Chair of Statistics, School of Business and Economics, Humboldt Universität zu Berlin, Berlin, Germany.
Departament de Matemàtiques, Universitat Autònoma de Barcelona, Cerdanyola del Vallès, Barcelona, Spain.
BMC Vet Res. 2020 Apr 14;16(1):110. doi: 10.1186/s12917-020-02312-8.
The automated collection of non-specific data from livestock, combined with techniques for data mining and time series analyses, facilitates the development of animal health syndromic surveillance (AHSyS). An example of AHSyS approach relates to the monitoring of bovine fallen stock. In order to enhance part of the machinery of a complete syndromic surveillance system, the present work developed a novel approach for modelling in near real time multiple mortality patterns at different hierarchical administrative levels. To illustrate its functionality, this system was applied to mortality data in dairy cattle collected across two Spanish regions with distinct demographical, husbandry, and climate conditions.
The process analyzed the patterns of weekly counts of fallen dairy cattle at different hierarchical administrative levels across two regions between Jan-2006 and Dec-2013 and predicted their respective expected counts between Jan-2014 and Jun- 2015. By comparing predicted to observed data, those counts of fallen dairy cattle that exceeded the upper limits of a conventional 95% predicted interval were identified as mortality peaks. This work proposes a dynamic system that combines hierarchical time series and autoregressive integrated moving average models (ARIMA). These ARIMA models also include trend and seasonality for describing profiles of weekly mortality and detecting aberrations at the region, province, and county levels (spatial aggregations). Software that fitted the model parameters was built using the R statistical packages.
The work builds a novel tool to monitor fallen stock data for different geographical aggregations and can serve as a means of generating early warning signals of a health problem. This approach can be adapted to other types of animal health data that share similar hierarchical structures.
从牲畜中自动收集非特定数据，结合数据挖掘和时间序列分析技术，有助于开发动物健康综合征监测（AHSyS）。AHSyS 方法的一个示例涉及监测牛群死亡情况。为了增强完整综合征监测系统的一部分机制，本工作开发了一种新方法，用于在不同层次的行政级别上实时模拟多种死亡率模式。为了说明其功能，该系统应用于在具有不同人口统计学、养殖和气候条件的两个西班牙地区收集的奶牛死亡率数据。
该过程分析了两个地区在 2006 年 1 月至 2013 年 12 月期间不同层次行政级别每周奶牛死亡数量的模式，并预测了 2014 年 1 月至 2015 年 6 月期间各自的预期数量。通过将预测数据与观测数据进行比较，确定超过传统 95%预测区间上限的奶牛死亡数量为死亡高峰。这项工作提出了一种动态系统，该系统结合了层次时间序列和自回归综合移动平均模型（ARIMA）。这些 ARIMA 模型还包括趋势和季节性，用于描述每周死亡率的分布，并在地区、省和县级（空间聚集）检测异常情况。用于拟合模型参数的软件是使用 R 统计软件包构建的。
这项工作构建了一种用于监测不同地理聚集的死亡牲畜数据的新工具，可以作为检测健康问题的预警信号的手段。这种方法可以适应具有类似层次结构的其他类型的动物健康数据。
