Development, calibration and performance of an HIV transmission model incorporating natural history and behavioral patterns: application in South Africa.

Understanding HIV transmission dynamics is critical to estimating the potential population-wide impact of HIV prevention and treatment interventions. We developed an individual-based simulation model of the heterosexual HIV epidemic in South Africa and linked it to the previously published Cost-Effectiveness of Preventing AIDS Complications (CEPAC) International Model, which simulates the natural history and treatment of HIV. In this new model, the CEPAC Dynamic Model (CDM), the probability of HIV transmission per sexual encounter between short-term, long-term and commercial sex worker partners depends upon the HIV RNA and disease stage of the infected partner, condom use, and the circumcision status of the uninfected male partner. We included behavioral, demographic and biological values in the CDM and calibrated to HIV prevalence in South Africa pre-antiretroviral therapy. Using a multi-step fitting procedure based on Bayesian melding methodology, we performed 264,225 simulations of the HIV epidemic in South Africa and identified 3,750 parameter sets that created an epidemic and had behavioral characteristics representative of a South African population pre-ART. Of these parameter sets, 564 contributed 90% of the likelihood weight to the fit, and closely reproduced the UNAIDS HIV prevalence curve in South Africa from 1990-2002. The calibration was sensitive to changes in the rate of formation of short-duration partnerships and to the partnership acquisition rate among high-risk individuals, both of which impacted concurrency. Runs that closely fit to historical HIV prevalence reflect diverse ranges for individual parameter values and predict a wide range of possible steady-state prevalence in the absence of interventions, illustrating the value of the calibration procedure and utility of the model for evaluating interventions. This model, which includes detailed behavioral patterns and HIV natural history, closely fits HIV prevalence estimates.

Test and treat DC: forecasting the impact of a comprehensive HIV strategy in Washington DC.

BACKGROUND: The United States and international agencies have signaled their commitment to containing the human immunodeficiency virus (HIV) epidemic via early case identification and linkage to antiretroviral therapy (ART) immediately at diagnosis. We forecast outcomes of this approach if implemented in Washington DC. METHODS: Using a mathematical model of HIV case detection and treatment, we evaluated combinations of HIV screening and ART initiation strategies. We define current practice as no regular screening program and ART at CD4 counts < or = 350 cells/microL, and we define test and treat as annual screening and administration of ART at diagnosis. Outcomes include life expectancy of HIV-infected persons and changes in the population time with transmissible HIV RNA levels. Data, largely from Washington DC, include undiagnosed HIV prevalence of 0.6%, annual incidence of 0.13%, 31% rate of test offer, 60% rate of acceptance, and 50% linkage to care. Input parameters, including optimized ART efficacy, are varied in sensitivity analyses. RESULTS: Projected life expectancies, from an initial mean age of 41 years, are 23.9, 25.0, and 25.6 years for current practice, test and treat, and test and treat with optimized ART, respectively. Compared with current practice, test and treat leads to a 14.7% reduction in time spent with transmissible HIV RNA level in the next 5 years; test and treat with optimized ART results in a 27.3% reduction. CONCLUSIONS: An expanded HIV test and treat program in Washington DC will increase life expectancy of HIV-infected patients but will have a modest impact on HIV transmission over the next 5 years and is unlikely to halt the HIV epidemic.