Abstract
Mathematical models have been used to investigate the dynamics of infectious disease transmission since Bernoulli's smallpox modelling in 1760. Their use has become widespread for exploring how epidemics can be prevented or contained. Here we discuss the importance of modelling the dynamics of sexually transmitted infections, the technology-driven dichotomy in methodology, and the need to 'keep it simple' to explore sensitivity, to link the models to reality and to provide understandable mechanistic explanations for real-world policy-makers. The aim of models, after all, is to influence or change public health policy by providing rational forecasting based on sound scientific principles.
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