This research will develop theory and methods for disease surveillance systems. Such systems are crucial for the control and elimination of animal and human infectious diseases. For newly emerging diseases, as well as those that have not been well studied, the development of evidence-based policy is hampered by a lack of locally-specific data. This lack is also faced by many low-and-middle income countries. The project will use outbreaks of foot-and-mouth disease (FMD) virus in Turkey as a case-study. Working with well-documented surveillance on historical outbreaks, this project will identify what data are necessary to estimate disease burden and outbreak risk, and will evaluate FMD mitigation strategies. The researchers will then work with veterinary training workshops in Kenya and Uganda to translate lessons learned from the Turkey case-study to the development of surveillance programs in East Africa. The resulting theory and methods will serve as a guide for developing surveillance systems for a wide variety of animal and human diseases in the US and elsewhere. The project also will provide an international research experience to US graduate students and post-doctoral scholars.
This project will develop theory and methods for the scale-up and adaptation of infectious disease surveillance systems in tandem with the development of dynamic disease transmission models to support disease control and elimination policies. While surveillance data will always advance scientific understanding, model-based prioritization of data collection and surveillance system design will lead to more efficient data collection to specifically support policy goals. This research will retrospectively analyze a time series of FMD virus outbreaks in livestock in Turkey between 2001-2012. This data set, which includes detailed records of the location, timing, size, and virus strain for each outbreak is uncommonly well-resolved and represents a gold standard of a nearly perfect surveillance system for a livestock disease. This project will first fit spatially-explicit stochastic models of farm-to-farm FMD transmission to the full data to derive the best-case understanding of dynamics, and predictability, of an FMD outbreak; this analysis will be the first country-scale model of endemic FMD transmission. Then, the researchers will strip away elements of the data to assess the minimally sufficient data necessary to estimate the burden of FMD disease, estimate the risk of FMD outbreaks, and evaluate FMD mitigation strategies, relative to the best-case analysis. The result will be novel methods for the integration of current model predictions into the optimal allocation of future surveillance to detect and/or mitigate outbreaks.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
