In order to prevent and control the emergence of biosecurity threats such as vector-borne diseases of plants, it is vital to understand drivers of entry, establishment, and spatio-temporal spread, as well as the form, timing and effectiveness of disease management strategies. An inherent challenge for policy in combatting emerging disease is the uncertainty associated with intervention planning in areas not yet affected, based on models and data from current outbreaks. Following the recent high-profile emergence of the bacterium Xylella fastidiosa in several European countries, we review the most pertinent epidemiological uncertainties concerning this bacterium dynamics in novel environments. To reduce the considerable ecological and socio-economic impacts of these outbreaks, eco-epidemiological research in a broader range of environmental conditions needs to be conducted and used to inform policy to enhance disease risk assessment, and support successful policy-making decisions. By characterising infection pathways, we can highlight the uncertainties that surround our knowledge of this disease, drawing attention to how these are amplified when trying to predict and manage outbreaks in currently unaffected locations. To help guide future research and decision-making processes, we invited experts in different fields of plant pathology to identify data to prioritise when developing pest-risk assessments. Our analysis revealed that epidemiological uncertainty is mainly driven by the large variety of hosts, vectors, and bacterial strains, leading to a range of different epidemiological characteristics further magnified by novel environmental conditions. These results offer new insights on how eco-epidemiological analyses can enhance understanding of plant disease spread and support management recommendations.
Consiglio per la ricerca in agricoltura e l'analisi dell'economia agrarian (CREA),
Innovation in environmental fields such as plant health is complex because of unbounded challenges and lack of certainty of commercial uptake. In this paper we present a Technology Readiness Level (TRL) framework, specifically to assist with assessment of technologies to support detection of tree pests and pathogens, but also for wider potential adaptation. Biosecurity can be enhanced by improved early detection of pests and pathogens, but development and deployment of new technologies requires robust scrutiny. We critically analyse the concept, practice and applicability of TRLs. Interviews revealed scientist perspectives during the development process of five novel early plant pest and pathogen detection technologies. A retrospective, collective narrative of one technology from concept to commercial deployment was undertaken. We then developed a calculator tool for assessment of biosecurity TRLs. Our findings illustrate the iterative process of technology development, the challenges in final TRLs of acquiring funding to move from proven success to viable product, inefficiencies created through the need for multiple projects for each technology and the imperative to consider the wider socio-ecological technical landscape, including policy context. End user engagement was particularly valuable at beginning and end of the TRL scale. We conclude that the TRL framework comprises a robust approach to assess technologies in that it facilitates progress tracking, evaluation of success likelihood and identification of opportunities for investment. However, its potential will only be realised for environmental management if it is integrated into the socio-ecological technical landscape and wider discussions regarding knowledge co-production and valuing nature.
Ensuring the reliability of diagnostic activities is an essential cornerstone of Plant Health strategies to reduce the risk of entry and spread of plant pests in a region and ultimately their impacts. Diagnostic tests should be validated to ensure that they are fit for purpose. Validation is usually done by diagnostic laboratories although companies commercializing diagnostic kits also produce validation data for their products. Due to the high number of pest , matrix and method combinations and given the significant resources required to validate tests, it is essential that validation data are shared with the entire diagnostic community and produced in a harmonized way to facilitate their use by different stakeholders. Indeed, the selection of tests to be used in specific contexts is not the sole responsibility of diagnostic laboratories and also involve National Plant Protection Organizations. The VALITEST EU project (2018-2021) was established to tackle all these issues. New validation data for tests targeting important pests for the EPPO region were produced. Guidelines to improve and harmonize the validation framework were developed. Sharing of validation data and experience was ensured through the development of new or existing databases, the organization of training courses and the dissemination of the project outputs in scientific publications and Standards. Finally, the involvement of researchers, diagnosticians, policy makers, inspectors, industries etc. and the establishment of the European Plant Diagnostic Industry Association were important actions to strengthen the interactions between Plant Health stakeholders.
Early detection of pests at a low population prevalence is a key pillar of surveillance for plant health. A novel pest is expected to undergo exponential growth when it first occurs in a host population. This allows the use of mathematical rules of thumb for the number of samples required to detect presence before it reaches a target prevalence given the diagnostic performance of the tests used. Previous work assumes that pest presence is diagnosed by applying a single diagnostic test to each sample. However, diagnostic decisions are often made based on outcomes of multiple tests applied to the same sample in a consistent test program: e.g. an assessment of symptoms followed by a PCR test applied to samples for which symptoms were observed. Each test may have different testing costs, as well as distinct and independent diagnostic performance and uncertainty values. A framework to optimize early detection surveys by minimizing overall costs for test programs that apply up to three diagnostic tests has been developed. The framework assesses the consequences of the test order and logical rule by which diagnoses are determined on costs across a range of pest prevalences. Explicit definitions of uncertainty in key parameters are incorporated to assess the consequences of uncertainty about their true value. Use of the framework is exemplified with two hypothetical case studies exploring the potential impact of selecting a sub-optimal test program (based on Xylella fastidiosa) and investment in test improvements (based on lateral flow devices (LFDs) applied to Phytophthora pluvialis).
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