BIGDATA: IA: A multi-level approach for global optimization of the surveillance and control of infectious disease in the swine industry

大数据：IA：全球优化养猪业传染病监测和控制的多层次方法

基本信息

批准号：
1838207
负责人：
Beatriz Martinez Lopez
金额：
$ 160万
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：
2019
资助国家：
美国
起止时间：
2019-01-01 至 2023-12-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1838207&HistoricalAwards=false
关键词：
BIGDATA IA multi level approach

项目摘要

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.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.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.

美国牲畜行业具有巨大的社会经济影响，导致了1800亿美元和550,000个直接工作的年销售额。它的可持续性和成功取决于维持良好的牲畜健康，高生产力和效率。这需要有效的分析方法，预测模型和决策工具。尽管在所有生产过程中都收集了大量数据，但此类数据的整合和使用以更好地为牲畜健康的决策提供了信息。它通常仅限于动物育种和病原体诊断特定方面的简单描述性统计或分子分析。该项目的目的是开发一种新的多尺度方法，以弥合数据可用性与其有效用法之间的差距。该项目将集中于猪工业及其经济上最具破坏性的疾病，猪生殖和呼吸综合征（PRRS）。这不仅将对猪行业产生直接有益的影响，而且还将有助于更好地管理其他牲畜健康问题，每年节省生产者和美国牲畜行业数百万美元。这是牲畜行业的第一个原则决策框架，该框架整合了多层数据以预测疾病动态，检测农场状况的变化并优化了测试，治疗和疫苗接种策略的使用。结果，预计将通过提供数据驱动的决策框架和工具来推动精确流行病学的前沿，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。该项目的结果将通过我们的教育和扩展计划（BIGDATA-4-健康）和疾病生物阶层中的方法整合来广泛传播。 PI已经很好地整合了研究和教育计划，并将继续这样做。该项目将为计算机科学，动物科学和兽医科学领域的多个课程生成新的课程，并将涉及本科生和研究生。该项目根据猪使用新的数据挖掘和机器学习技术，提出了一个基于猪的多层次数据源，提出了针对系统预防和控制的原则性数据驱动的决策框架。目标是：1）使用主动和成本敏感的测试框架通过有效测试提前检测。 2）通过有效整合生产系统级别的测试，疫苗接种和生物安全实施，预防和控制系统。 3）在五个示范猪操作中，使用大规模数值评估和小规模实验和实时验证，使用大规模数字评估和实时验证。 4）通过培训材料和技术转移活动以及扩展我们用户友好的疾病生物型平台的教育和扩展计划（BigData-4-Health），以促进为所有行业利益相关者，研究人员和公众使用开发方法的使用。该提案的智力优点是多重的：研究团队将开发出新的机制，以推动数据驱动的决策制定算法的最新机制。为了降低勘探成本，将开发出多臂匪徒的基本勘探 - 探索探索技术，并将开发出多臂匪徒的基本探索折衷和增强学习，以处理缺失的数据，新型的压缩感应算法旨在研究旨在更好地管理准确性差异的新型压缩传感算法。最后，为了平衡成本准确性的权衡，将探索整合模拟和实验的有效手段，这在文献中仅受到限制。此外，还将评估和证明这些工具对使用历史数据和前瞻性现实世界实验的农场和系统级别早期检测的价值。在专注于猪生产上的同时，这项工作提供了基础，并且可以适应改善其他牲畜物种的动物健康，并在面临相同数据挑战的其他学科中进步。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响审查标准来通过评估来支持的。

项目成果

期刊论文数量（28）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

CTS2: Time Series Smoothing with Constrained Reinforcement Learning

DOI：
发表时间：
2021
期刊：
IEEE Robotics and Automation Letters
影响因子：
5.2
作者：
Yongshuai Liu;Xin Liu;I. Tsang;X. Liu;Liu Liu-Liu
通讯作者：
Yongshuai Liu;Xin Liu;I. Tsang;X. Liu;Liu Liu-Liu

A Target-Agnostic Attack on Deep Models: Exploiting Security Vulnerabilities of Transfer Learning

DOI：
发表时间：
2019-04
期刊：
ArXiv
影响因子：
0
作者：
Shahbaz Rezaei;Xin Liu
通讯作者：
Shahbaz Rezaei;Xin Liu

Impact of sensor data pre-processing strategies and selection of machine learning algorithm on the prediction of metritis events in dairy cattle

传感器数据预处理策略和机器学习算法选择对奶牛子宫炎事件预测的影响

DOI：
10.1016/j.prevetmed.2023.105903
发表时间：
2023
期刊：
Preventive Veterinary Medicine
影响因子：
2.6
作者：
Vidal, Gema;Sharpnack, James;Pinedo, Pablo;Tsai, I Ching;Lee, Amanda Renee;Martínez-López, Beatriz
通讯作者：
Martínez-López, Beatriz

Exploiting Unlabeled Data to Improve Detection of Visual Anomalies in Soft Fruits

利用未标记的数据改进软水果视觉异常的检测