Modelling sexually transmitted infections: less is usually more for informing public health policy

National Centre in HIV Epidemiology and Clinical Research, The University of New South Wales, 316 Victoria Street, Darlinghurst, Sydney, NSW 2010, Australia.
Transactions of the Royal Society of Tropical Medicine and Hygiene (Impact Factor: 1.84). 04/2008; 102(3):207-8. DOI: 10.1016/j.trstmh.2007.08.009
Source: PubMed


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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    • "The latter approach is particularly useful for low prevalence infections where there is a possibility of extinction and/or where it is necessary to capture events that occur at the level of the individual (e.g., tracing and treating sexual partners of infected individuals). However, for their computational efficiency, analytical tractability and ability to provide mechanistic insights to epidemic dynamics, deterministic ordinary differential equation (ODE) models are often preferred, particularly for endemic infections such as HPV [47]. We have previously developed a deterministic single-type transmission model for HPV-16 [45] and more recently multi-type models for HPV types 6, 11, 16 and 18 in order to evaluate the potential impact of vaccination. "
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    ABSTRACT: A Bayesian statistical model and estimation methodology based on forward projection adaptive Markov chain Monte Carlo is developed in order to perform the calibration of a high-dimensional nonlinear system of ordinary differential equations representing an epidemic model for human papillomavirus types 6 and 11 (HPV-6, HPV-11). The model is compartmental and involves stratification by age, gender and sexual-activity group. Developing this model and a means to calibrate it efficiently is relevant because HPV is a very multi-typed and common sexually transmitted infection with more than 100 types currently known. The two types studied in this paper, types 6 and 11, are causing about 90% of anogenital warts. We extend the development of a sexual mixing matrix on the basis of a formulation first suggested by Garnett and Anderson, frequently used to model sexually transmitted infections. In particular, we consider a stochastic mixing matrix framework that allows us to jointly estimate unknown attributes and parameters of the mixing matrix along with the parameters involved in the calibration of the HPV epidemic model. This matrix describes the sexual interactions between members of the population under study and relies on several quantities that are a priori unknown. The Bayesian model developed allows one to estimate jointly the HPV-6 and HPV-11 epidemic model parameters as well as unknown sexual mixing matrix parameters related to assortativity. Finally, we explore the ability of an extension to the class of adaptive Markov chain Monte Carlo algorithms to incorporate a forward projection strategy for the ordinary differential equation state trajectories. Efficient exploration of the Bayesian posterior distribution developed for the ordinary differential equation parameters provides a challenge for any Markov chain sampling methodology, hence the interest in adaptive Markov chain methods. We conclude with simulation studies on synthetic and recent actual data. Copyright
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    09/2010; 2(3):123-31. DOI:10.1016/j.epidem.2010.04.002
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