Sampling for disease absence—deriving informed monitoring from epidemic traits

Bourhis, Yoann; Gottwald, T. R.; Lopez‐Ruiz, Francisco J.; Patarapuwadol, Sujin; Bosch, Frank van den

doi:10.1101/443895

Cited by 5 publications

(10 citation statements)

References 38 publications

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“…These methodologies have been developed for animal pests and maximum residue level compliance, respectively, but the underlying principles are the same for plant pests. Based on these principles, EFSA developed online software tools for the calculation of sample size in risk-based detection surveys (RiBESS+) and monitoring surveys (SAMPELATOR) that can also be applied for plant pests (Hester et al, 2015;Parnell et al, 2017;Bourhis et al, 2019). Detection surveys allow for conclusions in terms of probabilities of pest presence or absence, whereas monitoring surveys provide estimates for the mean and variance of pest population characteristics.…”

Section: Statistical Background For Sample Size Estimationmentioning

confidence: 99%

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General guidelines for statistically sound and risk‐based surveys of plant pests

Lázaro¹,

Parnell²,

Civera³

et al. 2020

EFS3

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At the request of the European Commission, EFSA prepared the general guidelines for surveys of plant pests, describing the legal, international and scientific context in which the surveys are designed, the basic principles implemented for surveillance of quarantine pests and introducing the concepts needed for the design of statistically sound and risk-based surveys. Three types of specific surveys are addressed: detection surveys for substantiation of pest freedom, delimiting surveys for determining the boundaries of an infested zone, and monitoring surveys for prevalence estimation when measuring the progress of eradication measures or for confirming a low pest prevalence area. For each survey, the survey parameters are introduced and their interactions analysed showing the importance of the assumptions that are taken for each one of them: (i) the aims of the survey are defined as the confidence of detecting a given pest prevalence (design prevalence), this reflects the trade-off between the acceptable level of the risk and availability of resources that determine the strength of the evidence to support the conclusion of the survey; (ii) the target population is addressed in terms of its structure and size, including the risk factors; and (iii) the method sensitivity is defined as the combination of the sampling effectiveness and the diagnostic sensitivity. EFSA's RiBESS+ tool is introduced for calculating the sample size using the survey parameters as input values for a statistically sound and risk-based survey design. The mathematical principles behind the tool are in line with the International Standards for Phytosanitary Measures. The survey design is flexible and can be tailored to each pest and specific situation in the Member States. Once the survey is implemented following this approach, the conclusions allow surveys to be compared across time and space, contributing to the harmonisation of surveillance activities across the EU Member States.

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Section: Statistical Background For Sample Size Estimationmentioning

confidence: 99%

“…Although the above pest freedom approaches, and derivatives thereof, have been widely applied in animal health (e.g. , their application in plant health is relatively new (Hester et al, 2015;Parnell et al, 2017;Bourhis et al, 2019).…”

Section: Confidence Levelmentioning

confidence: 99%

General guidelines for statistically sound and risk‐based surveys of plant pests

Lázaro¹,

Parnell²,

Civera³

et al. 2020

EFS3

View full text Add to dashboard Cite

show abstract

“…4. Mathematical approaches such as rule of three binomial are used to inform surveillance strategy and allocation of resource (Parnell et al, 2017;Bourhis et al, 2019;Jahanbin et al, 2018). Spatial approaches can be employed to improve the surveillance of TB.…”

Section: Recommendations For Further Actionsmentioning

confidence: 99%

Environmental and Socio- Economic Factors as Drivers of Tuberculosis in Nigeria: Paradigm to Adopt Mathematical Approach for Research and Intervention

Shehu¹,

Ike²,

Rabah³

et al. 2020

IJRP

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As the trend of globalization is increasing due to population growth, climate change and another social factor, it continues to increase vulnerability of many diseases. Tuberculosis (TB) disease remains one of the prevalent communicable diseases globally Its significant rate in developing countries like Nigeria has remains a fascinating global public health concern due to public health and economics posed by the disease. This paper reviewed environmental and socioeconomic factors affecting the prevalence; surveillance of TB from authoritative journal articles, grey reports, as well as other official and authoritative sources. The study found the connection between environmental; socioeconomic factors and prevalence and surveillance of TB in Nigeria. At the of study six (6) recommendations such as; education and awareness; online diseases reporting and notification; research priorities; use of mathematical models; inclusive provision of health facilities as well as partnership and collaboration were put forward in relation to environmental and socioeconomic factors and trends.

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“…If a pest is not yet known to be present in a population, then detection surveys are conducted to find outbreaks in their early stages and most often used to confirm (with a prescribed level of certainty) the absence of the pest (Ciubotaru et al 2018 ; Parnell et al 2015 ). Here, surveys are usually risk-targeted to maximise the probability of pest detection (Bourhis et al 2019 ; Hyatt-Twynam et al 2017 ; Parnell et al 2014 ; Martinetti and Soubeyrand 2019 ). If a pest is known to be present, then the focus of surveys changes to either delimitation of infested areas, or to estimates of the pests prevalence and spatial extent (EFSA 2019 ; Hauser et al 2016 ; Brown et al 2017a ).…”

Section: Introductionmentioning

confidence: 99%

The role of passive surveillance and citizen science in plant health

et al. 2020

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The early detection of plant pests and diseases is vital to the success of any eradication or control programme, but the resources for surveillance are often limited. Plant health authorities can however make use of observations from individuals and stakeholder groups who are monitoring for signs of ill health. Volunteered data is most often discussed in relation to citizen science groups, however these groups are only part of a wider network of professional agents, land-users and owners who can all contribute to significantly increase surveillance efforts through “passive surveillance”. These ad-hoc reports represent chance observations by individuals who may not necessarily be looking for signs of pests and diseases when they are discovered. Passive surveillance contributes vital observations in support of national and international surveillance programs, detecting potentially unknown issues in the wider landscape, beyond points of entry and the plant trade. This review sets out to describe various forms of passive surveillance, identify analytical methods that can be applied to these “messy” unstructured data, and indicate how new programs can be established and maintained. Case studies discuss two tree health projects from Great Britain (TreeAlert and Observatree) to illustrate the challenges and successes of existing passive surveillance programmes. When analysing passive surveillance reports it is important to understand the observers’ probability to detect and report each plant health issue, which will vary depending on how distinctive the symptoms are and the experience of the observer. It is also vital to assess how representative the reports are and whether they occur more frequently in certain locations. Methods are increasingly available to predict species distributions from large datasets, but more work is needed to understand how these apply to rare events such as new introductions. One solution for general surveillance is to develop and maintain a network of tree health volunteers, but this requires a large investment in training, feedback and engagement to maintain motivation. There are already many working examples of passive surveillance programmes and the suite of options to interpret the resulting datasets is growing rapidly.

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Sampling for disease absence—deriving informed monitoring from epidemic traits

Cited by 5 publications

References 38 publications

General guidelines for statistically sound and risk‐based surveys of plant pests

General guidelines for statistically sound and risk‐based surveys of plant pests

Environmental and Socio- Economic Factors as Drivers of Tuberculosis in Nigeria: Paradigm to Adopt Mathematical Approach for Research and Intervention

The role of passive surveillance and citizen science in plant health

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