The NSF Convergence Accelerator supports use-inspired, team-based, multidisciplinary efforts that address challenges of national importance and will produce deliverables of value to society in the near future. The broader impact and potential societal benefit of this Convergence Accelerator Phase I project is to improve animal production and health. Specifically, this project aims to address the maintenance of good swine health as a means of achieving high productivity and efficiency in the high production global pork market. The US is the world’s second-largest pork producer and a major player in the world pork market, ranking second as importer and exporter country (the export value for pork in 2019 was $7 billion). Thus, sustainability of this industry is important both as a food source as well as from an economic standpoint. Sustainability requires informed and timely decisions using scientific-based analytical tools and prediction models, based on reliable and current data to better manage swine health. However, in reality, while a vast amount of data has been collected in all steps of primary production -from farrowing and weaning to slaughter- such data is often incomplete, inconsistent and scattered at different stakeholders -producers, veterinary diagnostic labs, and veterinary clinics. Intertwined with this lack of integrated and high-quality data, there is also a dire need for effective artificial intelligence (AI) algorithms specifically designed to address key veterinary health challenges. The potential impact of this project extends far beyond swine health and could be used as a model across animal production and health in the US and globally not only assuring high quality food supply but also providing economic advantage. The team will build on their existing Disease BioPortal platform to facilitate the integration and sharing of key datasets and develop new data-driven models specifically adapted to animal health. Outcomes will not only have a direct beneficial impact in the swine industry saving producers millions of dollars yearly but also will significantly improve animal health/welfare, food safety and, ultimately, public health.
The team proposes both data-centric and model-centric approaches with the following objectives: 1) development of a pipeline for effective multi-level data connection and integration, including animal diagnostics; pathogen genomes; animal genetics, production and trade; farm management practices including biosecurity and treatment protocols and environmental information, among others; 2) elaboration, implementation and validation of advanced bioinformatic pipelines (i.e., read-based and assembly based methods) and AI algorithms (i.e., cost-aware adaptive sampling and explainable machine learning models) to solve key problems in the swine industry, in particular, antimicrobial resistance (AMR) and swine influenza infections and; 3) expansion and adoption of the Disease BioPortal platform to facilitate data sharing and AI user-friendly usage and visualization by veterinarians, producers as well as other stakeholders and the general public. This effort will enable us to address critical challenges: early detection of infectious diseases and timely outbreak investigations; better understanding of the variability and spread patterns of pathogens within and between farms; identification of the main drivers contributing to AMR; cost-effectiveness of the surveillance, treatment, vaccination, and biosecurity strategies implemented at animal-, farm- and system- level. While the work focuses on the swine industry and its two most imminent challenges, the proposed data pipeline, data integration, bioinformatics and, AI algorithm development can be applied to other livestock. Thus, this work has the potential to improve animal health and welfare and to secure the sustainability of US agriculture and food systems by providing data-driven decision tools that push the frontier of precision epidemiology.
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.
