Mathematical models of sexually transmitted disease (STD) aim to represent sexual contact networks, pathogen characteristics, and intervention activities, helping understand and quantify STD epidemics and their control. Models are, unfortunately, necessary because non-linear infectious disease dynamics combined with many interacting variables undermines intuitive understanding. It is hoped that rational decision makers will include a range of evidence, including the finding of models, in funding, planning, implementing, and evaluating STD interventions and programs. The level of detailed required for informative models that can lead to better decisions, and how models can best be included in the international, national, and local public health ecosystem are areas for attention. Questions to consider are: 1) The pros and cons of focusing resources on sub-groups within a population. This is influenced by the biology and behavior associated with the STD, along with structural context. 2) How to ensure successful implementation of interventions? Here simple cascades are being used to identify gaps in treatment and prevention. 3) What is the relative value of developing, introducing and scaling the use of specific testing, treatment and prevention products in STD programs? The epidemiological context and coverage of other interventions have profound implications here. 4) How do we design a surveillance system that provides timely and actionable data on STD epidemiology? Here again the epidemiological context matters. The history of modeling STDs provides many examples that address such questions, and models are currently influencing policy, particularly in HIV programs. However, the development and validation of models in STD epidemiology has been hampered by our inability to directly measure the sexual network via which STDs spread. Recent efforts to evaluate model performance, and advances in pathogen sequencing and phylogenetic analysis, provide avenues to improve the validity and utility of STD models as their use becomes more systematic.

Disclosure No significant relationships.