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      Assessing the probability of freedom from pine wood nematode based on 19 years of surveys

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      NeoBiota

      Pensoft Publishers

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          Abstract

          Many quarantine pests, such as the pine wood nematode (PWN, Bursaphelenchus xylophilus), are surveyed annually in all EU countries. Although a lot of resources are spent in the surveys, the confidence in pest freedom achieved with them is not commonly analysed. We assessed the probability that Finland is free from PWN, based on the surveys done in 2000–2018. We used the methods employed in the risk-based estimate of system sensitivity tool (RiBESS), which has recently been recommended for quarantine pest applications. We considered two scenarios: 1) the surveys aimed to justify phytosanitary import requirements and to facilitate exports and 2) the surveys aimed to detect invasions early to enable eradication of outbreaks. These differed only in the pest prevalence that the surveys were expected to detect. The surveys appeared to support the assumption that PWN is not present in Finland, but they did not seem extensive enough to ensure early detection of invasions. The sensitivity of the import-export surveys was greater than 0.6 in 13 years, whereas that of the early detection surveys was always below 0.25. The probability of freedom achieved in 2018 following 19 years of surveys increased asymptotically with the mean time between invasions. For the import-export surveys, this probability was at least 0.95 unless the mean time between invasions was less than 13 years. For the early detection surveys, the probability of freedom was less than 0.73 unless the mean time between invasions was 63 years or more. The results were rather robust with respect to the parameters for which exact information was lacking. To improve the assessment, a quantitative estimate of the probability of PWN invasion to Finland and a thorough assessment of the maximum area of an eradicable infestation would be needed. To gain an understanding about the true impact of quarantine pest surveys on biosecurity, more assessments, like the one presented in this paper, are needed.

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          Most cited references 38

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          Guidance on quantitative pest risk assessment

          Abstract This Guidance describes a two‐phase approach for a fit‐for‐purpose method for the assessment of plant pest risk in the territory of the EU. Phase one consists of pest categorisation to determine whether the pest has the characteristics of a quarantine pest or those of a regulated non‐quarantine pest for the area of the EU. Phase two consists of pest risk assessment, which may be requested by the risk managers following the pest categorisation results. This Guidance provides a template for pest categorisation and describes in detail the use of modelling and expert knowledge elicitation to conduct a pest risk assessment. The Guidance provides support and a framework for assessors to provide quantitative estimates, together with associated uncertainties, regarding the entry, establishment, spread and impact of plant pests in the EU. The Guidance allows the effectiveness of risk reducing options (RROs) to be quantitatively assessed as an integral part of the assessment framework. A list of RROs is provided. A two‐tiered approach is proposed for the use of expert knowledge elicitation and modelling. Depending on data and resources available and the needs of risk managers, pest entry, establishment, spread and impact steps may be assessed directly, using weight of evidence and quantitative expert judgement (first tier), or they may be elaborated in substeps using quantitative models (second tier). An example of an application of the first tier approach is provided. Guidance is provided on how to derive models of appropriate complexity to conduct a second tier assessment. Each assessment is operationalised using Monte Carlo simulations that can compare scenarios for relevant factors, e.g. with or without RROs. This document provides guidance on how to compare scenarios to draw conclusions on the magnitude of pest risks and the effectiveness of RROs and on how to communicate assessment results.
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            A new probability formula for surveys to substantiate freedom from disease.

            Surveys to substantiate freedom from disease are becoming increasingly important. This is due to the changes in rules governing international trade in animals and animal products, and to an increase in disease eradication and herd-level accreditation schemes. To provide the necessary assurances, these surveys must have a sound theoretical basis. Until now, most surveys have been based on the assumption that the screening test used was perfect (sensitivity and specificity both equal to one), and/or that the study population was infinite. Clearly, these assumptions are virtually always invalid. This paper presents a new formula that calculates the exact probability of detecting diseased animals, and considers both imperfect tests and finite population size. This formula is computationally inconvenient, and an approximation that is simpler to calculate is also presented. The use of these formulae for sample-size calculation and analysis of survey results is discussed. A computer program, 'FreeCalc', implementing the formulae is presented along with examples of sample size calculation for two different scenarios. These formulae and computer program enable the accurate calculation of survey sample-size requirements, and the precise analysis of survey results. As a result, survey costs can be minimised, and survey results will reliably provide the required level of proof.
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              Demonstrating freedom from disease using multiple complex data sources 1: a new methodology based on scenario trees.

              Current methods to demonstrate zone or country freedom from disease are based on either quantitative analysis of the results of structured representative surveys, or qualitative assessments of multiple sources of evidence (including complex non-representative sources). This paper presents a methodology for objective quantitative analysis of multiple complex data sources to support claims of freedom from disease. Stochastic scenario tree models are used to describe each component of a surveillance system (SSC), and used to estimate the sensitivity of each SSC. The process of building and analysing the models is described, as well as techniques to take into account any lack of independence between units at different levels within a SSC. The combination of sensitivity estimates from multiple SSCs into a single estimate for the entire surveillance system is also considered, again taking into account lack of independence between components. A sensitivity ratio is used to compare different components of a surveillance system. Finally, calculation of the probability of country freedom from the estimated sensitivity of the surveillance system is illustrated, incorporating the use and valuation of historical surveillance evidence.
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                Author and article information

                Journal
                NeoBiota
                NB
                Pensoft Publishers
                1314-2488
                1619-0033
                July 08 2020
                July 08 2020
                : 58
                : 75-106
                Article
                10.3897/neobiota.58.38313
                © 2020

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