Average number of main partners in the past year among those with ≥1 main partner (reported by sexually active men and women). Proportion reporting >1 non-main partner in the past year (denominator = sexually active men, women). Proportion of those with ≤1 non-main partner reporting ≥1 main partner in the past year (denominator = sexually active men, women), stratified by age group (>24 and ≤24 years old).
Proportion of those with >1 non-primary partner who report ≥1 primary partner in the last year (denominator = males, sexually active females), stratified by (>24 and ≤24 years). Median number of non-primary partners in the past year among those who reported >1 non-primary partner in the past year [excluding partners referred to as FSW] (reported by sexually active men and women who did not report paid sex in the past year). The ratio of the mean number of non-primary partners reported by the highest decile of those with >1 non-primary partner in the last year to the lowest 90% of those with >1 non-primary partner in the last year (sexually active men, women, excluding partners referred to as FSW and excluding female respondents who reported paid sex in the last year).
DHS: % of women aged 15-49 who have had sex with >1 non-primary partner in the past 12 months (denominator = sexually active in the past 1 year). DHS: % with >1 non-primary partner reporting exchange of money and/or gifts for sex (excluding sex with FSW) in past year. DHS: % of those with 0 nonprimary partners who reported ≥1 primary partner in the past year (denominator = sexually active males), ages 15–49.
DHS: % of those with 0 non-main partners reporting ≥ 1 main partner in the past year (denominator = sexually active women), ages 15-49.
Schematic ormer FSW, fo
Biological st V-attributable m
Individuals enter sex work (as FSWs or clients) from always low-risk groups and MP groups depending on the fraction likely to enter sex work and the inverse of duration in sex work. Duration of sex work or time spent in MP activity classes is assumed to be independent of age. Then, HIV infection is seeded with 1 infectious individual per activity class of the youngest age group (k=1) in the seed year.
Individuals are susceptible (S) at the onset of sexual activity and become infected with HIV (acute phase, I1) with a strength of infection (HIV incidence per S) depending on the type of partnership, rate of partner change and HIV prevalence of partners. However, patterns that included sex work (and very high levels of contact with relatively shorter durations of high-risk activity) suggest that substitution may be important for HIV persistence [19–22]. Where, Ω ′ is the relative share of the population in class j' that will enter sex work (FSW or client).
Ω ′ is generated by multiplying initial FSW or client population size by the fraction entering sex work from the MP class versus the always low activity class (Table 4.8). Here, one's own activity and age category are designated as j and k respectively, and members of the opposite sex are distinguished by a prime number (j' and k'). The pattern of contacts for each pt is defined through a matrix that determines age- and activity-group-specific rates of partnering with age- and activity-groups of the opposite sex.
We translated DHS estimates of the % of young women reporting at least one partner >10 years older into ∆ for 3,4,5. We then use estimates of the average number of HCV visits/client, the ratio of HCV visits between high- and low-volume HCVs, and the % of clients who visit HCVs multiple times to estimate the proportion of men who are clients. For casual sex, we use MP group size between men and women (based on data) and the average number of casual partners/year among men to determine the average number of casual partners/year among women's MP groups.
Where, determines whether the demand for sexual partnerships of men (0.5 1) or women (0 0.5 is the strongest determinant of the pattern of partnership formation). function, where the growth rate is varied between 0.1 and 0.5, and the time of maximum growth varies between the first third of the period to the last quarter of the period.If the sample level of condom use in Ycondom_year_2 was < Ycondom_year_1, then the lower of the two values was taken and held constant from Ycondom_year_1 onwards.
We used Latin hyper-cube sampling with a uniform parameter range distribution for each region due to the large number of parameters. Onoya D, Reddy P, Sifunda S, Lang D, Wingood GM, et al. relationships, sexually transmitted infection risk and condom use among young black women in peri-urban areas of the Western Cape Province of South Africa. Boily M, Bastos F, Desai K, Masse B (2004) Changes in HIV epidemic transmission dynamics after widespread use of antiretroviral therapy may explain the increase in sexually transmitted infections: Results from mathematical models.
Boily MC, Baggaley RF, Wang L, Masse B, White RG, et al. 2009) Heterosexual risk of HIV-1 infection by sexual act: systematic review and meta-analysis of observational studies.
