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

Funding Information: The research conducted by D.M.H. and N.H. in this study was funded by the Fonds Wetenschappelijk Onder-zoek—Vlaanderen (Research Foundation—Flanders; FWO, http://www.fwo.be/en/) (Grant Agreements G0E8416N and G0B2317N). The research done by J.D.S. and A.M.V. in this study has been supported in part by Grants G.0692.14 and G0B2317N, funded by the FWO, Belgium. P.J.K.L. was supported By a PhD Grant of the FWO (1S31916N), and is currently funded via a post-doctoral fellowship (1242021N) of the FWO. WD was supported by a postdoctoral fellowship from FWO (12L5816N). Research done by V.M. in this study has been completed as part of the ELTE Thematic Excellence Programme 2020 supported by the National Research, Development and Innovation Office (TKP2020-IKA-05). The authors gratefully acknowledge support from the FWO Scientific Research Community on Network Statistics for Sexually Transmitted Diseases Epidemiology. The computational resources and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by the Research Foundation—Flanders (FWO) and the Flemish Government—department EWI. Publisher Copyright: © 2021, The Author(s). 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. publishersversion published