Effectiveness of combination packages for HIV-1 prevention in sub-Saharan Africa depends on partnership network structure: a mathematical modelling study

Abstract

Objectives Combination packages for HIV prevention can leverage the effectiveness of biomedical and behavioural elements to lower disease incidence with realistic targets for individual and population risk reduction. We investigated how sexual network structures can maximise the effectiveness of a package targeting sexually active adults in sub-Saharan Africa (SSA) with intervention components for medical male circumcision (MMC) and sexual partnership concurrency (having >1 ongoing partner). Methods Network-based mathematical models of HIV type 1 (HIV-1) transmission dynamics among heterosexual couples were used to explore how changes to MMC alone and in combination with changes to concurrency impacted endemic HIV-1 prevalence and incidence. Starting from a base model parameterised from empirical data from West Africa, we simulated the prevalence of circumcision from 10% to 90% and concurrency was modelled at four discrete levels corresponding to values observed across SSA. Results MMC and concurrency could contribute to the empirical variation in HIV-1 disease prevalence across SSA. Small reductions in concurrency resulted in large declines in HIV-1 prevalence. Scaling up circumcision in low-concurrency settings yields a greater relative benefit, but the absolute number of infections averted depends on both the circumcision coverage and baseline incidence. Epidemic extinction with this package will require substantial scale-up of MMC in low-concurrency settings. Conclusions Dynamic sexual network structure should be considered in the design and targeting of MMC within combination HIV-1 prevention packages. Realistic levels of coverage for these packages within southern Africa could lead to a reduction of incidence to the low levels observed in western Africa, and possibly, epidemic extinction.

EpiModel: AnRPackage for Mathematical Modeling of Infectious Disease over Networks

“…Network models, in contrast, have been able to replicate generalized HIV epidemic dynamics when driven with observed behavioral data and realistic transmission parameters (Jenness et al 2016b). The partnership networks produced by these models emerge from an empirically informed set of micro-level behaviors, including (but not limited to) how people choose their partners (e.g., based on attributes like sex and age), how many partners persons have at any one time, and the distribution of partnership lengths and overlaps Jenness et al 2016a).…”

Geographic mobility and potential bridging for sexually transmitted infections in Agbogbloshie, Ghana

“…Understanding network structure is critical in developing and implementing prevention interventions for HIV (Jenness et al, 2016). Migrants and other mobile individuals historically have been shown to connect otherwise distinct networks, thereby influence ongoing HIV transmission (Coffee et al, 2007; Decosas, 1995; Martinez-Donate et al, 2015), including in Ghana (Agyei-Mensah, 2001; Anarfi, 1993; Decosas, 1995; Oppong, 1998).…”

EpiModel: An R Package for Mathematical Modeling of Infectious Disease over Networks

“…The package has been designed to support this kind of extension, informed, in part by our own research goals. This includes projects that focus on drivers of HIV infection in men who have sex with men (MSM; Goodreau et al 2012), network-related causes of racial disparities in HIV and STIs among heterosexuals in the United States (Morris et al 2009), the role of acute-stage infection and concurrency for HIV in Zimbabwe (Goodreau et al 2010), the combined impact of male circumcision and network structure on HIV in heterosexual couples in West Africa (Jenness et al 2016a), the prevention benefits of combination HIV prevention among MSM globally (Sullivan et al 2012), and the impact of new HIV prevention technologies among MSM in the United States (Jenness et al 2016b). Earlier projects relied on the statnet tools and prototyped code that led to the development of package EpiModel , while the more recent projects have been programmed directly in package EpiModel and led to the development of package EpiModelHIV , an extension package of modules designed specifically to support modeling HIV transmission (available at https://github.com/statnet/EpiModelHIV).…”

“…Note that these summary statistics may also be observed from a network census, along with a much wider range of additional statistics (e.g., triads and larger cycles), and package EpiModel can also use these if they are available. Examples of how egocentric network data are translated into network statistics then used for epidemic modeling are provided in detail in our applied modeling papers (Goodreau et al 2010, 2012; Jenness et al 2016a,b). …”

“…Network models, in contrast, have been able to replicate generalized HIV epidemic dynamics when driven with observed behavioral data and realistic transmission parameters (Jenness, Goodreau, Morris, and Cassels 2016a). The partnership networks produced by these models emerge from an empirically informed set of micro-level behaviors, including (but not limited to) how people choose their partners (e.g., based on attributes like sex and age), how many partners persons have at any one time, and the distribution of partnership lengths and overlaps (Goodreau et al 2012; Jenness et al 2016b).…”