01/02/2018, 16:00 - 17:00 in MAP/0G/018
Watson, Emma (Queen’s University Belfast)
Bovine Tuberculosis (bTB) is one of the most extensively researched livestock diseases, yet continues to be difficult to manage due to its complex epidemiology and inherent uncertainty around the role of various factors. One of the most prominent risk factors is cattle movements as they can potentially move infected animals into susceptible herds. However, cattle trade is vital to the agricultural economy in Northern Ireland, thus there is a need to understand how these activities can affect the spread and maintenance of infectious diseases throughout the region.
In the context of cattle movements, social network analysis creates a network by representing farms as nodes connected by cattle movements which are represented as edges. The resulting networks were used to assess the trends of movements and were found to be Scale-Free. Due to this property the networks structures were changed to simulate movement based control measures. To simulate this, random or targeted nodes were removed from the network and the resulting networks were used to assess how well cattle trade could be fragmented. Observing how the network of movements would change under movement based control measures was useful as movement of infected animals is a risk factor of bTB spread. The changes in the networks as increasing numbers of nodes were removed were analysed by calculating the values of seven metrics: average path length, mean number of movements between node pairs, diameter, density, reciprocity, number of components, and the size of the largest component (GWCC: Giant Weakly Connected Component). These metrics will be discussed along with their utility in network analysis. Some results from this study will be presented and discussed to show how social network analysis may be routinely used for disease management in livestock.