Division for Diagnostics and Scientific Advice - Epidemiology, National Veterinary Institute/Centre for Diagnostics - Technical University of Denmark, Kongens Lyngby, Denmark.
Department of Veterinary and Animal Sciences, University of Copenhagen, Frederiksberg C, Denmark.
BMC Vet Res. 2019 Jul 8;15(1):231. doi: 10.1186/s12917-019-1981-y.
Monitoring systems are essential to detect if the number of cases of a specific disease is rising. Data collected as part of voluntary disease monitoring programs is particularly useful to evaluate if control and eradication programs achieve the target. These data are characterized by random noise which makes harder to interpret temporal changes in the data. Monitoring trends in the data is a possible approach to overcome this issue. The objective of this study was to assess the performance of three time-series models that allows monitoring trends in data in terms of its adaptability when used to monitor changes in disease sero-prevalence at a national scale based on data collected as part of voluntary monitoring programs. We compared two Bayesian forecasting methods and an Exponential smoothing method, specifically a Dynamic Linear Model, a Dynamic Generalized Linear Model and a Holt's linear trend method, respectively. These three different types of time series models were applied to data on weekly sero-prevalence of Porcine Reproductive and Respiratory Syndrome (PRRS) in Danish swine herds.
Comparing the linear cross-dependence between the filtered values obtained from the three models and the raw data, we observed that the Holt's linear trend method shows negative linear dependence for roughly half of the time for breeding/nucleus and multiplier herds, having values close to zero for most of the period in finisher herds.
Bayesian forecasting methods adapt faster to changes in the data, compared to the deterministic Holt's linear trend method. The practical implication of this greater flexibility is that the Bayesian methods will provide more reliable values of changes in the data and have potential to be implemented as part of a surveillance system in Denmark.
监测系统对于检测特定疾病的病例数量是否上升至关重要。作为自愿疾病监测计划的一部分收集的数据对于评估控制和根除计划是否达到目标特别有用。这些数据的特点是随机噪声，这使得更难以解释数据中的时间变化。监测数据趋势是克服这个问题的一种可能方法。本研究的目的是评估三种时间序列模型的性能，这些模型能够监测数据趋势，就其在基于自愿监测计划收集的数据监测国家范围内疾病血清流行率变化时的适应性而言。我们比较了两种贝叶斯预测方法和一种指数平滑方法，分别是动态线性模型、动态广义线性模型和霍尔特线性趋势方法。这三种不同类型的时间序列模型应用于丹麦猪群每周猪繁殖与呼吸综合征（PRRS）血清流行率的数据。
比较三种模型得到的滤波值与原始数据之间的线性交叉相关性，我们观察到霍尔特线性趋势方法对于繁殖/核心和繁殖群的大约一半时间显示出负线性相关性，对于育肥群，在大部分时间内接近零。
与确定性的霍尔特线性趋势方法相比，贝叶斯预测方法对数据变化的适应速度更快。这种更大灵活性的实际意义是，贝叶斯方法将提供更可靠的数据变化值，并有可能作为丹麦监测系统的一部分实施。
