Mathematical Modelling of Trachoma Transmission, Control and Elimination
The World Health Organization has targeted the elimination of blinding trachoma by the year 2020. To this end, the Global Elimination of Blinding Trachoma (GET, 2020) alliance relies on a four-pronged approach, known as the SAFE strategy (S for trichiasis surgery; A for antibiotic treatment; F for facial cleanliness and E for environmental improvement). Well-constructed and parameterized mathematical models provide useful tools that can be used in policy making and forecasting in order to help to control trachoma and understand the feasibility of this large-scale elimination effort. As we approach this goal, the need to understand the transmission dynamics of infection within areas of different endemicities, to optimize available resources and to identify which strategies are the most cost-effective becomes more pressing. In this study, we conducted a review of the modelling literature for trachoma and identified 23 articles that included a mechanistic or statistical model of the transmission, dynamics and/or control of (ocular) Chlamydia trachomatis. Insights into the dynamics of trachoma transmission have been generated through both deterministic and stochastic models. A large body of the modelling work conducted to date has shown that, to varying degrees of effectiveness, antibiotic administration can reduce or interrupt trachoma transmission. However, very little analysis has been conducted to consider the effect of nonpharmaceutical interventions (and particularly the F and E components of the SAFE strategy) in helping to reduce transmission. Furthermore, very few of the models identified in the literature review included a structure that permitted tracking of the prevalence of active disease (in the absence of active infection) and the subsequent progression to disease sequelae (the morbidity associated with trachoma and ultimately the target of GET 2020 goals). This represents a critical gap in the current trachoma modelling literature, which makes it difficult to reliably link infection and disease. In addition, it hinders the application of modelling to assist the public health community in understanding whether trachoma programmes are on track to reach the GET goals by 2020. Another gap identified in this review was that of the 23 articles examined, only one considered the cost-effectiveness of the interventions implemented. We conclude that although good progress has been made towards the development of modelling frameworks for trachoma transmission, key components of disease sequelae representation and economic evaluation of interventions are currently missing from the available literature. We recommend that rapid advances in these areas should be urgently made to ensure that mathematical models for trachoma transmission can robustly guide elimination efforts and quantify progress towards GET 2020.