PopART-IBM, a highly efficient stochastic individual-based simulation model of generalised HIV epidemics developed in the context of the HPTN 071 (PopART) trial

Mathematical models are powerful tools in HIV epidemiology, producing quantitative projections of key indicators such as HIV incidence and prevalence. In order to improve the accuracy of predictions, such models need to incorporate a number of behavioural and biological heterogeneities, especially those related to the sexual network within which HIV transmission occurs. An individual-based model, which explicitly models sexual partnerships, is thus often the most natural type of model to choose. In this paper we present PopART-IBM, a computationally efficient individual-based model capable of simulating 50 years of an HIV epidemic in a large, high-prevalence community in under a minute. We show how the model calibrates within a Bayesian inference framework to detailed age- and sex-stratified data from multiple sources on HIV prevalence, awareness of HIV status, ART status, and viral suppression for an HPTN 071 (PopART) study community in Zambia, and present future projections of HIV prevalence and incidence for this community in the absence of trial intervention.

Comparison of two simulators for individual based models in HIV epidemiology in a population with HSV 2 in Yaoundé (Cameroon)

Model comparisons have been widely used to guide intervention strategies to control infectious diseases. Agreement between different models is crucial for providing robust evidence for policy-makers because differences in model properties can influence their predictions. In this study, we compared models implemented by two individual-based model simulators for HIV epidemiology in a heterosexual population with Herpes simplex virus type-2 (HSV-2). For each model simulator, we constructed four models, starting from a simplified basic model and stepwise including more model complexity. For the resulting eight models, the predictions of the impact of behavioural interventions on the HIV epidemic in Yaoundé-Cameroon were compared. The results show that differences in model assumptions and model complexity can influence the size of the predicted impact of the intervention, as well as the predicted qualitative behaviour of the HIV epidemic after the intervention. These differences in predictions of an intervention were also observed for two models that agreed in their predictions of the HIV epidemic in the absence of that intervention. Without additional data, it is impossible to determine which of these two models is the most reliable. These findings highlight the importance of making more data available for the calibration and validation of epidemiological models.

Temporal Trends in the Epidemiology of HIV in Turkey

Objective: The aim of this study was to analyze the temporal trends of HIV epidemiology in Turkey from 2011 to 2016. Method: Thirty-four teams from 28 centers at 17 different cities participated in this retrospective study. Participating centers were asked to complete a structured form containing questions about epidemiologic, demographic and clinical characteristics of patients presented with new HIV diagnosis between 2011 and 2016. Demographic data from all centers (complete or partial) were included in the analyses. For the cascade of care analysis, 15 centers that provided full data from 2011 to 2016 were included. Overall and annual distributions of the data were calculated as percentages and the Chi square test was used to determine temporal changes. Results: A total of 2,953 patients between 2011 and 2016 were included. Overall male to female ratio was 5:1 with a significant increase in the number of male cases from 2011 to 2016 (p<0.001). The highest prevalence was among those aged 25-34 years followed by the 35-44 age bracket. The most common reason for HIV testing was illness (35%). While the frequency of sex among men who have sex with men increased from 16% to 30.6% (p<0.001) over the study period, heterosexual intercourse (53%) was found to be the most common transmission route. Overall, 29% of the cases presented with a CD4 count of >500 cells/mm3 while 46.7% presented with a CD4 T cell count of <350 cells/mm3. Among newly diagnosed cases, 79% were retained in care, and all such cases initiated ART with 73% achieving viral suppression after six months of antiretroviral therapy. Conclusion: The epidemiologic profile of HIV infected individuals is changing rapidly in Turkey with an increasing trend in the number of newly diagnosed people disclosing themselves as MSM. New diagnoses were mostly at a young age. The late diagnosis was found to be a challenging issue. Despite the unavailability of data for the first 90, Turkey is close to the last two steps of 90-90-90 targets.

PopART-IBM, a highly efficient stochastic individual-based simulation model of generalised HIV epidemics developed in the context of the HPTN 071 (PopART) trial

Mathematical models are powerful tools in HIV epidemiology, producing quantitative projections of key indicators such as HIV incidence and prevalence. In order to improve the accuracy of predictions, such models need to incorporate a number of behavioural and biological heterogeneities, especially those related to the sexual network within which HIV transmission occurs. An individual-based model, which explicitly models sexual partnerships, is thus often the most natural type of model to choose. In this paper we present PopART-IBM, a computationally efficient individual-based model capable of simulating 50 years of an HIV epidemic in a large, high-prevalence community in under a minute. We show how the model calibrates within a Bayesian inference framework to detailed age- and sex-stratified data from multiple sources on HIV prevalence, awareness of HIV status, ART status, and viral suppression for an HPTN 071 (PopART) study community in Zambia, and present future projections of HIV prevalence and incidence for this community in the absence of trial intervention.