SimpactCyan 1.0: An Open-source Simulator for Individual-Based Models in HIV Epidemiology with R and Python Interfaces

Liesenborgs, Jori; Hendrickx, Diana M.; Kuylen, Elise; Niyukuri, David; Hens, Niel; Delva, Wim

doi:10.1038/s41598-019-55689-4

Cited by 8 publications

(13 citation statements)

References 48 publications

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“…In this simulation study, we considered a population of men and women within a generalized HIV epidemic in a heterosexual network. The simulation was conducted using agent-based models (ABMs) with Simpact Cyan 1.0 simulation tool [18], which simulated dynamic sexual network, HIV transmission dynamic, and viral evolution across the transmission network. More explicitly, the dynamic of sexual network is simulated through establishment and dissolution of sexual partnerships.…”

Section: Methodsmentioning

confidence: 99%

Assessing the uncertainty around age-mixing patterns in HIV transmission inferred from phylogenetic trees

2021

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Understanding age-mixing patterns in Human Immunodeficiency Virus (HIV) transmission networks can enhance the design and implementation of HIV prevention strategies in sub-Saharan Africa. Due to ethical consideration, it is less likely possible to conduct a benchmark study to assess which sampling strategy, and sub-optimal sampling coverage which can yield best estimates for these patterns. We conducted a simulation study, using phylogenetic trees to infer estimates of age-mixing patterns in HIV transmission, through the computation of proportions of pairings between men and women, who were phylogenetically linked across different age groups (15–24 years, 25–39 years, and 40–49 years); and the means, and standard deviations of their age difference. We investigated also the uncertainty around these estimates as a function of the sampling coverage in four sampling strategies: when missing sequence data were missing completely at random (MCAR), and missing at random (MAR) with at most 30%—50%—70% of women in different age groups being in the sample. The results suggested that age-mixing patterns in HIV transmission can be unveiled from proportions of phylogenetic pairings between men and women across age groups; and the mean, and standard deviation of their age difference. A 55% sampling coverage was sufficient to provide the best values of estimates of age-mixing patterns in HIV transmission with MCAR scenario. But we should be cautious in interpreting proportions of men phylogenetically linked to women because they may be overestimated or underestimated, even at higher sampling coverage. The findings showed that, MCAR was the best sampling strategy. This means, it is advisable not to use sequence data collected in settings where we can find a systematic imbalance of age and gender to investigate age-mixing in HIV transmission. If not possible, ensure to take into consideration the imbalance in interpreting the results.

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Section: Methodsmentioning

confidence: 99%

Assessing the uncertainty around age-mixing patterns in HIV transmission inferred from phylogenetic trees

2021

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“…Simpact Cyan 1.0 ( http://www.simpact.org/ ) 15 is a freely available framework for developing IBMs to simulate HIV transmission, progression and treatment. Models developed with Simpact Cyan 1.0 are event-driven, which means that the models are not updated at fixed time intervals, but at every time an event happens, making Simpact Cyan 1.0 a continuous-time simulation modelling framework.…”

Section: Methodsmentioning

confidence: 99%

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

Hendrickx

Sousa

Libin

et al. 2021

Sci Rep

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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.

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“…Simpact Cyan 1.0 modeling framework Simpact Cyan 1.0 is a open-source framework for constructing individual-based models for simulating the transmission, diagnosis and treatment of HIV [21]. The program models each individual in a heterosexual or homosexual population, and the sexual relationships between individuals.…”

Section: Methodsmentioning

confidence: 99%

“…More detailed information on the Simpact Cyan 1.0 modeling framework is available in [21] and from http://www.simpact.org/.…”

Section: Methodsmentioning

confidence: 99%

Influence of sexual risk behaviour and STI co-infection dynamics on the evolution of HIV set point viral load in MSM

Hendrickx

Delva

Hens

2019

Preprint

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HIV set-point viral load (SPVL) is an important predictor of HIV progression and transmission. Although it has been reported that anti-retroviral therapy (ART) reduces viral load (VL), increased SPVL levels have been observed in MSM in the decade following the introduction of ART in the Netherlands. Several studies have been devoted to explain these counter-intuitive trends in SPVL. However, to our knowledge, none of these studies has investigated an explanation in which it arises as the result of a sexually transmitted infection (STI) co-factor in detail. In this study, we adapted an event-based, individual-based model to investigate how STI co-infection and sexual risk behaviour affect the evolution of HIV SPVL in MSM before and after the introduction of ART. The results suggest that both STI co-factors and sexual risk behaviour have an effect on SPVL. However, the observed trends in SPVL cannot be explained by sexual risk behaviour and STI co-factors only. We recommend to develop mathematical models including also factors related to viral evolution as reported earlier in the literature. However, this requires more complex models, and the collection of more data for parameter estimation than what is currently available.

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SimpactCyan 1.0: An Open-source Simulator for Individual-Based Models in HIV Epidemiology with R and Python Interfaces

Cited by 8 publications

References 48 publications

Assessing the uncertainty around age-mixing patterns in HIV transmission inferred from phylogenetic trees

Assessing the uncertainty around age-mixing patterns in HIV transmission inferred from phylogenetic trees

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

Influence of sexual risk behaviour and STI co-infection dynamics on the evolution of HIV set point viral load in MSM

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