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Evaluation of farm-level parameters derived from animal movements for use in risk-based surveillance programmes of cattle in Switzerland.

Schärrer S, Widgren S, Schwermer H, Lindberg A, Vidondo B, Zinsstag J, Reist M - BMC Vet. Res. (2015)

Bottom Line: Validation of the scores against results from the BVD surveillance programme 2013 gave promising results for setting the cut off for each of the five selected farm level criteria at the 50th percentile.Restricting testing to farms with a score ≥ 2 would have resulted in the same number of detected BVD positive farms as testing all farms, i.e., the outcome of the 2013 surveillance programme could have been reached with a smaller survey.The proposed method is a promising framework for the selection of farms according to the risk of infection based on animal movements.

View Article: PubMed Central - PubMed

Affiliation: Veterinary Public Health Institute (VPHI), Vetsuisse Faculty, University of Bern, Bern, Switzerland. sara.schaerrer@vetsuisse.unibe.ch.

ABSTRACT

Background: This study focused on the descriptive analysis of cattle movements and farm-level parameters derived from cattle movements, which are considered to be generically suitable for risk-based surveillance systems in Switzerland for diseases where animal movements constitute an important risk pathway.

Methods: A framework was developed to select farms for surveillance based on a risk score summarizing 5 parameters. The proposed framework was validated using data from the bovine viral diarrhoea (BVD) surveillance programme in 2013.

Results: A cumulative score was calculated per farm, including the following parameters; the maximum monthly ingoing contact chain (in 2012), the average number of animals per incoming movement, use of mixed alpine pastures and the number of weeks in 2012 a farm had movements registered. The final score for the farm depended on the distribution of the parameters. Different cut offs; 50, 90, 95 and 99%, were explored. The final scores ranged between 0 and 5. Validation of the scores against results from the BVD surveillance programme 2013 gave promising results for setting the cut off for each of the five selected farm level criteria at the 50th percentile. Restricting testing to farms with a score ≥ 2 would have resulted in the same number of detected BVD positive farms as testing all farms, i.e., the outcome of the 2013 surveillance programme could have been reached with a smaller survey.

Conclusions: The seasonality and time dependency of the activity of single farms in the networks requires a careful assessment of the actual time period included to determine farm level criteria. However, selecting farms in the sample for risk-based surveillance can be optimized with the proposed scoring system. The system was validated using data from the BVD eradication program. The proposed method is a promising framework for the selection of farms according to the risk of infection based on animal movements.

No MeSH data available.


Related in: MedlinePlus

Illustration of a temporal network. a Three time steps (t1, t2, t3) in a schematic temporal network. In every time step, two movements between holdings take place. b the same network over the time period t1- t3. The network metrics ID, OD, ICC and OCC are calculated for every node in this network. c Table with the network metrics for every node in the temporal network. Note that paths can only be built from darker to lighter colours of the arcs
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Fig1: Illustration of a temporal network. a Three time steps (t1, t2, t3) in a schematic temporal network. In every time step, two movements between holdings take place. b the same network over the time period t1- t3. The network metrics ID, OD, ICC and OCC are calculated for every node in this network. c Table with the network metrics for every node in the temporal network. Note that paths can only be built from darker to lighter colours of the arcs

Mentions: Most of the traditional network metrics describe a static network considering all arcs to be permanent. However, in animal movement networks, arcs are only active over a short period of time and therefore, the sequence of movements is important to understand potential disease transmission patterns. Such temporal networks were subject of numerous recent studies [15–17]. A path in a temporal network between two premises exists only if all connecting movements are in a time sequence (see Fig. 1). By arranging contacts between premises in a chronological order, the temporal dimension of the network is accounted for. This allows backwards and forward tracing of potentially infected farms in case of an outbreak. To track potentially infected farms from a given source, the infection chain was proposed by Dubé et al. [8]. Nöremark et al. [9] refined this concept by introducing the ingoing contact chain to trace back potential sources of infection. The ingoing contact chain contains all possible paths onto a premise in a given time interval, taking the sequence by which the connecting movements occur into account. The ingoing contacts and corresponding contact chain have been shown to be relevant measures for the probability of disease detection in the final herd of destination [7, 18].Fig. 1


Evaluation of farm-level parameters derived from animal movements for use in risk-based surveillance programmes of cattle in Switzerland.

