Daily school absence prevalence (years 7–11) and the weekly proportion of samples positive for RSV and Rhinovirus from Respiratory Datamart.

<p>Fig 3 Note: Rhinovirus data for weeks 40 (69%) and 41 (55%) in 2011/12 season plotted at 50% to preserve axis scales. Data underlying this graph are available at UCL Discovery.[<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0146964#pone.0146964.ref014" target="_blank">14</a>]</p

Daily school absence prevalence (years 7–11), weekly influenza like illness consultation rates to sentinel general practices in England (all ages), and weekly proportion of samples positive for influenza A & B (all ages) from Respiratory Datamart.

<p>Fig 2 note: data underlying this graph are available at UCL Discovery.[<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0146964#pone.0146964.ref014" target="_blank">14</a>]</p

Daily school absence prevalence (years 7–11) and the weekly number of laboratory confirmed cases of Norovirus.

<p>Fig 4 Note: Data underlying this graph are available at UCL Discovery.[<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0146964#pone.0146964.ref014" target="_blank">14</a>]</p

Univariable linear regression models examining association between weighted school absence prevalence (years 7–11) and surveillance data for respiratory infections and Norovirus.

<p>Univariable linear regression models examining association between weighted school absence prevalence (years 7–11) and surveillance data for respiratory infections and Norovirus.</p

Number of schools submitting absence data each week.

<p>Number of schools submitting absence data each week.</p

Diagram of the within-host model for disease progression of HIV-1 infected individuals.

<p>For the treatment regimen of primary interest (blue diagram), the adherence model dictates the daily intake of ART. Through a pharmacokinetic (PK) and pharmacodynamic (PD) model, the plasma drug concentration and drug effect are determined. Drug effect influences the inter-dependent and time-evolving plasma HIV-1 RNA level and CD4+ T cell count in the basic HIV-1 dynamics model (HIV-D). For treatment regimens of secondary interest (orange diagram), a discrete event simulation model (DES) determines the plasma HIV-1 RNA level over time. Using the dynamics of the HIV-D model, CD4+ T cell counts are updated accordingly.</p

PK/PD modeling parameters for RPV, FTC and TDF.

<p>PK/PD modeling parameters for RPV, FTC and TDF.</p

Week 96 snapshot analysis for the RPV/FTC/TDF arm of the STaR study [33] and the simulated population in the EPICE-HIV model: detailed results.

<p>Week 96 snapshot analysis for the RPV/FTC/TDF arm of the STaR study [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0149007#pone.0149007.ref033" target="_blank">33</a>] and the simulated population in the EPICE-HIV model: detailed results.</p

Drug inhibition as a function of drug concentration and time.

<p>Left: Logarithmic measure of inhibition (F_wt) as a function of drug concentration (percentage of maximum concentration, log₁₀-scale), for drugs RPV, FTC and TDF alone (green lines) and predictions of the combined effect of RPV+FTC+TDF by the Bliss, Loewe and DI model. For the DI model, a hypothetical index 0.5 of degree of independence was assumed. The vertical grey dashed line represents the inhibition potential at maximum concentration for all three drugs. Right: Logarithmic measure of inhibition (F_wt) as a function of days, based on the Bliss, Loewe and DI model combined effect predictions. Simulations are representative of triple combination ART initiation with RPV+FTC+TDF, for a patient 100% adherent to therapy, except for a treatment interruption of 15 days, starting at day 50. After 15 days, a level of only 50% inhibition, represented by the horizontal grey dashed line, starts being approached.</p

Evolution of plasma HIV-1 RNA levels and CD4+ T cell counts in an untreated subject.

<p>Left: Plasma HIV-1 RNA levels as a function of time, simulated by the HIV-D model accounting for disease progression. Right: corresponding CD4⁺ T cell counts. The model simulates the 3 typical stages of HIV-1 infection: acute infection, clinical latency and AIDS phase. Dashed grey lines represent the HIV-D model without disease progression, the stable-state solution of which is exploited to introduce between-patient variability in plasma cell concentrations.</p