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

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.

Fig. 1

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- t_3. 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

Fig. 2

The January cohort followed over one year. The proportion expresses how many cattle were still in the same herd on the 1st of every month in 2012. Over the summer month, the proportion of animals leaving the herd increases because entire herds are moved to summer pasture. The increase of bovines originally in the herd in October is due to cattle returning from summer pasture

Fig. 3

Maximum ID, OD, ICC and OCC for the different holding types in the Swiss cattle trade network in 2012

Fig. 4

Probability density functions of the farm level criteria considered (ID_y, ICC_max, NS, avAN, AW). Data from 2012 in Switzerland is presented. The applied thresholds are shown as vertical lines: skyblue: 50 % quantile; green: 90 % quantile; red: 95 % quantile; grey: 99 % quantile

Fig. 5

Proportion of farms with the same score count for different thresholds. Blue: farms that never had a suspicious BVD result since the beginning of the eradication programme; black: farms in the BVD surveillance programme 2013 and at least one PI