Centre for Veterinary Epidemiological Research, Department of Health Management, Atlantic Veterinary College, University of Prince Edward Island, Charlottetown, PE, C1A 4P3, Canada.
Centre for Veterinary Epidemiological Research, Department of Health Management, Atlantic Veterinary College, University of Prince Edward Island, Charlottetown, PE, C1A 4P3, Canada.
Epidemics. 2018 Sep;24:76-87. doi: 10.1016/j.epidem.2018.04.002. Epub 2018 Apr 11.
Sea lice are marine parasites affecting salmon farms, and are considered one of the most costly pests of the salmon aquaculture industry. Infestations of sea lice on farms significantly increase opportunities for the parasite to spread in the surrounding ecosystem, making control of this pest a challenging issue for salmon producers. The complexity of controlling sea lice on salmon farms requires frequent monitoring of the abundance of different sea lice stages over time. Industry-based data sets of counts of lice are amenable to multivariate time-series data analyses. In this study, two sets of multivariate autoregressive state-space models were applied to Chilean sea lice data from six Atlantic salmon production cycles on five isolated farms (at least 20 km seaway distance away from other known active farms), to evaluate the utility of these models for predicting sea lice abundance over time on farms. The models were constructed with different parameter configurations, and the analysis demonstrated large heterogeneity between production cycles for the autoregressive parameter, the effects of chemotherapeutant bath treatments, and the process-error variance. A model allowing for different parameters across production cycles had the best fit and the smallest overall prediction errors. However, pooling information across cycles for the drift and observation error parameters did not substantially affect model performance, thus reducing the number of necessary parameters in the model. Bath treatments had strong but variable effects for reducing sea lice burdens, and these effects were stronger for adult lice than juvenile lice. Our multivariate state-space models were able to handle different sea lice stages and provide predictions for sea lice abundance with reasonable accuracy up to five weeks out.
海虱是一种寄生在三文鱼养殖场的海洋寄生虫，被认为是三文鱼养殖业最昂贵的害虫之一。养殖场的海虱大量繁殖，大大增加了寄生虫在周围生态系统中传播的机会，这使得控制这种寄生虫成为三文鱼养殖户面临的一个挑战。控制三文鱼养殖场的海虱需要频繁监测不同阶段海虱数量的随时间变化。基于行业的海虱计数数据集适合进行多元时间序列数据分析。在这项研究中，我们应用了两套多元自回归状态空间模型来分析智利海虱数据，这些数据来自五个独立养殖场的六个大西洋三文鱼生产周期（与其他已知的活跃养殖场的距离至少为 20 公里），以评估这些模型用于预测养殖场海虱数量随时间变化的能力。这些模型具有不同的参数配置，分析表明自回归参数、化学处理浴处理和过程误差方差在生产周期之间存在很大的异质性。允许在生产周期之间使用不同参数的模型具有最佳拟合度和最小的总体预测误差。然而，在漂移和观测误差参数方面跨周期汇总信息并没有实质性地影响模型性能，从而减少了模型中所需参数的数量。处理浴对减少海虱负担有很强的但可变的影响，而且对成年海虱的影响比对幼虱的影响更大。我们的多元状态空间模型能够处理不同的海虱阶段，并在合理的时间范围内（长达五周）提供海虱数量的预测，具有合理的准确性。
