| Sumario: | Farm animal movement usually occurs during grazing, watering, and dipping and livestock trade among others. These movements creates contacts between different farms and the contacts created forms networks of varying magnitude ranging from local to international depending on the scale of movement of animals. The networks are dynamic in nature and are mostly influenced by season which determines the availability of pastures and water. Contacts created acts as a route through which disease pathogens may spread between farms in various geographical locations. Farms which are infected acts as source of infection to other farms when there is movement of animals from the infected farms to non-infected farms.
Limited studies have been done in contact networks between herds to give a better understanding of the spread of infectious pathogens in Africa. Most predictive models for farm animal diseases focus on gathering information and estimating transmission parameters with little attention on modeling the underlying network of contacts. These models also ignore the complex structures present at different levels including between animals, farms and the regions. Lack of accurate information on contact structures and the factors affecting them is also a challenge when complex structures are considered. Therefore understanding of the contact networks between farms is critical in instituting proper surveillance and control measures. The overall objective of the study was to determine and assess the types of contact networks between herds and the potential for transmission of diseases. The specific objectives were to determine the existing contact networks between cattle herds, to determine the frequency of the contact within the network between farms and to determine the factors affecting heterogeneity in contact in the study area.
The study area was in Kimilili Sub-county of Bungoma County and it was a cross sectional study involving seven villages randomly sampled. A census of all cattle keeping households in the sampled villages was undertaken. Farm contacts within these villages of Bungoma County were explored using contact structure interview which involved a combination of photo-elicitation, structured questionnaire and a contact collection form.
In total 329 farms were included in the study, the farm contacts as a result of sharing grazing fields, water points for the four weeks during the wet season (October-December) together with breeding and ploughing contacts for the last one year of the study was reconstructed and analyzed for the seven villages. The overall farm networks for the various farm contacts in the villages were also visualized.
There was significant difference in the proportion of farmers that took their animals for grazing outside their farms daily during dry and rainy season (p=0.0000, z-test=0.652) and the proportion of farms that took their livestock for water daily outside their homestead during dry and rainy seasons was also significantly different (p=0.006, z-test=2.75). The overall farm contacts and the distances among the farms were negatively correlated (p=0.0000, Pearson correlation coefficient range -0.2 to -0.4). Contact of cattle at common water points was significantly influenced by extensive grazing management practice in the farm (p=0.0000, OR=9.57), number of animals kept in the farm (p=0.0212, OR=1.23) and presence of cross breeds in the farm (p=0.0044, OR=0.37). The networks in the villages resembled undirected scale free graphs with a normalized degree range 9.6 – 14.0 and the topology of the networks were heterogeneous with some farms exhibited high degree of contacts compared to others.
An introduction of same disease pathogen in the villages would lead to increased rate of spread of the disease in Chebukwabi, Lutonyi and Kibunde due to higher values of coefficient of variation which is a determinant of basic reproduction number (Ro) in the networks. Studies should be conducted to describe contacts during dry season to give a better understanding of the structure of the networks to inform surveillance and control strategies.
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