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Use of social network analysis to improve the understanding of social behaviour in dairy cattle and its impact on disease transmission

By Inès de Freslon, Beatriz Martínez-López, Jaber Belkhiria, Ana Strappini, Gustavo Monti

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A better comprehension of cattle contact structure can enhance the prevention of the transmission of infectious agents within livestock farms. Social network analysis has proven to provide a more accurate picture of social structures than traditional methods. In this study, we focused on leptospirosis, a zoonosis of global importance caused by pathogenic strains of Leptospira spp. that can be transmitted directly between animals. We hypothesized that contact patterns between dairy cattle of the same group are influenced by individual cow attributes and structural properties of the social network. We worked with a milking cow group (n = 170) and two weaned calf groups of different ages (both n = 33) kept in pasture-based systems. We focused on three contact behaviours that may lead to transmission of pathogenic Leptospira spp.: sniffing, licking and rubbing the face on the genital area of another animal. The occurrence of these behaviours was directly observed and recorded for three weeks in lactating cows and four weeks in weaned calves. Based on those observations, we created social networks and used exponential random graph models to estimate the probability of contact between the animals based on individual covariates (cows: parity number, age, reproductive status, and entrance time into the group; calves: sex, age and entrance time) and structural effects. Despite most of the individuals in each group being either directly or indirectly connected, networks were extremely sparse. Most animals were involved in few contacts; however, some individuals had a very high degree of interaction (mainly cows in oestrus and male calves). Those highly connected individuals could play a key role during outbreaks. There was negative age heterophily (OR = 0.92, p 

Publication Title Applied Animal Behaviour Science
Volume 213
Pages 47-54
ISBN/ISSN 0168-1591
DOI https://doi.org/10.1016/j.applanim.2019.01.006
Cite this work

Researchers should cite this work as follows:

  1. Bacteria
  2. Cattle
  3. models
  4. networks
  5. Sexual behavior