University of California San Francisco
The WHO Trachoma control program aims to eliminate blinding trachoma by 2020, but to succeed, health workers, policy makers, and other contributing organizations cannot afford to waste time and effort. Some districts have yet to receive a single mass azithromycin treatment, while others are still being treated despite apparent success. Infectious disease forecasting, while in its infancy, has begun to achieve success in influenza and other diseases. The trachoma elimination program will benefit from disease forecasting, because it will help identify places where more effort is needed. We propose to use mathematical models, statistical forecasts, and expert opinion to predict trachoma epidemics, and prospectively test these forecasts against data as it becomes available. More specifically, trachoma elimination programs rely on clinical signs, and not laboratory data. We propose to (a) test whether models fit to clinical trial data forecast significantly better than expert opinion or statistical extrapolation in the short term, (b) test whether models fit to program data forecast significantly better than expert opinion or statistical extrapolation in the longer term, and finally (c) use the most successful models with district-leve prevalence data from the International Trachoma Initiative to help guide global district-level blinding trachoma elimination planning decisions. Such decisions include whether organizations should treat for longer or more frequently, as well as whether some districts will succeed even if we reduce effort. These efforts will not only help to guide the WHO trachoma control program's efforts to control trachoma by 2020 and enhance surveillance efforts after control has been achieved. They will also provide crucial tests of epidemic forecasting methods. Significant project team efforts will be dedicated towards ongoing dissemination and mapping of project-developed forecasts and models to help better inform ITI and other stakeholders throughout the project, including customized forecasts if requested by specific regions, based on their actual available resources.