (Version 1.3.0). A first pass for fitting βm, ε, p and tir† was done using the curve fit feature Berkeley Madonna (Version 8.3.18) using the values for the other parameters given in Sect. 2.2.1. Parameters βm, p, and ε were fit in Python with 95% confidence intervals in brackets. #Value for p for M1 was estimated without confidence. The upper bound for the confidence interval was infinity. tir† could not be fit with confidence, hence we had to base our estimates with Berkeley Madonna. \({\beta}_{e}=1\times{10}^{-11}\) mL/virions/day (\(4.17\times{10}^{-13}\) mL/virions/hour).
Parameter | Symbol [unit] | ODE estimates | |||||
|---|---|---|---|---|---|---|---|
M1 | M2 | M3 | M4 | M5 | Median [Min, Max] | ||
Virion clearance rate | \(c\)* [1/hr] | 0.93 | 0.87 | 0.86 | 0.85 | 0.90 | 0.87 [0.85, 0.93] |
Infection rate of virions produced by the mouse | βm (10− 11) [mL/virions/hr] | 3.2 [0.8–6.5] | 5.1 [1.86–13.1] | 6.5 [2.3–17.8] | 10.7 [5.2–22.5] | 2.3 [0.8-5.0] | 5.1 [2.3, 10.7] |
Virion production rate before tir | p [virions/cell/hr] | 15.4# | 11.2 [4.7–28.9] | 8.1 [3.1–21.5] | 8.3 [4.0-16.7] | 27.1 [10.3–81.3] | 11.2 [8.1, 27.1] |
Time at virion production drops | tir† [day] | 3.5 | 3.0 | 2.8 | 1.7 | 2.8 | 2.8 [1.7, 3.5] |
Efficacy in blocking viral production | ε | 0.92 [0.88–0.96] | 0.92 [0.87–0.95] | 0.91 [0.85–0.94] | 0.91 [0.85–0.94] | 0.95 [0.92–0.97] | 0.92 [0.91, 0.95] |
Virion production rate after tir | p (1-ε) [virions/cell/hr] | 1.23 | 0.90 | 0.73 | 0.75 | 1.36 | 0.90 [0.73, 1.36] |
