Abstract
Mathematical modelling has been widely applied to better understand the transmission, treatment and prevention of infectious diseases. The comparison of different models is crucial for providing robust evidence for policy-makers because differences in model properties can influence their prediction.
In this study, two individual-based models for simulating HIV epidemics and sexually transmitted infection (STI) co-factor effects, i.e. models developed with the Simpact Cyan 1.0 and StepSyn 1.0 frameworks, were compared. Simpact Cyan 1.0 uses a continuous-time implementation and is updated each time an event happens, while StepSyn 1.0 uses discrete daily or weekly time-steps and updates population, sexual links, and STI states at each time-step. Furthermore, there are differences in how stochastic processes are described, how individuals enter and leave the population, and in the formation and break-up of ties in the sexual network. We evaluated how these differences would affect survival and HIV prevalence curves during the course of a heterosexual HIV epidemic with Herpes simplex virus-2 as contributing STI, using as case study Yaoundé (Cameroon), 1989-1998. To allow such direct comparison, neither modelling framework used its full potential. For each model, 100 simulations were performed with parameters calibrated to HIV prevalence data from Yaoundé between 1989-1998. The median time profile for both models matched the data equally well, but a slight difference in variability among simulations could be observed. This can be explained by differences in model initialization and the generation of the sexual network. Moreover, our example shows that similar HIV prevalence curves can be simulated using a different combination of sexual network and HIV transmission parameters.
We conclude that it is important to carefully consider differences in model properties, here in particular their initialization and the generation of sexual networks. We strongly recommend using different models to assess the robustness of evidence provided by mathematical modelling.