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Model for ranking freshwater fish farms according to their risk of infection and illustration for viral haemorrhagic septicaemia

Oidtmann, Birgit C., Pearce, Fiona M., Thrush, Mark A., Peeler, Edmund J., Ceolin, Chiara, Stärk, Katharina D.C., Pozza, Manuela Dalla, Afonso, Ana, Diserens, Nicolas, Reese, R. Allan, Cameron, Angus
Preventive veterinary medicine 2014 v.115 no.3-4 pp. 263-279
Cyprinid herpesvirus 3, Infectious hematopoietic necrosis virus, Oncorhynchus mykiss, Viral hemorrhagic septicemia virus, aquaculture, cost effectiveness, data collection, eggs, fish farms, freshwater fish, models, monitoring, pathogens, relative risk, risk estimate, risk factors, risk ranking, septicemia, England, Italy, Switzerland
We developed a model to calculate a quantitative risk score for individual aquaculture sites. The score indicates the risk of the site being infected with a specific fish pathogen (viral haemorrhagic septicaemia virus (VHSV); infectious haematopoietic necrosis virus, Koi herpes virus), and is intended to be used for risk ranking sites to support surveillance for demonstration of zone or member state freedom from these pathogens. The inputs to the model include a range of quantitative and qualitative estimates of risk factors organised into five risk themes (1) Live fish and egg movements; (2) Exposure via water; (3) On-site processing; (4) Short-distance mechanical transmission; (5) Distance-independent mechanical transmission. The calculated risk score for an individual aquaculture site is a value between zero and one and is intended to indicate the risk of a site relative to the risk of other sites (thereby allowing ranking). The model was applied to evaluate 76 rainbow trout farms in 3 countries (42 from England, 32 from Italy and 2 from Switzerland) with the aim to establish their risk of being infected with VHSV. Risk scores for farms in England and Italy showed great variation, clearly enabling ranking. Scores ranged from 0.002 to 0.254 (mean score 0.080) in England and 0.011 to 0.778 (mean of 0.130) for Italy, reflecting the diversity of infection status of farms in these countries. Requirements for broader application of the model are discussed. Cost efficient farm data collection is important to realise the benefits from a risk-based approach.