Schärrer S, Widgren S, Schwermer H, Lindberg A, Vidondo B, Zinsstag J, Reist M - BMC Vet. Res. (2015)

Illustration of a temporal network. a Three time steps (t1, t2, t3) in a schematic temporal network. In every time step, two movements between holdings take place. b the same network over the time period t1- t3. The network metrics ID, OD, ICC and OCC are calculated for every node in this network. c Table with the network metrics for every node in the temporal network. Note that paths can only be built from darker to lighter colours of the arcs
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4499910&req=5

Fig1: Illustration of a temporal network. a Three time steps (t1, t2, t3) in a schematic temporal network. In every time step, two movements between holdings take place. b the same network over the time period t1- t3. The network metrics ID, OD, ICC and OCC are calculated for every node in this network. c Table with the network metrics for every node in the temporal network. Note that paths can only be built from darker to lighter colours of the arcs
Mentions: Most of the traditional network metrics describe a static network considering all arcs to be permanent. However, in animal movement networks, arcs are only active over a short period of time and therefore, the sequence of movements is important to understand potential disease transmission patterns. Such temporal networks were subject of numerous recent studies [15–17]. A path in a temporal network between two premises exists only if all connecting movements are in a time sequence (see Fig. 1). By arranging contacts between premises in a chronological order, the temporal dimension of the network is accounted for. This allows backwards and forward tracing of potentially infected farms in case of an outbreak. To track potentially infected farms from a given source, the infection chain was proposed by Dubé et al. [8]. Nöremark et al. [9] refined this concept by introducing the ingoing contact chain to trace back potential sources of infection. The ingoing contact chain contains all possible paths onto a premise in a given time interval, taking the sequence by which the connecting movements occur into account. The ingoing contacts and corresponding contact chain have been shown to be relevant measures for the probability of disease detection in the final herd of destination [7, 18].Fig. 1

Bottom Line: Validation of the scores against results from the BVD surveillance programme 2013 gave promising results for setting the cut off for each of the five selected farm level criteria at the 50th percentile.Restricting testing to farms with a score ≥ 2 would have resulted in the same number of detected BVD positive farms as testing all farms, i.e., the outcome of the 2013 surveillance programme could have been reached with a smaller survey.The proposed method is a promising framework for the selection of farms according to the risk of infection based on animal movements.

View Article: PubMed Central - PubMed

Affiliation: Veterinary Public Health Institute (VPHI), Vetsuisse Faculty, University of Bern, Bern, Switzerland. sara.schaerrer@vetsuisse.unibe.ch.

ABSTRACT

Background: This study focused on the descriptive analysis of cattle movements and farm-level parameters derived from cattle movements, which are considered to be generically suitable for risk-based surveillance systems in Switzerland for diseases where animal movements constitute an important risk pathway.

Methods: A framework was developed to select farms for surveillance based on a risk score summarizing 5 parameters. The proposed framework was validated using data from the bovine viral diarrhoea (BVD) surveillance programme in 2013.

Results: A cumulative score was calculated per farm, including the following parameters; the maximum monthly ingoing contact chain (in 2012), the average number of animals per incoming movement, use of mixed alpine pastures and the number of weeks in 2012 a farm had movements registered. The final score for the farm depended on the distribution of the parameters. Different cut offs; 50, 90, 95 and 99%, were explored. The final scores ranged between 0 and 5. Validation of the scores against results from the BVD surveillance programme 2013 gave promising results for setting the cut off for each of the five selected farm level criteria at the 50th percentile. Restricting testing to farms with a score ≥ 2 would have resulted in the same number of detected BVD positive farms as testing all farms, i.e., the outcome of the 2013 surveillance programme could have been reached with a smaller survey.

Conclusions: The seasonality and time dependency of the activity of single farms in the networks requires a careful assessment of the actual time period included to determine farm level criteria. However, selecting farms in the sample for risk-based surveillance can be optimized with the proposed scoring system. The system was validated using data from the BVD eradication program. The proposed method is a promising framework for the selection of farms according to the risk of infection based on animal movements.

No MeSH data available.


Related in: MedlinePlus