A Simple Model to Rank Shellfish Farming Areas Based on the Risk of Disease Introduction and Spread

Abstract

Summary The European Union Council Directive 2006/88/EC requires that risk‐based surveillance (RBS) for listed aquatic animal diseases is applied to all aquaculture production businesses. The principle behind this is the efficient use of resources directed towards high‐risk farm categories, animal types and geographic areas. To achieve this requirement, fish and shellfish farms must be ranked according to their risk of disease introduction and spread. We present a method to risk rank shellfish farming areas based on the risk of disease introduction and spread and demonstrate how the approach was applied in 45 shellfish farming areas in England and Wales. Ten parameters were used to inform the risk model, which were grouped into four risk themes based on related pathways for transmission of pathogens: (i) live animal movement, (ii) transmission via water, (iii) short distance mechanical spread (birds) and (iv) long distance mechanical spread (vessels). Weights (informed by expert knowledge) were applied both to individual parameters and to risk themes for introduction and spread to reflect their relative importance. A spreadsheet model was developed to determine quantitative scores for the risk of pathogen introduction and risk of pathogen spread for each shellfish farming area. These scores were used to independently rank areas for risk of introduction and for risk of spread. Thresholds were set to establish risk categories (low, medium and high) for introduction and spread based on risk scores. Risk categories for introduction and spread for each area were combined to provide overall risk categories to inform a risk‐based surveillance programme directed at the area level. Applying the combined risk category designation framework for risk of introduction and spread suggested by European Commission guidance for risk‐based surveillance, 4, 10 and 31 areas were classified as high, medium and low risk, respectively.

Validation of a model for ranking aquaculture facilities for risk-based disease surveillance

“…This study showed that a questionnaire does not suffice to gather farm data of a sufficient quality for performing a risk-based surveillance of aquaculture facilities. However, depending on the resources available, it might not be possible for the authorities to visit every single farm on a regular basis for the collection of data necessary for a risk analysis (Thrush et al 2016). Moreover, the cost-benefit ratio of collecting field data versus relying solely on questionnaire data needs to be considered.…”

Ecosystem modelling to assess the impact of rearing density, environment variability and mortality on oyster production

“…Several studies have assessed the impact of global changes, in cluding those related to climate, on the growth and biogeo graphy of cultivated bivalves (Guyondet et al 2015, Filgueira et al 2016, Thomas et al 2016, Thomas & Bacher 2018, Steeves et al 2018). Modelling tools have also been developed to assess disease propagation amongst individuals and be tween farms and to potentially help the producers facing these phenomena (Thrush et al 2017, Gang nery et al 2019, Lupo et al 2019, 2020. Due to their complexity, few models allow the combination of a large number of changing scenarios to help with decision making.…”

Risk Assessment of Highly Pathogenic Avian Influenza by Comparison of Biosecurity Level in Domestic Poultry Farms

“…Specifically, the items not displayed in other 1, other 2 and other 3 are as follows. (Allepuz et al, 2018;Van Limbergen et al, 2018 (Thrush et al, 2017;Allepuz et al, 2018…”