Department of Disease Control and Epidemiology, National Veterinary Institute (SVA), Uppsala SE-751 89, Sweden; Department of Animal Production and Food Safety, Faculty of Veterinary Medicine, University of Lisbon, Pólo Universitário do Alto da Ajuda, Avenida da Universidade Técnica, 1300-477 Lisbon, Portugal; Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Grønnegårdsvej 2, 1870 Frederiksberg, Denmark.
Växa Sverige, Ulls väg 29a, Uppsala SE 756 51, Sweden; Farm and Animal Health, Kungsängens gård, Uppsala SE-753 23, Sweden.
Prev Vet Med. 2022 Aug;205:105659. doi: 10.1016/j.prevetmed.2022.105659. Epub 2022 Apr 27.
The use of syndromic surveillance (SyS) has grown in animal health since the 2010s, but the use of production data has been underexplored due to methodological and practical challenges. This paper aimed to tackle some of those challenges by developing a SyS system using production data routinely collected in pig breeding farms. Health-related indicators were created from the recorded data, and two different time-series types emerged: the weekly counts of events traditionally used in SyS; and continuous time-series, where every new event is a new observation, and grouping by time-unit is not applied. Exponentially Weighted Moving Average (EWMA) and Shewhart control charts were used for temporal aberration detection, using three detection limits to create a "severity" score. The system performance was evaluated using simulated outbreaks of porcine respiratory and reproduction syndrome (PRRS) as a disease introduction scenario. The system proved capable of providing early detection of unexpected trends, serving as a useful health and management decision support tool for farmers. Further research is needed to combine results of monitoring multiple parallel time-series into an overall assessment of the risk of reproduction failure.
自 2010 年代以来，综合征监测（SyS）在动物健康领域的应用日益增多，但由于方法和实际挑战，生产数据的应用仍未得到充分探索。本文旨在通过开发一种使用养猪场常规收集的生产数据的 SyS 系统来解决其中的一些挑战。从记录的数据中创建了与健康相关的指标，出现了两种不同类型的时间序列：传统上用于 SyS 的每周事件计数；以及连续时间序列，其中每个新事件都是一个新的观察值，并且不应用时间单位分组。指数加权移动平均（EWMA）和休哈特控制图用于时间异常检测，使用三个检测限来创建“严重程度”评分。该系统的性能使用猪呼吸道和繁殖综合征（PRRS）的模拟暴发作为疾病引入场景进行了评估。该系统能够提供对意外趋势的早期检测，为农民提供有用的健康和管理决策支持工具。需要进一步研究如何将监测多个平行时间序列的结果结合起来，以对繁殖失败的风险进行总体评估。
