School of Veterinary Medicine, National University of La Pampa, General Pico, Argentina.
National Service of Health and Agro-Food Quality (SENASA), Centro Regional La Pampa-San Luis, Corrientes, Santa Rosa, Argentina.
PLoS One. 2018 Aug 6;13(8):e0201739. doi: 10.1371/journal.pone.0201739. eCollection 2018.
The venereal diseases bovine trichomoniasis (BT) and bovine genital campylobacteriosis (BGC) cause economic losses in endemic areas like La Pampa province in Argentina where beef cattle are usually extensively managed. This study used data compiled between 2007 and 2014 by a Provincial Program for the Control and Eradication of venereal diseases in order to develop and analyze retrospective models of time series for BT and BGC. Seasonality and long-term trend were explored with decomposition and simple regression methods. Autoregressive Integrated Moving Average models (ARIMA) were used to fit univariate models for the prevalence and persistence of BT and BGC. Autoregressive Integrated Moving Average with Explanatory Variable models (ARIMAX) were used to analyze the association between different time series, replacement entries and herd samplings. The prevalence and persistence of BT and BGC have decreased from 2007 to 2014. All the BT and BGC time series are seasonal and their long-term trend is decreasing. Seasonality of BT and BGC is similar, with higher rates of detection in autumn-winter than is spring-summer. Prevalence and persistence time series are correlated, indicating their changes are synchronic and follow a similar time pattern. Prevalence of BT and BGC showed the best fitting with the ARIMA (0,0,1)(0,1,1)12 model. While for persistence of BT and BGC, the best adjustment was with the same model with no seasonal difference where the current number of cases depends on the moving averages of the month and the previous season. Including covariates improve the fitting of univariate models, in addition, estimations using ARIMAX models are more precise than using ARIMA models. The time distribution of the samplings could be increasing the false negative ratio. According to the obtained results, the ARIMA and ARIMAX models can be considered an option to predict the BT and BGC prevalence and persistence in La Pampa (Argentina).
牛传染性滴虫病（BT）和牛生殖道弯曲杆菌病（BGC）是在阿根廷拉潘帕省等流行地区造成经济损失的性病，这些地区的肉牛通常采用大群放牧的方式管理。本研究使用了 2007 年至 2014 年期间由省级性病控制和根除计划汇编的数据，旨在为 BT 和 BGC 开发和分析时间序列回顾性模型。采用分解法和简单回归法探索季节性和长期趋势。采用自回归综合移动平均模型（ARIMA）拟合 BT 和 BGC 流行率和持续时间的单变量模型。采用带解释变量的自回归综合移动平均模型（ARIMAX）分析不同时间序列、替换项和畜群抽样之间的相关性。从 2007 年到 2014 年，BT 和 BGC 的流行率和持续时间呈下降趋势。所有 BT 和 BGC 时间序列均具有季节性，且长期趋势呈下降趋势。BT 和 BGC 的季节性相似，秋冬季的检出率高于春夏季。流行率和持续时间时间序列呈正相关，表明它们的变化是同步的，遵循相似的时间模式。BT 和 BGC 的流行率时间序列与 ARIMA（0,0,1）（0,1,1）12 模型拟合最好。而对于 BT 和 BGC 的持续时间，最佳拟合模型为相同模型，且无季节性差异，即当前病例数取决于当月和上一季度的移动平均值。包含协变量可改善单变量模型的拟合度，此外，使用 ARIMAX 模型进行估计比使用 ARIMA 模型更精确。采样的时间分布可能会增加假阴性的比例。根据所得结果，ARIMA 和 ARIMAX 模型可作为预测拉潘帕省（阿根廷）BT 和 BGC 流行率和持续时间的一种选择。
