Additional file 1: of Infection prevention behaviour and infectious disease modelling: a review of the literature and recommendations for the future

This file contains the Medline search strategy, the extraction criteria used, and an included papers reference list. (DOCX 25 kb

Additional file 2: of Infection prevention behaviour and infectious disease modelling: a review of the literature and recommendations for the future

This excel spreadsheet contains the table detailing all extracted data summarised within the manuscript. (XLSX 26 kb

Projected impact of the intervention on HIV incidence in Arms A and B compared with Arm C for central and optimistic target scenarios (specific parameter values for each scenario defined in Table S10 in File S1).

<p>Projected impact of the intervention on HIV incidence in Arms A and B compared with Arm C for central and optimistic target scenarios (specific parameter values for each scenario defined in Table S10 in File S1).</p

Model structure for infected individuals.

<p><b>A.</b> Flow diagram of the model. The model describes progression through different stages of natural history and treatment. Arrows depict the different flow rates between compartments. Once infected, individuals enter an early/acute infection stage and then progress to death through 4 stages of infection shown in b. At any stage after acute infection, individuals can get HIV tested, following which a proportion of individuals will decide to seek care. Those individuals then enter the “treatment pending” stage, during which they visit the clinic and get assessed towards ART eligibility. Once assessed, all those eligible initiate treatment. Once on treatment, individuals go through a first phase during which viral load is not fully suppressed, before becoming successfully treated, that is, having a negligible viral load. Individuals on ART can drop out of or fail treatment; they then go back to the pre-test stages. <b>B.</b> Flow diagram of the four stages of infection following acute infection and leading to death. Those stages are defined by the level of CD4 in cells/mm3. Individuals can only move from a higher to a lower CD4 count. The rate of progressing through those stages is different for treated and untreated individuals (see File S1). <b>C.</b> Relative infectivity (on the log scale) of the different stages, compared to an undiagnosed individual with CD4≥350, not in acute infection. Individuals in acute infection and stage 4 (CD4<200) have an increased infectivity. Individuals on ART have a decreased infectivity.</p

Sensitivity analysis: range of parameter values considered and influence on the relative reduction in 3-year cumulative incidence in arms A and B compared to arm C in each country.

<p>Regression exploring the linear dependence of the relative reduction in 3-year cumulative incidence in arms A and B compared to arm C (on the linear-scale) on process parameters (see File S1 for detail).</p><p>%var: proportion of variance in the reduction in 3-year cumulative incidence explained by each predictor (see File S1 for detail).</p

Uncertainty on the trial outcome in Zambia (top panels) and South Africa (bottom panels).

<p>The red and blue histograms show the relative reduction in 3-year cumulative incidence in arms A and B respectively when parameters vary within ranges shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0084511#pone-0084511-t001" target="_blank">Table 1</a>. The left panels show results obtained when all parameters are varied, and the right panels when assuming no population-level behavioural changes associated with the intervention.</p

Model fit and projections under central target scenario for Zambia (top row) and South Africa (bottom row).

<p>Left panels show HIV prevalence and right panels show annualized HIV incidence over time. The red, blue and black lines correspond to arms A, B and C respectively. The grey dots and error bars are the UNAIDS HIV prevalence estimates <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0084511#pone.0084511-UNAIDSWHO1" target="_blank">[37]</a>.</p

Evidence for scaling up HIV treatment in sub-Saharan Africa: A call for incorporating health system constraints

<p>Evidence for scaling up HIV treatment in sub-Saharan Africa: A call for incorporating health system constraints</p