Combining surveillance and expert evidence of viral hemorrhagic septicemia freedom: A decision science approach

Gustafson, Lori; Klotins, K.; Tomlinson, Sarah M.; Karreman, G.; Cameron, Angus; Wagner, Bruce A.; Remmenga, Marta D.; Bruneau, Nathalie; Scott, Aaron E.

doi:10.1016/j.prevetmed.2009.11.021

Cited by 30 publications

(11 citation statements)

References 35 publications

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“…The initial selection of cattle diseases to be included in the smartphone-app was based on an interaction, using a modified Delphi protocol [ 19 ], with 17 experienced veterinarians from the College of Veterinary Medicine and Agriculture of Addis Ababa University in May 2014. Based on this Delphi exercise, 12 diseases were identified as being of particular significance for Ethiopia in terms of economic importance and/or had a high likelihood of providing a challenge in terms of the interpretation of clinical signs.…”

Section: Methodsmentioning

confidence: 99%

Assisting differential clinical diagnosis of cattle diseases using smartphone-based technology in low resource settings: a pilot study

et al. 2017

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BackgroundThe recent rise in mobile phone use and increased signal coverage has created opportunities for growth of the mobile Health sector in many low resource settings. This pilot study explores the use of a smartphone-based application, VetAfrica-Ethiopia, in assisting diagnosis of cattle diseases. We used a modified Delphi protocol to select important diseases and Bayesian algorithms to estimate the related disease probabilities based on various clinical signs being present in Ethiopian cattle.ResultsA total of 928 cases were diagnosed during the study period across three regions of Ethiopia, around 70% of which were covered by diseases included in VetAfrica-Ethiopia. Parasitic Gastroenteritis (26%), Blackleg (8.5%), Fasciolosis (8.4%), Pasteurellosis (7.4%), Colibacillosis (6.4%), Lumpy skin disease (5.5%) and CBPP (5.0%) were the most commonly occurring diseases. The highest (84%) and lowest (30%) levels of matching between diagnoses made by student practitioners and VetAfrica-Ethiopia were for Babesiosis and Pasteurellosis, respectively. Multiple-variable logistic regression analysis indicated that the putative disease indicated, the practitioner involved, and the level of confidence associated with the prediction made by VetAfrica-Ethiopia were major determinants of the likelihood that a diagnostic match would be obtained.ConclusionsThis pilot study demonstrated that the use of such applications can be a valuable means of assisting less experienced animal health professionals in carrying out disease diagnosis which may lead to increased animal productivity through appropriate treatment.

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Section: Methodsmentioning

confidence: 99%

Assisting differential clinical diagnosis of cattle diseases using smartphone-based technology in low resource settings: a pilot study

et al. 2017

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“…It is unlikely that there will be published data on which to weight risk themes and routes within themes, and thus, expert opinion is needed. Formal methods to elicit expert opinion have been developed and applied in aquatic animal health management (Gustafson et al., ; Peeler et al., ; Oidtmann et al., ). The parameterization of this model for England and Wales could be improved by a wide and more formal expert elicitation exercise.…”

Section: Discussionmentioning

confidence: 99%

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

Thrush

Pearce

Gubbins

et al. 2016

Transbound Emerg Dis

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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.

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“…The survey was developed and implemented as a modified Delphi group process. This method was chosen because it is the most commonly used survey method in ecology and veterinary epidemiology and could be adapted to the large number of expert groups required for this study (Kuhnert et al, 2005(Kuhnert et al, , 2010Gustafson et al, 2010Gustafson et al, , 2013. The goal of this survey was to develop data on intrastate cattle shipments with good geographic coverage of the continental U.S.…”

Section: Expert Elicitation Survey Development and Implementationmentioning

confidence: 99%

Estimating and exploring the proportions of inter- and intrastate cattle shipments in the United States

Beck‐Johnson

Hallman

Miller

et al. 2019

Preventive Veterinary Medicine

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Mathematical models are key tools for the development of surveillance, preparedness and response plans for the potential events of emerging and introduced foreign animal diseases. Creating these types of plans requires data; when data are incomplete, mathematical models can help fill in missing information, provided they are informed by the data that are available. In the United States, the most complete national-scale data available on cattle shipments are based on Interstate Certificates of Veterinary Inspection, which track the shipment of cattle between states; data on intrastate cattle shipments are lacking. Here we develop four new datasets on intrastate cattle shipments in the U.S., including an expert elicitation survey covering 19 states and territories and three state-level brand inspection data sets. The expert elicitation survey provides estimates on the proportion of shipments that travel interstate over multiple regions of the U.S. These survey data also identify differences in shipment patterns between regions, cattle commodity types, and sectors of the cattle industry. These survey data cover more states than any other source of intrastate data; however, one limitation of these data is the small number of participating experts in many of the states, only seven of the 19 responding states and territories had a group size of three or larger. The brand data sets include origin and destination information for both intra-and interstate shipments. These data, therefore, also provide detailed information on the proportion of interstate shipments in three Western states, including the temporal and geographic variation in shipments. Because the survey and brand data overlap in the Western U.S., they can be compared. We find that in the Western U.S. the expert estimates of the overall proportion of cattle shipments matched the brand data well. However, the experts estimated that there would be larger differences in beef and dairy shipments than the brand data show. This suggests the cattle industries in the West may be sending similar proportions of commodity specific cattle shipments over state lines. We additionally used the expert survey data to explore how differences in the proportion of interstate shipments can change predictions about cattle shipment patterns using the example of model-guided suggestions for targeted surveillance in Texas. Together these four data sets are the most extensive and geographically comprehensive information to date on intrastate cattle shipments. Additionally, our analyses on predicted shipment patterns suggest that assumptions about intrastate shipments could have consequences for targeted surveillance.

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Combining surveillance and expert evidence of viral hemorrhagic septicemia freedom: A decision science approach

Cited by 30 publications

References 35 publications

Assisting differential clinical diagnosis of cattle diseases using smartphone-based technology in low resource settings: a pilot study

Assisting differential clinical diagnosis of cattle diseases using smartphone-based technology in low resource settings: a pilot study

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

Estimating and exploring the proportions of inter- and intrastate cattle shipments in the United States

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