Various European Member States have implemented control or eradication programmes for endemic infectious diseases in cattle. The design of these programmes varies between countries and therefore comparison of the outputs of different control programmes is complex. Although output-based methods to estimate the confidence of freedom resulting from these programmes are under development, as yet there is no practical modeling framework applicable to a variety of infectious diseases. Therefore, a data collection tool was developed to evaluate data availability and quality and to collect actual input data required for such a modeling framework. The aim of the current paper is to present the key learnings from the process of the development of this data collection tool. The data collection tool was developed by experts from two international projects: STOC free (Surveillance Tool for Outcome-based Comparison of FREEdom from infection, www.stocfree.eu) and SOUND control (Standardizing OUtput-based surveillance to control Non-regulated Diseases of cattle in the EU, www.sound-control.eu). Initially a data collection tool was developed for assessment of freedom of bovine viral diarrhea virus in six Western European countries. This tool was then further generalized to enable inclusion of data for other cattle diseases i.e., infectious bovine rhinotracheitis and Johne's disease. Subsequently, the tool was pilot-tested by a Western and Eastern European country, discussed with animal health experts from 32 different European countries and further developed for use throughout Europe. The developed online data collection tool includes a wide range of variables that could reasonably influence confidence of freedom, including those relating to cattle demographics, risk factors for introduction and characteristics of disease control programmes. Our results highlight the fact that data requirements for different cattle diseases can be generalized and easily included in a data collection tool. However, there are large differences in data availability and comparability across European countries, presenting challenges to the development of a standardized data collection tool and modeling framework. These key learnings are important for development of any generic data collection tool for animal disease control purposes. Further, the results can facilitate development of output-based modeling frameworks that aim to calculate confidence of freedom from disease.
Within the European Union (EU), microbiological criteria (MC) sampling for Salmonella in poultry was introduced in 2005. In particular, processors had to meet a target of fewer than seven positive samples out of 50. However, processors producing small amounts of poultry meat did not have to sample if national authorities determined this was an acceptable risk. The U.K. Food Standards Agency (FSA) thus has a sampling regime based on throughput that allows smaller processors not to sample. In 2011, the limit of 7/50 was reduced to 5/50. Given the current uncertainty regarding U.K. trade relations with the EU, the U.K. FSA decided to conduct a new risk assessment of the risks of Salmonella produced by smaller processors, to determine whether sampling was now necessary. Current evidence suggests that an MC sampling regime in smaller slaughterhouses is not warranted from a national public health perspective. Because of the insensitivities of the MC sampling scheme, the introduction of MC sampling into smaller slaughterhouses would only be necessary if the suspected carcass prevalence was 15% or more. While our analysis is prone to uncertainty, we estimated that the carcass prevalence in smaller processors is below this. Thus, we recommended that the current sampling framework, allowing smaller processors not to sample, was still applicable.
Indirect costs of animal disease outbreaks often significantly exceed the direct costs. Despite their importance, indirect costs remain poorly characterized due to their complexity. In this study, we developed a framework to assess the indirect costs of a hypothetical African swine fever outbreak in Switzerland. We collected data through international and national stakeholder interviews, analysis of national disease control regulations and industry data. We developed a framework to capture the resulting qualitative and quantitative data, categorize the impacts of these regulations, and rank the impacts in order of importance. We then developed a spreadsheet model to calculate the indirect costs of one category of control measure for an individual group of stakeholders. We developed a decision tree model to guide the most economically favourable implementation plan for a given control measure category, under different outbreak scenarios. Our results suggest that the most important measure/impact categories were ‘Transport logistics’, ‘Consumer demand’, ‘Prevention of wild boar and domestic pig contact’ and ‘Slaughter logistics’. In our hypothetical scenario, the greatest costs associated with ‘Prevention of wild boar and domestic pig contact’ were due to assumed partial or total depopulation of fattening pig farms in order to reduce herd size to comply with the simulated control regulations. The model also provides suggestions on the most economically favourable strategy to reduce contact between wild boar and domestic pigs in control areas. Our approach provides a new framework to integrate qualitative and quantitative data to guide disease control strategy. This method could be useful in other countries and for other diseases, including in data‐ and resource‐poor settings, or areas with limited experience of animal disease outbreaks.
Indirect costs of animal disease outbreaks often significantly exceed
the direct costs. Despite their importance, indirect costs remain poorly
characterised due to their complexity. In this study, we developed a
framework to assess the indirect costs of a hypothetical African Swine
Fever outbreak in Switzerland. We collected data through international
and national stakeholder interviews, analysis of national disease
control regulations and industry data. We developed a framework to
capture the resulting qualitative and quantitative data, categorise the
impacts of these regulations, and rank the impacts in order of
importance. We then developed a spreadsheet model to calculate the
indirect costs of one category of control measure for an individual
group of stakeholders. We developed a decision tree model to guide the
most economically favourable implementation plan for a given control
measure category, under different outbreak scenarios. Our results
suggest that the most important measure/impact categories were
‘Transport logistics’, ‘Consumer demand’, ‘Prevention of wild boar and
domestic pig contact’ and ‘Slaughter logistics’. In our hypothetical
scenario, the greatest costs associated with ‘Prevention of wild boar
and domestic pig contact’ were due to assumed partial or total
depopulation of pig farms in order to reduce herd size to comply with
the simulated control regulations. The model also provides suggestions
on the most economically favourable strategy to reduce contact between
wild boar and domestic pigs in control areas depending on the duration
of the outbreak. Our approach provides a new framework to integrate
qualitative and quantitative data to guide disease control strategy.
This method could be useful in other countries and for other diseases,
including in data- and resource-poor settings, or areas with limited
experience of animal disease outbreaks.
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