Kristoffersen Anja B, Jimenez Daniel, Viljugrein Hildegunn, Grøntvedt Randi, Stien Audun, Jansen Peder A
Norwegian Veterinary Institute, PO Box 750, Sentrum, N-0106 Oslo, Norway; Department of Informatics, University of Oslo, PO Box 1080, Blindern, N-0316 Oslo, Norway.
Norwegian Veterinary Institute, PO Box 750, Sentrum, N-0106 Oslo, Norway.
Epidemics. 2014 Dec;9:31-9. doi: 10.1016/j.epidem.2014.09.007. Epub 2014 Sep 28.
Infection by parasitic sea lice is a substantial problem in industrial scale salmon farming. To control the problem, Norwegian salmonid farms are not permitted to exceed a threshold level of infection on their fish, and farms are required to monitor and report lice levels on a weekly basis to ensure compliance with the regulation. In the present study, we combine the monitoring data with a deterministic model for salmon lice population dynamics to estimate farm production of infectious lice stages. Furthermore, we use an empirical estimate of the relative risk of salmon lice transmission between farms, that depend on inter-farm distances, to estimate the external infection pressure at a farm site, i.e. the infection pressure from infective salmon lice of neighbouring farm origin. Finally, we test whether our estimates of infection pressure from neighbouring farms as well as internal within farm infection pressure, predicts subsequent development of infection in cohorts of farmed salmonids in their initial phase of marine production. We find that estimated external infection pressure is a main predictor of salmon lice population dynamics in newly stocked cohorts of salmonids. Our results emphasize the importance of keeping the production of infectious lice stages at low levels within local networks of salmon farms. Our model can easily be implemented for real time estimation of infection pressure at the national scale, utilizing the masses of data generated through the compulsory lice monitoring in salmon farms. The implementation of such a system should give the salmon industry greater predictability with respect to salmon lice infection levels, and aid the decision making process when the development of new farm sites are planned.
寄生海虱感染是工业化规模鲑鱼养殖中的一个重大问题。为控制该问题，挪威鲑鱼养殖场的鱼的感染水平不得超过阈值，并且养殖场需要每周监测并报告虱子数量，以确保符合规定。在本研究中，我们将监测数据与鲑鱼虱子种群动态的确定性模型相结合，以估计养殖场感染性虱子阶段的产量。此外，我们利用基于养殖场间距离的鲑鱼虱子传播相对风险的经验估计值，来估计养殖场的外部感染压力，即来自相邻养殖场感染性鲑鱼虱子的感染压力。最后，我们测试来自相邻养殖场的感染压力估计值以及养殖场内部的感染压力，是否能预测养殖鲑鱼在海洋生产初始阶段的感染后续发展情况。我们发现，估计的外部感染压力是新放养鲑鱼种群中鲑鱼虱子种群动态的主要预测指标。我们的结果强调了在鲑鱼养殖场的本地网络中将感染性虱子阶段的产量保持在低水平的重要性。利用鲑鱼养殖场强制虱子监测产生的大量数据，我们的模型可以很容易地用于在国家层面实时估计感染压力。实施这样一个系统应该能让鲑鱼产业在鲑鱼虱子感染水平方面具有更大的可预测性，并在规划新养殖场的发展时帮助决策过程。
