SurF: an innovative framework in biosecurity and animal health surveillance evaluation

Muellner, Petra; Watts, Jonathan; Bingham, Paul; Bullians, M.S.; Gould, Brendan; Pande, Anjali; Riding, Tim; Stevens, P. H. M.; Vink, Daan; Stärk, K. D. C.

doi:10.1111/tbed.12898

Cited by 5 publications

(3 citation statements)

References 7 publications

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“…While the outputs presented provide a quantitative estimate of the surveillance sensitivity and probability of freedom over a period of time, there are other factors which influence the ability of surveillance to detect disease. Well‐described frameworks for the evaluation of surveillance activities in animal health have been published (Calba et al., 2013; Cameron et al., 2014; Comin et al., 2019; Drewe et al., 2015; Hoinville et al., 2013; Muellner et al., 2018); the results of this study would be best contextualized within one of these frameworks to provide a more holistic evaluation of AHS surveillance in the controlled area of South Africa.…”

Section: Discussionmentioning

confidence: 99%

Post‐outbreak African horse sickness surveillance: A scenario tree evaluation in South Africa’s controlled area

Grewar

Porphyre

Sergeant

et al. 2020

Transbound Emerg Dis

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An African horse sickness (AHS) outbreak occurred in March and April 2016 in the controlled area of South Africa. This extended an existing trade suspension of live equids from South Africa to the European Union. In the post‐outbreak period ongoing passive and active surveillance, the latter in the form of monthly sentinel surveillance and a stand‐alone freedom from disease survey in March 2017, took place. We describe a stochastic scenario tree analysis of these surveillance components for 24 months, starting July 2016, in three distinct geographic areas of the controlled area. Given that AHS was not detected, the probability of being free from AHS was between 98.3% and 99.8% assuming that, if it were present, it would have a prevalence of at least one infected animal in 1% of herds. This high level of freedom probability had been attained in all three areas within the first 9 months of the 2‐year period. The primary driver of surveillance outcomes was the passive surveillance component. Active surveillance components contributed minimally (<0.2%) to the final probability of freedom. Sensitivity analysis showed that the probability of infected horses showing clinical signs was an important parameter influencing the system surveillance sensitivity. The monthly probability of disease introduction needed to be increased to 20% and greater to decrease the overall probability of freedom to below 90%. Current global standards require a 2‐year post‐incursion period of AHS freedom before re‐evaluation of free zone status. Our findings show that the length of this period could be decreased if adequately sensitive surveillance is performed. In order to comply with international standards, active surveillance will remain a component of AHS surveillance in South Africa. Passive surveillance, however, can provide substantial evidence supporting AHS freedom status declarations, and further investment in this surveillance activity would be beneficial.

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Section: Discussionmentioning

confidence: 99%

Post‐outbreak African horse sickness surveillance: A scenario tree evaluation in South Africa’s controlled area

Grewar

Porphyre

Sergeant

et al. 2020

Transbound Emerg Dis

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“…Unlike existing tools promoting structured ways to design or evaluate AHS [e.g., RISKSUR design and EVA tools (5, 11), SERVAL (12), SurF (13)], AHSURED does not involve any assessment of surveillance performances, but rather aims at documenting how surveillance activities were designed and carried out. The focus of AHSURED is really on communication, through the systematic description of how the output of surveillance have been generated.…”

Section: Discussionmentioning

confidence: 99%

Development of Reporting Guidelines for Animal Health Surveillance—AHSURED

Comin

Grewar²,

Schaik

et al. 2019

Front. Vet. Sci.

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With the current trend in animal health surveillance toward risk-based designs and a gradual transition to output-based standards, greater flexibility in surveillance design is both required and allowed. However, the increase in flexibility requires more transparency regarding surveillance, its activities, design and implementation. Such transparency allows stakeholders, trade partners, decision-makers and risk assessors to accurately interpret the validity of the surveillance outcomes. This paper presents the first version of the Animal Health Surveillance Reporting Guidelines (AHSURED) and the process by which they have been developed. The goal of AHSURED was to produce a set of reporting guidelines that supports communication of surveillance activities in the form of narrative descriptions. Reporting guidelines come from the field of evidence-based medicine and their aim is to improve consistency and quality of information reported in scientific journals. They usually consist of a checklist of items to be reported, a description/definition of each item, and an explanation and elaboration document. Examples of well-reported items are frequently provided. Additionally, it is common to make available a website where the guidelines are documented and maintained. This first version of the AHSURED guidelines consists of a checklist of 40 items organized in 11 sections (i.e., surveillance system building blocks), which is available as a wiki at https://github.com/SVA-SE/AHSURED/wiki. The choice of a wiki format will allow for further inputs from surveillance experts who were not involved in the earlier stages of development. This will promote an up-to-date refined guideline document.

