NSF Convergence Accelerator - Track D: Data-Driven Disease Control and Prevention in Veterinary Health

NSF 融合加速器 - 轨道 D：兽医健康中数据驱动的疾病控制和预防

• 批准号: 2040680
• 负责人: Beatriz Martinez Lopez
• 金额: $ 94.49万
• 依托单位: University of California-Davis
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-15 至 2022-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2040680&HistoricalAwards=false
• 关键词: NSF; Convergence; Accelerator; Track; Data

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 biosecuritystrategies 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.

NSF融合加速器支持以团队为基础的多学科努力，这些努力应对国家重要性的挑战，并将在不久的将来为社会带来价值的可交付成果。该融合加速器I期项目的更广泛影响和潜在的社会利益是改善动物的生产和健康。具体而言，该项目旨在解决良好的猪健康的维护，以此作为在全球高产猪肉市场中提高生产力和效率的高效手段。美国是世界第二大猪肉生产商，也是世界猪肉市场的主要参与者，排名第二，是进口商和出口国（2019年猪肉的出口价值为70亿美元）。从经济角度来看，这个行业的可持续性既是食品来源也很重要。可持续性需要基于可靠和当前数据的基于科学的分析工具和预测模型的明智和及时决定，以更好地管理猪健康。但是，实际上，尽管在初级生产的所有步骤中都收集了大量数据 - 从男生到断奶到屠宰，但这种数据通常是不完整的，不一致的，并且在不同的利益相关者中散布了 - 生产者，兽医诊断实验室和兽医诊所。与缺乏综合和高质量数据的缺乏交织在一起，还需要直接需要有效的人工智能（AI）算法，专门针对应对关键的兽医健康挑战。该项目的潜在影响远远超出了猪健康状况，可以用作美国和全球动物生产和健康的模型，不仅可以确保高质量的粮食供应，还可以提供经济优势。该团队将建立在其现有的疾病生物方面平台的基础上，以促进关键数据集的集成和共享，并开发针对动物健康的新的数据驱动模型。成果不仅将对游泳行业节省每年数百万美元的生产者产生直接的有益影响，而且还将显着改善动物健康/福利，食品安全，最终，最终是公共卫生。团队提出以数据为中心和以模型为中心的目标，具有以下目标：1）为有效的多层数据连接和整合量的A efectline开发，包括动物诊断，包括有效的多层数据连接和整合。病原体基因组；动物遗传学，生产和贸易；农场管理实践，包括生物安全和治疗方案以及环境信息等； 2）先进生物信息学管道（即基于读取和组装的方法）和AI算法（即成本吸引的自适应抽样和可解释的机器学习模型）的详细，实施和验证，以解决游泳行业中的关键问题，尤其是抗生素抵抗（AMR）和游泳的Infections and swimming thectections and swimming thrections and swimming thrections and; 3）扩展和采用疾病生物阶层，以促进兽医，生产者以及其他利益相关者和公众的数据共享和AI用户友好的使用和可视化。这项工作将使我们能够应对关键挑战：早期发现传染病和及时爆发调查；更好地了解农场内和农场之间病原体的变异性和传播模式；识别为AMR做出贡献的主要驱动因素；在动物，农场和系统级别实施的监视，治疗，疫苗和生物安全性的成本效益。尽管这项工作着重于游泳行业及其两个最迫在眉睫的挑战，但拟议的数据管道，数据集成，生物信息学和AI算法开发可以应用于其他牲畜。这项工作有可能通过提供数据驱动的决策工具来改善动物健康和福利，并确保美国农业和食品系统的可持续性，从而推动精确流行病学的前沿。该奖项反映了NSF的法定任务，并通过使用该基金会的知识分子和更广泛的影响来评估NSF的法定任务，并被认为是珍贵的支持。