Introduction:Pathogen genetic diversity is shaped by host-species population structure and contact patterns, and much can be learned about host population structure by analysis of random samples of pathogen genetic sequences. We demonstrate that by combining realistic infectious disease models with a population genetic model, it is possible to estimate transmission rates between groups with different risk behaviour. We apply the new methods to HIV sequence data from Abuja, Nigeria. The epidemiological role of small key populations in sub Sahara Africa is poorly understood, and targeted interventions focused on key populations promise to have increased efficacy at reducing population- level AIDS-related morbidity and mortality.
Methods:We analysed 151 HIV-1 sequences from a cohort of men who have sex with men (MSM) in Abuja, Nigeria between 2012 and January 2015, and these data were combined with 158 HIV-1 sequences from the general population of Abuja over the same period. We developed a series of compartmental models which account for population structure by sex, clinical stage of infection, and risk group. The models account for secular trends in risk behaviour and trends in diagnosis and treatment. This was combined with a structured coalescent genealogical model which was fitted to time- scaled HIV phylogenies. Models were extended to predict effectiveness of interventions targeted at MSM.
Results:Incidence of infection in MSM is estimated 12.5% (95% CI: 9.0 to 17.6) in 2014, which is consistent with previous estimates based on longitudinal cohorts. We estimate the population attributable fraction (PAF) of transmissions from MSM, and find it is increasing in recent years. More infections are prevented per unit cost using targetted interventions.
Discussion:While recent incidence of infection has fallen in Nigeria, incidence and PAF has grown in MSM, highlighting the need to target high risk groups to achieve control of HIV even in generalised epidemics.
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