BackgroundIn sub-Saharan Africa, where ~ 25 million individuals are infected with HIV and transmission is predominantly heterosexual, there is substantial geographic variation in the severity of epidemics. This variation has yet to be explained. Here, we propose that it is due to geographic variation in the size of the high-risk group (HRG): the group with a high number of sex partners. We test our hypothesis by conducting a geospatial analysis of data from Malawi, where ~ 13% of women and ~ 8% of men are infected with HIV.MethodsWe used georeferenced HIV testing and behavioral data from ~ 14,000 participants of a nationally representative population-level survey: the 2010 Malawi Demographic and Health Survey (MDHS). We constructed gender-stratified epidemic surface prevalence (ESP) maps by spatially smoothing and interpolating the HIV testing data. We used the behavioral data to construct gender-stratified risk maps that reveal geographic variation in the size of the HRG. We tested our hypothesis by fitting gender-stratified spatial error regression (SER) models to the MDHS data.ResultsThe ESP maps show considerable geographic variation in prevalence: 1–29% (women), 1–20% (men). Risk maps reveal substantial geographic variation in the size of the HRG: 0–40% (women), 16–58% (men). Prevalence and the size of the HRG are highest in urban centers. However, the majority of HIV-infected individuals (~75% of women, ~ 80% of men) live in rural areas, as does most of the HRG (~ 80% of women, ~ 85% of men). We identify a significant (P < 0.001) geospatial relationship linking the size of the HRG with prevalence: the greater the size, the higher the prevalence. SER models show HIV prevalence in women is expected to exceed the national average in districts where > 20% of women are in the HRG. Most importantly, the SER models show that geographic variation in the size of the HRG can explain a substantial proportion (73% for women, 67% for men) of the geographic variation in epidemic severity.ConclusionsTaken together, our results provide substantial support for our hypothesis. They provide a potential mechanistic explanation for the geographic variation in the severity of the HIV epidemic in Malawi and, potentially, in other countries in sub-Saharan Africa.

Summary Background Worldwide, ~35 million individuals are infected with HIV; ~25 million in sub-Saharan Africa (SSA). The WHO proposes using “treatment as prevention” (TasP) to eliminate HIV. Treatment suppresses viral load, decreasing the probability an individual transmits HIV. The elimination threshold is one new HIV infection per 1,000 individuals. Here, we test the hypothesis that TasP can substantially reduce epidemics and eliminate HIV. We estimate the impact of TasP, between 1996–2013, on the Danish HIV epidemic in Men-who-have-Sex-with-Men (MSM), an epidemic UNAIDS has identified as a priority for elimination. Methods We use a CD4-staged Bayesian back-calculation approach to estimate incidence, and the “hidden epidemic” (the number of HIV-infected undiagnosed MSM). We use data from an ongoing nationwide population-based study: the Danish HIV Cohort Study. Findings Incidence, and the hidden epidemic, decreased substantially after treatment was introduced in 1996. By 2013, incidence was close to the elimination threshold: 1·4 (median, 95% Bayesian Credible Interval (BCI): 0·4–2·1) new HIV infections per 1,000 MSM. There were only 617 (median, 95% BCI: 264–858) undiagnosed MSM. Decreasing incidence and increasing treatment coverage are highly correlated; a threshold effect is apparent. Interpretation Our study is the first to show that TasP can substantially reduce a country’s HIV epidemic, and bring it close to elimination. However, we have shown the effectiveness of TasP under optimal conditions: very high treatment coverage, and exceptionally high (98%) viral suppression rate. Unless these extremely challenging conditions can be met in SSA, the WHO’s global elimination strategy is unlikely to succeed. Funding NIAID/NIH

Multiple phylogenetic studies of HIV in sub-Saharan Africa (SSA) have shown that mobilitydriven transmission frequently occurs: many communities "export" and "import" strains. Mobilitydriven transmission can result in source-sink dynamics: one community can sustain a microepidemic in another community where transmission is too low to be self-sustaining. In epidemiology, the Basic Reproduction Number (ℛ 0 ) is used to specify the sustainability threshold. ℛ 0 represents the average number of secondary infections generated by one infected individual in a community where everyone is susceptible. If ℛ 0 is greater than one, transmission is high enough to sustain an epidemic; if ℛ 0 is less than one, it is not. Here, we discuss the conditions that are needed (in terms of ℛ 0 ) for source-sink transmission dynamics to occur in generalized HIV epidemics in SSA, present an example of where these conditions may occur (specifically, we use Namibia), and discuss the necessity of taking mobility-driven transmission into consideration when designing control strategies. Additionally, we discuss the need for a new generation of HIV transmission models that are more realistic than the current models. The new models should reflect, not only geographic variation in epidemiology and demography, but also the spatialtemporal complexity of population-level movement patterns.

Background UNAIDS has prioritised Malawi and 21 other countries in sub-Saharan Africa for fast-tracking the end of their HIV epidemics. UNAIDS' elimination strategy requires achieving a treatment coverage of 90% by 2030. However, many individuals in the prioritised countries have to travel long distances to access HIV treatment and few have access to motorised transportation. Using data-based geospatial modelling, we investigated whether these two factors are barriers to achieving HIV elimination in Malawi and assessed the effect of increasing bicycle availability on expanding treatment coverage. Methods We built a data-based geospatial model that we used to estimate the minimum travel time needed to access treatment, for every person living with HIV in Malawi. We constructed our model by combining a spatial map of health-care facilities, a map that showed the number of HIV-infected individuals per km², and an impedance map. We quantified impedance using data on road and river networks, land cover, and topography. We estimated travel times for the existing coverage of 70%, and the time that HIV-infected individuals would need to spend travelling in order to achieve a coverage of 90%, whether driving, bicycling, or walking. Findings We identified a quantitative relationship between the maximum achievable coverage of treatment and the minimum travel time to the nearest health-care facility. At 70% coverage, health-care facilities can be reached within approximately 45 min if driving, 65 min if bicycling, and 85 min if walking. Increasing coverage above 70% will become progressively more difficult. To reach 90% coverage, many HIV-infected individuals (who have yet to initiate treatment) will need to travel for almost twice as long as those already on treatment. Bicycling, rather than walking, in rural areas would substantially increase the maximum achievable coverage. Interpretation The long travel times needed to reach health-care facilities coupled with little motorised transportation in rural areas are substantial barriers to reaching 90% coverage in Malawi. Increased bicycle availability could help eliminate HIV.

We analyzed georeferenced data on mobility and HIV infection from the 2009 Demographic and Health Survey of Lesotho. We found ~50% of the population traveled in the preceding year. By constructing gender-specific mobility maps we discovered travel is highest in the urban areas bordering South Africa, and in the mountainous interior of the country. For both genders, increased mobility was associated with increased levels of “recent” sexual behavior. Notably, mobility was only associated with an increased risk of HIV infection for men who travelled frequently. We discuss the implications of our results for designing effective treatment programs and HIV interventions.