No abstract

Summary Trading of aquatic animals and aquatic animal products has become increasingly globalized during the last couple of decades. This commodity trade has increased the risk for the spread of aquatic animal pathogens. The World Organisation for Animal Health (OIE) is recognized as the international standard‐setting organization for measures relating to international trade in animals and animal products. In this role, OIE has developed the Aquatic Animal Health Code, which provides health measures to be used by competent authorities of importing and exporting countries to avoid the transfer of agents pathogenic for animals or humans, whilst avoiding unjustified sanitary barriers. An OIE ad hoc group developed criteria for assessing the safety of aquatic animals or aquatic animal products for any purpose from a country, zone or compartment not declared free from a given disease ‘X’. The criteria were based on the absence of the pathogenic agent in the traded commodity or inactivation of the pathogenic agent by the commercial processing used to produce the commodity. The group also developed criteria to assess the safety of aquatic animals or aquatic animal products for retail trade for human consumption from potentially infected areas. Such commodities were assessed considering the form and presentation of the product, the expected volume of waste tissues generated by the consumer and the likely presence of viable pathogenic agent in the waste. The ad hoc group applied the criteria to commodities listed in the individual disease chapters of the Aquatic Animal Health Code (2008 edition). Revised lists of commodities for which no additional measures should be required by the importing countries regardless of the status for disease X of the exporting country were developed and adopted by the OIE World Assembly of Delegates in May 2011. The rationale of the criteria and their application will be explained and demonstrated using examples.

No abstract

No abstract

Infectious salmon anaemia (ISA) can be a serious viral disease of farmed Atlantic salmon (Salmo salar). A tool to rank susceptible farms based on the risk of ISA virus (ISAv) infection spread from infectious farms after initial incursion or re-occurrence in an endemic area, can help guide monitoring and surveillance activities. Such a tool could also support the response strategy to contain virus spread, given available resources. We developed a tool to rank ISAv infection risks using seaway distance and hydrodynamic information separately and combined. The models were validated using 2002-2004 ISAv outbreak data for 30 farms (24 in New Brunswick, Canada and 6 in Maine, United States). Time sequence of infection spread was determined from the outbreak data that included monthly infection status of the cages on these farms. The first infected farm was considered as the index site for potential spread of ISAv to all other farms. To assess the risk of ISAv spreading to susceptible farms, the second and subsequent infected farms were identified using the farm status in the given time period and all infected farms from the previous time periods. Using the three models (hydrodynamic only, seaway-distance, and combined hydrodynamic-seaway-distance based models), we ranked susceptible farms within each time interval by adding the transmission risks from surrounding infected farms and sorting them from highest to lowest. To explore the potential efficiency of targeted sampling, we converted rankings to percentiles and assessed the model's predictive performance by comparing farms identified as high risk based on the rank with those that were infected during the next time interval as observed in the outbreak data. The overall predictive ability of the models was compared using area under the ROC curve (AUC). Farms that become infected in the next period were always within the top 65% of the rank predicted by our models. The overall predictive ability of the combined (hydrodynamic-seaway-distance based model) model (AUC = 0.833) was similar to the model that only used seaway distance (AUC = 0.827). Such models can aid in effective surveillance planning by balancing coverage (number of farms included in surveillance) against the desired level of confidence of including all farms that become infected in the next time period. Our results suggest that 100% of the farms that become infected in the next time period could be targeted in a surveillance program, although at a significant cost of including many false positives.