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.
Highlights
Mathematical modelling has been widely applied to better understand the transmission, treatment and prevention of infectious diseases
individual-based models (IBMs) have been recently applied to assess the influence of pre-exposure prophylaxis (PrEP) in M SM4, to evaluate the long-term effect of early antiretroviral therapy (ART) initiation[5] and to understand the factors underlying the emergence of human immunodeficiency virus (HIV) in h umans[6]
Eight individual-based models, generated with the simulators Simpact Cyan 1.0 and StepSyn 1.0 were compared in terms of their prediction of the impact of behavioural interventions on the course of the HIV epidemic in the heterosexual population in Yaoundé, Cameroon during the period 1980–2005
Summary
Mathematical modelling has been widely applied to better understand the transmission, treatment and prevention of infectious diseases. The role of mathematical models in understanding the dynamics of the human immunodeficiency virus (HIV) epidemic has been recently reviewed by Geffen and Welte[1] They present examples of HIV models that were used for estimating the size of the epidemic in specific subpopulations such as the Black population in South Africa and for estimating the impact of interventions such as the use of antiretrovirals and condoms to reduce HIV and AIDS. In contrast to compartmental models, IBMs allow to model heterogeneous interactions between individuals[7] (e.g. regarding (sexual) risk behaviour, age demography and individual response to treatment). This is an advantage when heterogeneity matters for the particular question/process that is studied.
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