The Use of Sheep Movement Data to Inform Design and Interpretation of Slaughterhouse-Based Surveillance Activities

Stirling, Julie M.; Eze, Jude; Foster, Geoffrey; Reeves, Aaron; Gunn, George J.; Tongue, Sue C.

doi:10.3389/fvets.2020.00205

Cited by 3 publications

(2 citation statements)

References 36 publications

(41 reference statements)

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“…This is important for capturing neighbour-to-neighbour transmission between contiguous farms and the heterogeneity in long-distance movements that occur between farms that may allow for transmission across large distances. Explicit sheep movement data has previously only been analysed spatially [ 34 ] or included in network models [ 35 , 36 ], but not in metapopulation models for sheep diseases. The model presented here can be used to investigate the transmission dynamics of sheep scab in any geographical region where suitable data are available.…”

Section: Discussionmentioning

confidence: 99%

Sheep scab transmission: a spatially explicit dynamic metapopulation model

Nixon

Brooks-Pollock

Wall

2021

Vet Res

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Psoroptic mange (sheep scab), caused by the parasitic mite, Psoroptes ovis, is an important disease of sheep worldwide. It causes chronic animal welfare issues and economic losses. Eradication of scab has proved impossible in many sheep-rearing areas and recent reports of resistance to macrocyclic lactones, a key class of parasiticide, highlight the importance of improving approaches to scab management. To allow this, the current study aimed to develop a stochastic spatial metapopulation model for sheep scab transmission which can be adapted for use in any geographical region, exhibited here using data for Great Britain. The model uses agricultural survey and sheep movement data to geo-reference farms and capture realistic movement patterns. Reported data on sheep scab outbreaks from 1973 to 1991 were used for model fitting with Sequential Monte Carlo Approximate Bayesian Computation methods. The outbreak incidence predicted by the model was from the same statistical distribution as the reported outbreak data ($${\chi }^{2}$$ χ 2 = 115.3, p = 1) and the spatial location of sheep scab outbreaks predicted was positively correlated with the observed outbreak data by county ($$\tau$$ τ = 0.55, p < 0.001), confirming that the model developed is able to accurately capture the number of farms infected in a year, the seasonality of scab incidence and the spatial patterns seen in the data. This model gives insight into the transmission dynamics of sheep scab and will allow the exploration of more effective control strategies.

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Section: Discussionmentioning

confidence: 99%

Sheep scab transmission: a spatially explicit dynamic metapopulation model

Nixon

Brooks-Pollock

Wall

2021

Vet Res

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“…Due to the structure of surveillance and monitoring datasets the regional analyses are limited. Additional indicators such as trade ( 21 ), and animal movement ( 22 ) should be considered in further studies. Further investigations on the food-chain network between the European countries will support as well further explanations on the variation of resistance combinations between the countries, as these countries have different regulations on monitoring systems ( 23 ).…”

Section: Discussionmentioning

confidence: 99%

A Joint Regional Analysis of Resistance Combinations in Escherichia coli in Humans and Different Food-Producing Animal Populations in Germany Between 2014 and 2017

Suwono

Eckmanns

Kaspar

et al. 2022

Front. Public Health

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A joint comparative regional analysis of different resistance combinations across human and veterinary medicine has not been previously conducted in Germany. This study analyses 16 resistance combinations from four antibiotics in E. coli from different human and food-producing animal populations in three German regions: East, North West and South West. The E. coli data were collected from the three national surveillance and monitoring systems for antimicrobial resistance (AMR) bacteria in humans (ARS), food-safety (Zoonosis Monitoring) and animal pathogens (GERM-Vet) from January 2014 to December 2017. Analyses were performed using cluster analysis (hierarchical clustering, average linkage) in R. We included data from 537,215 E. coli isolates from human clinical isolates, from clinical as well as non-clinical isolates from food-producing animals and from food. The majority of the data originated from the North West region. There were two main clusters built on 54 different human and animal populations. We observed close similarities of resistance combinations in human isolates from the different regions within the same human populations from outpatient cares, general wards and ICUs. These resistance combinations clustered separately from non-clinical isolates from broilers, turkeys, cattle and pigs; except for some of clinical isolates from these populations which clustered closely to isolates from human populations. Frequently, the resistance combinations in E. coli isolates from farms clustered closely to the resistance combinations in isolates from slaughterhouses from broilers and turkeys over all regions. However, the resistance combinations in E. coli isolates from retail meat populations tended to cluster separately within their respective populations in between all regions.

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