Division for Diagnostics & Scientific Advice-Epidemiology, National Veterinary Institute/Centre for Diagnostics-Technical University of Denmark, Lyngby, Denmark.
Department of Veterinary and Animal Sciences, University of Copenhagen, Frederiksberg C, Denmark.
PLoS One. 2019 Oct 9;14(10):e0223250. doi: 10.1371/journal.pone.0223250. eCollection 2019.
As our capacity to collect and store health data is increasing, a new challenge of transforming data into meaningful information for disease monitoring and surveillance has arisen. The aim of this study was to explore the potential of using livestock mortality and antibiotic consumption data as a proxy for detecting disease outbreaks at herd level. Changes in the monthly records of mortality and antibiotic consumption were monitored in Danish swine herds that became positive for porcine reproductive and respiratory syndrome (PRRS) and porcine pleuropneumonia. Laboratory serological results were used to identify herds that changed from a negative to a positive status for the diseases. A dynamic linear model with a linear growth component was used to model the data. Alarms about state changes were raised based on forecast errors, changes in the growth component, and the values of the retrospectively smoothed values of the growth component. In all cases, the alarms were defined based on credible intervals and assessed prior and after herds got a positive disease status. The number of herds with alarms based on mortality increased by 3% in the 3 months prior to laboratory confirmation of PRRS-positive herds (Se = 0.47). A 22% rise in the number of weaner herds with alarms based on the consumption of antibiotics for respiratory diseases was found 1 month prior to these herds becoming PRRS-positive (Se = 0.22). For porcine pleuropneumonia-positive herds, a 10% increase in antibiotic consumption for respiratory diseases in sow herds was seen 1 month prior to a positive result (Se = 0.5). Monitoring changes in mortality data and antibiotic consumption showed changes at herd level prior to and in the same month as confirmation from diagnostic tests. These results also show a potential value for using these data streams as part of surveillance strategies.
随着我们收集和存储健康数据的能力不断增强，如何将数据转化为有意义的信息，以用于疾病监测和防控，这一全新的挑战随之出现。本研究旨在探索利用牲畜死亡率和抗生素使用数据来监测畜群疾病爆发的潜力。我们对丹麦感染了猪繁殖与呼吸综合征（PRRS）和猪传染性胸膜肺炎的猪群进行了每月死亡率和抗生素使用数据的监测。利用实验室血清学结果来识别从疾病阴性状态转为阳性状态的畜群。我们采用带线性增长成分的动态线性模型对数据进行建模。根据预测误差、增长成分变化和增长成分回溯平滑值，发出关于状态变化的警报。在所有情况下，警报都是基于置信区间定义的，并在畜群出现阳性疾病状态之前和之后进行评估。基于死亡率的警报畜群数量在 PRRS 阳性畜群实验室确诊前的 3 个月内增加了 3%（Se = 0.47）。在这些畜群 PRRS 阳性前 1 个月，发现使用抗生素治疗呼吸道疾病的断奶猪群中，基于抗生素使用的警报畜群数量增加了 22%（Se = 0.22）。对于感染猪传染性胸膜肺炎的畜群，在出现阳性结果前 1 个月，母猪群用于治疗呼吸道疾病的抗生素使用量增加了 10%（Se = 0.5）。监测死亡率数据和抗生素使用量的变化显示，在确诊前和同一月份，畜群层面发生了变化。这些结果还表明，将这些数据流用作监测策略的一部分具有潜在价值。
