Figure 4. The Extended Immune-Response Model Fits the Available In Vivo Data and Does Not Change the Optimal Latency Probability for Resting Cells, $p_{\text{lat}}^{\text{opt}}(0)$.

(A) Dynamics of cell compartments during systemic infection calculated from Equations [S9] and [S10]. Antiretroviral therapy (ART) initiated during steadystate infection causes a decline of the latent reservoir (L). The saturation of the fall in the latent reservoir is due to the decline in immune cells (E) during ART. (Data points across human patients) Virus load prior to ART (Fraser et al., 2007) (green triangles); latent cells prior to ART (Chun et al., 1997b) and after highly active ART (Finzi et al., 1997) (cyan triangles); effector CD8 T cells (Turnbull et al., 2009) (red triangles). For each data set (triangles), box-and-whisker plots show the upper and lower quartiles of the patient data. (Blowout) Virus load after the onset of ART (Markowitz et al., 2003) (green triangles, error bars show SD).

(B) Normalized transmission rate p_transmission as a function of p_lat(0) calculated from the dynamics in A and Equation [1]. Two cases are shown for comparison: with immune cells (E, green triangles) and without immune cells (E = N = 0, blue curve). Inclusion of immune cells into the model only weakly affects the prediction of a large optimal latency probability for resting cells, $p_{\text{lat}}^{\text{opt}}(0)~0.5$. Model parameters in A and B are in Tables S1 and S2 (with R₀^LT = 15 and p_lat(0) = 0.5 in A). See also Figure S3.