Animal and Plant Health Agency (APHA) - Weybridge, Woodham Lane, New Haw, Addlestone, Surrey, KT15 3NB, United Kingdom.
Animal and Plant Health Agency (APHA) - Weybridge, Woodham Lane, New Haw, Addlestone, Surrey, KT15 3NB, United Kingdom.
Prev Vet Med. 2020 May;178:104984. doi: 10.1016/j.prevetmed.2020.104984. Epub 2020 Apr 6.
Determining the size, location and structure of a livestock population is an essential aspect of surveillance and research as it provides understanding of the representativeness and coverage of any project or scheme. It is an important input for a variety of epidemiological analyses, for example, allowing generation of more accurate sample size calculations for estimating prevalence or freedom from disease, cost-benefit analyses for control measures to reduce or eradicate livestock disease, or development of between-herd network models to estimate the impact of movement of animals between farms on the spread of livestock diseases. The work described here provides information on how British pig movement data was compared against other datasets related to the British pig population to define its appropriateness for defining pig holding demographics. The data were then used to identify the location of pig holdings and the estimated herd size (split into five categories). Two methods are described that were used to classify the holding type of the identified pig holdings. The first method was an epidemiological method that used expert opinion to determine a set of rules based on movement characteristics to classify each holding. The second method was a machine learning approach that used k means cluster analysis to automatically estimate the holding type based on a set of proxy indicators. Each method had a good accuracy rate, when compared to matched holdings present in data provided by the Annual June Agricultural Survey, but all misclassified some holdings. While both of the methods on their own provided a reasonable estimate, it was concluded that a consensus model, considering the results of both models and the Survey, provided the most accurate result. However, the machine learning approach was beneficial, as although some technical expertise was needed to set up the model, it was considerably faster to implement than the other method, as well as being quicker and easier to adapt and re-run with updated information.
确定牲畜种群的规模、位置和结构是监测和研究的一个基本方面，因为它提供了对任何项目或计划的代表性和覆盖范围的理解。它是各种流行病学分析的重要输入，例如，允许更准确地计算估计患病率或无病自由的样本大小，对减少或根除牲畜疾病的控制措施的成本效益分析，或开发畜群间网络模型，以估计动物在农场之间移动对牲畜疾病传播的影响。这里描述的工作提供了有关如何将英国猪移动数据与其他与英国猪群相关的数据集进行比较的信息，以确定其适合定义养猪场人口统计学的信息。然后，使用这些数据来识别养猪场的位置和估计的畜群规模（分为五类）。描述了两种用于识别养猪场的方法。第一种方法是一种流行病学方法，利用专家意见确定了一组基于移动特征的规则，用于对每个畜群进行分类。第二种方法是一种机器学习方法，使用 k 均值聚类分析根据一组代理指标自动估计畜群类型。与年度 6 月农业调查提供的数据中存在的匹配畜群相比，每种方法的准确率都很高，但都有一些畜群分类错误。虽然这两种方法本身都提供了合理的估计，但得出的结论是，考虑到两个模型和调查的结果的共识模型提供了最准确的结果。然而，机器学习方法是有益的，因为尽管设置模型需要一些技术专业知识，但它比其他方法实施速度快得多，并且更快、更容易适应和重新运行，同时更新信息。
