The United States livestock industry has an enormous socio-economic impact contributing to annual sales of $180 billion and 550,000 direct jobs. Its sustainability and success rely on the maintenance of good livestock health, high productivity, and efficiency. This requires effective analytical methods, prediction models, and decision tools. While a vast amount of data has been collected in all production processes, the integration and usage of such data to better inform decisions in livestock health has remained circumstantial. It is usually restricted to simple descriptive statistics or molecular analyses for specific aspects of animal breeding and pathogen diagnostics. The goal of this project is to develop a new, multi-scale, approach to bridge the gap between the data availability and its effective usage. The project will focus on the swine industry and its most economically devastating disease, the Porcine Reproductive and Respiratory Syndrome (PRRS). This will not only have a direct beneficial impact in the swine industry but will contribute to the better manage other livestock health problems, saving producers and US livestock industry millions of dollars yearly. This is the first principled decision framework for the livestock industry that integrates multi-level data to predict disease dynamics, detect changes in farm status, and optimize the use of testing, treatment and vaccination strategies. As a consequence, it is expected to improve animal health and welfare and secure the sustainability of US agriculture and food systems by providing a data-driven decision framework and tools that push the frontier of precision epidemiology. The outcome of this project will be widely disseminated through our education and extension program (BIGDATA-4- HEALTH) and the integration of methods in the Disease BioPortal platform. PIs have well integrated the research and education programs and will continue to do so. The project will generate new curriculum for multiple classes in computer science, animal science and veterinary science and will involve undergraduate and graduate students.<br/><br/>The project proposes a principled data-driven decision framework for systematic PRRS prevention and control, based on the multi-level data sources collected during swine production, using novel data mining and machine learning techniques. The objectives are: 1) Early detection through efficient testing using a proactive and cost sensitive testing framework. 2) Systematic PRRS prevention and control by effective integration of testing, vaccination, and biosecurity implementation at a production system level. 3) Real- world experimentation and algorithm evaluation using both large-scale numerical evaluation with real traces and small-scale experimentations and real-time validation in five demonstration swine operations. 4) Education and extension program (BIGDATA-4-HEALTH) with training materials and technology transfer activities and the expansion of our user-friendly Disease BioPortal platform to facilitate the use of the developed methods to all industry stakeholders, researchers, and the general public. The intellectual merits of this proposal are multi-folds: the research team will develop novel mechanisms that advance the state-of-the-art in data-driven decision making algorithms. To reduce exploration cost, efficient situation-aware exploration techniques that addresses the fundamental exploration-exploitation tradeoff in multi-armed bandits and reinforcement learning will be developed, To handle missing data, novel compressive sensing algorithms designed to better manage accuracy disparity will be investigated. Finally, to balance the cost-accuracy tradeoff, efficient means to integrate simulation and experimentation will be explored, which only has been limited studied in the literature. Furthermore, the value of these tools for the early detection of diseases at farm and system level using historical data and prospective real world experimentation will be also evaluated and demonstrated. While focusing on swine production, this work provides the foundations and can be adapted to improve animal health of other livestock species and to advance in other disciplines facing the same data challenges.<br/><br/>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.
BIGDATA: IA: A multi-level approach for global optimization of the surveillance and control of infectious disease in the swine industry
Beatriz Martinez Lopez; Xin Liu
University of California - Davis