Figure 1.
Sexual structure of the dynamic model, complex MOT model, and generic MOT model.
(A) In the dynamical model, the population is divided into four different activity classes based on the frequency of yearly partner change (FSWs/clients, two multiple partnership classes, and a low-activity class). Four partnership types are possible: commercial (regular or occasional), casual, or main. In the dynamical model, males and females who engage in higher-risk activity (commercial or casual sex) cease higher-risk activity and enter into the low-activity population reflecting a turn-over in each of the higher-risk activity groups (solid black lines). Multiple concurrent exposures are possible, and subpopulations are linked via bridging groups (individuals with multiple exposures). The partnerships are therefore shown with double-headed arrows to represent bridging between groups. (B) The complex Modes of Transmission model (cMOT) divides the population into the same activity classes as the dynamical model. The cMOT allows for multiple exposures to HIV (i.e. multiple types of partnerships). For visibility, only partnerships where infections are acquired by males are shown. Infections acquired by males and by females are counted separately, and partnerships are therefore shown with single-arrows to represent the lack of bridging between groups. Secondary infections and movement between risk-groups are not possible. (C) The generic Modes of Transmission model (gMOT) uses a simplified sexual structure, and only partnerships where infections are acquired by males are shown. In the gMOT, only one type of HIV exposure or partnership is possible, and subgroups are amalgamated in keeping with the generic MOT template [8], [9]. Infections acquired by males and by females are counted separately. As with the cMOT, single-headed arrows are used to represent different partnerships without bridging between groups. Hence, indirect transmission via bridging populations and secondary infections, and movement between risk-groups are not possible. MOT (modes of transmission); FSWs (female sex workers).
Table 1.
Differences between the models.
Table 2.
Epidemiologic context and parameters.
Table 3.
Prevention policies under finite resources, as formulated in 2012.
Figure 2.
Model-predicted distribution of new HIV infections over one year (MOT metric) in three epidemic types.
The benchmark Modes of Transmission (MOT) metric is obtained from the 4-stage dynamical model, and corresponding MOT metric obtained from the 1-stage dynamical model (1-stage DM), complex MOT (cMOT), and generic MOT (gMOT) models. The MOT metric reflects the fraction of new HIV infections acquired by different risk groups (colored bars) estimated for 2012 using data from the synthetic epidemics.*local epidemic drivers.
Figure 3.
Sensitivity of the MOT metric to biased input parameters (concentrated epidemic).
The range in the predicted fraction of new HIV infections acquired by the low-activity group (A), clients (B), and female sex workers (FSWs, C) are depicted for the five most influential parameters from the complex Modes of Transmission model (cMOT) using biased inputs. Also shown are the benchmark MOT metric and the unbiased cMOT metric. Across the parameter range examined here, the low-activity group incurred the largest burden of new infections, and the unbiased MOT metric did not identify the epidemic driver (no red regions). Pop. (population); Prev. (prevalence).
Figure 4.
The benchmark MOT metric over time in three epidemic types.
The benchmark Modes of Transmission (MOT) metric is obtained from the 4-stage dynamical model, and is shown for the years 1990 and 2012. The MOT metric reflects the fraction of new HIV infections acquired by different risk groups (colored bars) estimated for 2012 using data from the synthetic epidemics.*local epidemic drivers.
Figure 5.
MOT metrics by subgroups and their contribution to overall HIV transmission (concentrated epidemic).
The predicted fraction of new infections acquired by female sex workers (A, FSWs) and the low-activity group (B), as obtained from the complex Modes of Transmission model (cMOT acquired) and the generic Modes of Transmission model (gMOT acquired), and the benchmark MOT (acquired), are shown in grey. The fraction of HIV infections transmitted from FSWs and the low-activity group is shown in green (cMOT transmitted). The cumulative population attributable fraction (PAFt) over different time horizons measured from the year of the MOT (2012) for the epidemic driver (FSWs) and low-activity groups are shown in black.
Figure 6.
Impact of different prevention policies on the overall HIV incidence in three epidemic types.
A generic intervention that reduces HIV transmission by 80% per sex-act is used. Policy 1 (red) prioritizes the low-activity group based on the largest burden of new HIV infections estimated from the generic Modes of Transmission model (gMOT) in 2012. gMOT-guided Policy 1 redistributes finite resources from condom-use coverage in high-risk groups to a generic intervention focus on low-activity individuals. gMOT-guided Policy 2 (blue) prioritizes the low-activity group but resources are added to existing interventions (baseline condom use in high-risk partnerships is sustained). Policy 3 (green) is guided by an increasing long-term population attributable fraction over time t (PAFt), and therefore prioritizes epidemic drivers to receive the generic intervention. Policy 4 is not informed by an epidemic appraisal, and randomly allocates additional resources across subgroups. Each policy is implemented in 2012, is immediately scaled-up, and sustained over 30 years of follow-up. The person-years of the generic intervention are fixed throughout the follow-up period, and equivalent within each simulated synthetic epidemic type.
Table 4.
Key Findings.