Abstract
There are calls from policy-makers and industry to use existing data sources to contribute to livestock surveillance systems, especially for syndromic surveillance. However, the practical implications of attempting to use such data sources are challenging; development often requires incremental steps in an iterative cycle. In this study the utility of business operational data from a voluntary fallen stock collection service was investigated, to determine if they could be used as a proxy for the mortality experienced by the British sheep population. Retrospectively, Scottish ovine fallen stock collection data (2011–2014) were transformed into meaningful units for analysis, temporal and spatial patterns were described, time-series methods and a temporal aberration detection algorithm applied. Distinct annual and spatial trends plus seasonal patterns were observed in the three age groups investigated. The algorithm produced an alarm at the point of an historic known departure from normal (April 2013) for two age groups, across Scotland as a whole and in specific postcode areas. The analysis was then extended. Initially, to determine if similar methods could be applied to ovine fallen stock collections from England and Wales for the same time period. Additionally, Scottish contemporaneous laboratory diagnostic submission data were analyzed to see if they could provide further insight for interpretation of statistical alarms. Collaboration was required between the primary data holders, those with industry sector knowledge, plus veterinary, epidemiological and statistical expertise, in order to turn data and analytical outcomes into potentially useful information. A number of limitations were identified and recommendations were made as to how some could be addressed in order to facilitate use of these data as surveillance “intelligence.” e.g., improvements to data collection and provision. A recent update of the fallen stock collections data has enabled a longer temporal period to be analyzed, with evidence of changes made in line with the recommendations. Further development will be required before a functional system can be implemented. However, there is potential for use of these data as: a proxy measure for mortality in the sheep population; complementary components in a future surveillance system, and to inform the design of additional surveillance system components.
Highlights
In the last two decades the surveillance of animal, especially livestock, populations has become an increasingly discussed topic
An existing data source—business operational data from a voluntary fallen stock collection service—was investigated, to determine if these data could be used as a proxy for the mortality experienced by the British sheep population, in the absence of any other appropriate data source for this species
Despite various limitations, these data could, with appropriate domain expertise, be converted into a useable format. They did reflect the seasonal pattern expected from knowledge of the British sheep production-year calendar [47]; the spatial distribution of the sheep population of Great Britain (GB) [48, 49] and the slight variations across Britain associated with the different sheep management systems and its stratified nature [50]
Summary
In the last two decades the surveillance of animal, especially livestock, populations has become an increasingly discussed topic. The use of automated bio-surveillance systems for outbreak detection and syndromic surveillance (SynS) in the human field [10,11,12,13,14] stimulated studies within the veterinary sphere, reviewed in Dupuy et al [15]. They found that, in 2013, there were 27 veterinary syndromic surveillance systems in 12 European countries, most of these did not yet have the statistical wherewithal to adequately analyse the data. There is evidence of further development of digital surveillance systems for animal populations [23], they have not yet matured into fully functional, digital, automated biosurveillance systems providing outbreak detection, syndromic surveillance, monitoring of trends and situational awareness in animal populations
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