Predicting Resistance: Validating Mathematical Models
Location(s): Ethiopia; India
There is concern that antimicrobial resistance is spreading worldwide, seriously limiting our ability to successfully treat a wide variety of diseases. Mathematical models have contributed much to our understanding of drug resistance, but their results have not been widely accepted in clinical medicine, perhaps because of parameter uncertainty and lack of validation. Mass antibiotic distributions are currently being implemented around the world as part of the WHO's effort to eliminate trachoma, the leading cause of infectious blindness. These distributions offer an unprecedented opportunity to model the emergence of antimicrobial resistance and to test the model's predictions empirically. Programs know precisely who is treated, when they are treated, and what dose of antibiotic is taken. In this application, we develop mathematical models to predict how much resistance will emerge after multiple rounds of mass antibiotic administrations, and then test the models' predictions by collecting data from the field. We anticipate that this project will evaluate several principles of resistance modeling and evaluate the validity of mathematical transmission models in general. Specific Aims: 1. To determine the strength of the association between macrolide use and macrolide-resistant pneumococcus using data from existing epidemiological studies. 2. To estimate the level of resistance following mass azithromycin distributions for trachoma with mathematical models. 3. To test the validity of mathematical models by determining the prevalence of pneumococcal resistance empirically after multiple mass distributions.