Abstract
Determining the Bovine Viral Diarrhoea (BVD) infection status of cattle herds is a challenge for control and eradication schemes. Given the changing dynamics of BVD virus (BVDV) antibody responses in cattle, classifying herds based on longitudinal changes in the results of BVDV antibody tests could offer a novel, complementary approach to categorising herds that is less likely than the present system to result in a herd’s status changing from year to year, as it is more likely to capture the true exposure dynamics of the farms. This paper describes the dynamics of BVDV antibody test values (measured as percentage positivity (PP)) obtained from 15,500 bovines between 2007 and 2010 from thirty nine cattle herds located in Scotland and Northern England. It explores approaches of classifying herds based on trend, magnitude and shape of their antibody PP trajectories and investigates the epidemiological similarities between farms within the same cluster. Gaussian mixture models were used for the magnitude and shape clustering. Epidemiologically meaningful clusters were obtained. Farm cluster membership depends on clustering approach used. Moderate concordance was found between the shape and magnitude clusters. These methods hold potential for application to enhance control efforts for BVD and other infectious livestock diseases.
Highlights
Bovine viral diarrhoea (BVD) is an economically important disease of cattle caused by a pestivirus[1]
Given that BVD virus (BVDV) antibodies are dynamic due to their response to exposure factors, it is likely that cluster memberships obtained from a single cross-sectional antibody test may change if clustering is repeated after another test in the near future
The dynamics in the levels of BVDV antibodies, when measured over time, can mean that the Bovine Viral Diarrhoea (BVD) status is likely to change as antibody levels change if herd status is based on BVDV antibodies alone
Summary
Bovine viral diarrhoea (BVD) is an economically important disease of cattle caused by a pestivirus[1]. The test results were presented as percentage positivity (PP) values, calculated from the optical density (OD) values of the samples in comparison to positive and negative control samples[8,9] We used these PP values as proxy measures of BVDV antibody levels in this paper. Patterns of farm antibody trajectories or patterns of changes of farm BVD status over time may be important in monitoring impact of control measures adopted to eradicate BVD. Clustering these patterns may reveal a group of problem farms where targeted control efforts and resources should be directed. The beauty of the approaches used in our paper is their ability to model dependence of the model mixture parameters on time and this dependence may be linear or nonlinear[13,21,22]
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