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

Abstract

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.

Figure 1

Prevalence curves for HIV from 1980 to 2005 in case no behavioural intervention is implemented. Median HIV prevalence (in %) of 100 simulations. The black dot represents the literature value from Ref.. Left panel: females; right panel: males. Models: Si_Ba: Simpact 1.0 basic model; Si_IO: Simpact 1.0 model with inflow and outflow; Si_VL: Simpact 1.0 model with VL-dependent HIV transmission hazard; Si_IO_VL: Simpact 1.0 model with inflow, outflow, VL-dependent HIV transmission hazard; St_Ba: StepSyn 1.0 basic model; St_IO: Stepsyn 1.0 model with inflow and outflow; St_RG: StepSyn 1.0 model with STI life history explicitly modeled; St_IO_RG: StepSyn 1.0 model with inflow, outflow and STI life history explicitly modeled. Figures were generated using R software version 3.6.0. (R Core Team (2019). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (URL https://www.R-project.org/).

Figure 2

Cumulative incidence for HIV in females from 1980 to 2005. Median cumulative incidence of 100 simulations and the range ([minimum,maximum]) (shaded area). Red: no intervention implemented; blue: intervention implemented in 1990; green: lower promiscuity from 1980 onwards. Interventions were implemented as follows. For Simpact Cyan 1.0, the weight for the number of relationships a person already has (α_(numrel,man) = α_(numrel,woman) in formula (1)) was changed from 0 to − 0.05. For StepSyn 1.0, the proportion of male pending shorts links that are fulfilled by females (pending.short.links.fulfilled) was changed from 1 to 0.7. Model abbreviations as in Fig. 1. Figures were generated using R software version 3.6.0. (R Core Team (2019). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (URL https://www.R-project.org/).

Figure 3

Cumulative incidence for HIV in males from 1980 to 2005. Median cumulative incidence of 100 simulations and the range ([minimum,maximum]) (shaded area). Red: no intervention implemented; blue: intervention implemented in 1990; green: lower promiscuity from 1980 onwards. Interventions were implemented as follows. For Simpact Cyan 1.0, the weight for the number of relationships a person already has (α_(numrel,man) = α_(numrel,woman) in formula (1)) was changed from 0 to − 0.05. For StepSyn 1.0, the proportion of male pending shorts links that are fulfilled by females (pending.short.links.fulfilled) was changed from 1 to 0.7. Model abbreviations as in Fig. 1. Figures were generated using R software version 3.6.0. (R Core Team (2019). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (URL https://www.R-project.org/).