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

被引:26
作者
de Freslon, Ines [1 ]
Martinez-Lopez, Beatriz [2 ]
Belkhiria, Jaber [2 ]
Strappini, Ana [3 ]
Monti, Gustavo [1 ]
机构
[1] Univ Austral Chile, Fac Vet Sci, Prevent Vet Med Dept, Valdivia, Chile
[2] Univ Calif Davis, Ctr Anim Dis Modeling & Surveillance, Dept Med & Epidemiol, Sch Vet Med, Davis, CA 95616 USA
[3] Univ Austral Chile, Fac Vet Sci, Anim Sci Dept, Valdivia, Chile
关键词
Contact network; Dairy cattle; Sexual behaviour; Exponential random graph models; Leptospirosis; ESTRUS; HERD; CONSEQUENCES; MANAGEMENT; INFECTION; DOMINANCE;
D O I
10.1016/j.applanim.2019.01.006
中图分类号
S8 [畜牧、 动物医学、狩猎、蚕、蜂];
学科分类号
0905 ;
摘要
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 < 0.001), meaning that cows interacted mainly with cows of the same age. Male calves were significantly more likely to start contacts than females (CALF1 group: OR = 3.79, p < 0.001; CALF2 group: OR = 7.71, p < 0.001). This study provides evidence that social interactions in dairy cattle are heterogeneous and highlights the importance of specific individual attributes in the formation of the contact structure of a group. Considering the contact structure within groups might facilitate the design of more efficient surveillance systems and mitigation strategies to prevent or reduce the transmission of infectious agents in dairy farms.
引用
收藏
页码:47 / 54
页数:8
相关论文
共 51 条
[1]   Leprospira and leptospirosis [J].
Adler, Ben ;
de la Pena Moctezuma, Alejandro .
VETERINARY MICROBIOLOGY, 2010, 140 (3-4) :287-296
[2]  
[Anonymous], 2016, R LANGUAGE ENV STAT
[3]  
[Anonymous], 2013, STRUCTURAL ANAL SOCI
[4]  
Bastian M., 2009, DBLP 3 INT C WEBL SO, DOI [10.13140/2.1.1341.1520, DOI 10.13140/2.1.1341.1520, DOI 10.1609/ICWSM.V3I1.13937]
[5]   Contact Networks in a Wildlife-Livestock Host Community: Identifying High-Risk Individuals in the Transmission of Bovine TB among Badgers and Cattle [J].
Boehm, Monika ;
Hutchings, Michael R. ;
White, Piran C. L. .
PLOS ONE, 2009, 4 (04)
[6]   ESTABLISHMENT OF PREFERENTIAL RELATIONS IN HERD OF CATTLE [J].
BOUISSOU, MF ;
ANDRIEU, S .
BEHAVIOUR, 1978, 64 :148-157
[7]   Spatial proximity loggers for recording animal social networks: consequences of inter-logger variation in performance [J].
Boyland, N. K. ;
James, R. ;
Mlynski, D. T. ;
Madden, J. R. ;
Croft, D. P. .
BEHAVIORAL ECOLOGY AND SOCIOBIOLOGY, 2013, 67 (11) :1877-1890
[8]   The social network structure of a dynamic group of dairy cows: From individual to group level patterns [J].
Boyland, Natasha K. ;
Mlynski, David T. ;
James, Richard ;
Brent, Lauren J. N. ;
Croft, Darren P. .
APPLIED ANIMAL BEHAVIOUR SCIENCE, 2016, 174 :1-10
[9]   Spatial-Temporal Dynamics of High-Resolution Animal Networks: What Can We Learn from Domestic Animals? [J].
Chen, Shi ;
Ilany, Amiyaal ;
White, Brad J. ;
Sanderson, Michael W. ;
Lanzas, Cristina .
PLOS ONE, 2015, 10 (06)
[10]   Infection in social networks: Using network analysis to identify high-risk individuals [J].
Christley, RM ;
Pinchbeck, GL ;
Bowers, RG ;
Clancy, D ;
French, NP ;
Bennett, R ;
Turner, J .
AMERICAN JOURNAL OF EPIDEMIOLOGY, 2005, 162 (10) :1024-1031