Excessive delays in treatment onset limit current tuberculosis (TB) control programs, presenting a major obstacle to the control of the TB epidemic. Increasing case detection, both quantitatively and temporally, is considered a priority, benefiting the patient (reduced morbidity and mortality) and the society (shortened period of infectiousness). These improvements in patient care have a common goal: to reduce transmission and eventually contain the spread of TB within the population. However, assessing reduced transmission of TB proofs to be difficult, as to date molecular tools have not been integrated in mathematical models or in field trials of public health interventions. Therefore, I aim to develop a model that incorporates bacterial genotyping to follow TB transmission within the human host population, hypothesizing that an effective Enhanced-Case-Finding (ECF) method can interrupt TB transmission. Integration of routine epidemiological and genotyping data with bioinformatics and mathematical modelling provides a novel and powerful approach to understand the key determinants of the TB epidemic, such as the Effective Reproductive Number, and predict the dynamics of TB transmission. I have the unique opportunity to position the present proposal as an added-value study that builds on 3-year Cluster Randomized Trial of ECF that I designed, which is about to be launched in The Gambia in 2012. By applying molecular genotyping methods to bacterial isolates collected from both the ECF intervention- and control arm, I will develop a method to measure and model the impact of ECF on the transmission of TB. This will be the first study of its kind in integrating molecular genotyping data in a TB transmission model as applied to a population level interventional study. The identification of significant transmission parameters will be important both for basic TB research and also for health experts that design and evaluate Public Health TB interventions in the future.
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