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Livestock premises and disease spread through the trade network of Espírito Santo, Brazil

Grant number: 18/17762-4
Support type:Scholarships abroad - Research Internship - Doctorate
Effective date (Start): January 10, 2019
Effective date (End): July 09, 2019
Field of knowledge:Health Sciences - Collective Health - Epidemiology
Principal researcher:José Henrique de Hildebrand e Grisi Filho
Grantee:Jason Onell Ardila Galvis
Supervisor abroad: Rowland Raymond Kao
Home Institution: Faculdade de Medicina Veterinária e Zootecnia (FMVZ). Universidade de São Paulo (USP). São Paulo , SP, Brazil
Research place: University of Edinburgh, Scotland  
Associated to the scholarship:16/03441-6 - Spatial analysis of the bovine tuberculosis and animal movement network in the state of Espírito Santo, Brazil., BP.DR


Livestock production in Brazil has a high economic importance for the country. Therefore, emerging or unknown diseases outbreaks and spread must be avoided by the Brazilian surveillance system in the national territory. The animal trade plays an important role in the transmission of pathogens between premises. For this reason, it is necessary to study the characteristics of the animal network to identify both the premises with higher importance in the transmission and the premises where the surveillance can act efficiently. The aim of this study will be classify the premises of the bovine trade network in risk groups and identify which of them plays an important role in the disease spread. This study will use the database of bovine movements from Espírito Santo State (ES) in Brazil, between 2014 and 2015. The general structure of the network will be described using the parameters of size, cohesion and centrality and the premises will be classified in five groups accordingly to the network components. The importance of premises and groups to spread a disease will be evaluated using the number of premises reachable by each one of the premises in the network. Finally, to study a hypothetical interference in the transmission of a disease, we will disaggregate the network, removing progressively the premises with the highest importance according to its measures of centrality. This study finding will provide information to the Brazilian surveillance system about which premises must be monitored to prevent and control the spread of an infectious disease. (AU)

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