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“…Consequently, a review of 12 evaluation tools focusing either on surveillance evaluation or on OH evaluation was conducted between January and December 2019, as a part of the research conducted with the CoEval-AMR Network (Convergence in evaluation frameworks for integrated surveillance of AM resistance and AM use), of which five of the authors are members ( 20 ). The following 12 tools were included: [1] Evaluation of collaboration for surveillance (EcoSurTool) ( 21 ), [2] Network for the Evaluation of OH Framework (NEOH) ( 22 ), [3] OH Assessment for Planning and Performance (OH-APP) ( 23 ), [4] The FAO Assessment Tool for Laboratory and AMR Surveillance Systems (ATLASS) ( 24 ), [5] Outil d'Analyse des Systèmes de Surveillance (OASIS) ( 25 ), [6] SuRveillance EVALuation framework (SERVAL) ( 26 ), [7] SurvTools ( 27 ), [8] Surveillance Evaluation Framework (SurF) ( 28 ), [9] The FAO Progressive Management Pathway for AMR (PMP-AMR) ( 29 ), [10] Joint External Evaluation tool (Second edition) (JEE) ( 30 ), [11] International Health Regulation core capacity monitoring framework (IHR) ( 31 ), and [12] The OIE Tool for the Evaluation of Performance of Veterinary Services (PVS) ( 32 ) ( Table 4 ). For each tool, the general purpose, scope, process, and output were synthesized by the CoEval-AMR research team and all the evaluation items (questions or criteria) from these tools were extracted, examined, and attributed to one or more ISSE evaluation levels by three different analysts (Aenishaenslin, Mediouni, and Bennani) ( 20 ).…”

Section: Methodsmentioning

confidence: 99%

Evaluating the Integration of One Health in Surveillance Systems for Antimicrobial Use and Resistance: A Conceptual Framework

et al. 2021

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It is now widely acknowledged that surveillance of antimicrobial resistance (AMR) must adopt a “One Health” (OH) approach to successfully address the significant threats this global public health issue poses to humans, animals, and the environment. While many protocols exist for the evaluation of surveillance, the specific aspect of the integration of a OH approach into surveillance systems for AMR and antimicrobial Use (AMU), suffers from a lack of common and accepted guidelines and metrics for its monitoring and evaluation functions. This article presents a conceptual framework to evaluate the integration of OH in surveillance systems for AMR and AMU, named the Integrated Surveillance System Evaluation framework (ISSE framework). The ISSE framework aims to assist stakeholders and researchers who design an overall evaluation plan to select the relevant evaluation questions and tools. The framework was developed in partnership with the Canadian Integrated Program for Antimicrobial Resistance Surveillance (CIPARS). It consists of five evaluation components, which consider the capacity of the system to: [1] integrate a OH approach, [2] produce OH information and expertise, [3] generate actionable knowledge, [4] influence decision-making, and [5] positively impact outcomes. For each component, a set of evaluation questions is defined, and links to other available evaluation tools are shown. The ISSE framework helps evaluators to systematically assess the different OH aspects of a surveillance system, to gain comprehensive information on the performance and value of these integrated efforts, and to use the evaluation results to refine and improve the surveillance of AMR and AMU globally.

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SurF: an innovative framework in biosecurity and animal health surveillance evaluation

Cited by 5 publications

References 7 publications

Post‐outbreak African horse sickness surveillance: A scenario tree evaluation in South Africa’s controlled area

Post‐outbreak African horse sickness surveillance: A scenario tree evaluation in South Africa’s controlled area

Development of Reporting Guidelines for Animal Health Surveillance—AHSURED

Evaluating the Integration of One Health in Surveillance Systems for Antimicrobial Use and Resistance: A Conceptual Framework

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