Past research has demonstrated that social networks and community structures have a strong effecton the dynamics of how an infectious disease spreads, although many have focused moreintensively on the impact of the number of connections an individual has. This thesis investigatedhow disease transmission is affected by the social networks’ structures such as communities,centrality measures, cliques, and diameter. For this purpose, a farm located in Sweden with around200 cows will be used and two social networks are were built based on spatial data from a periodof 24h. A hypothetical model was built to simulate disease transmission with two scenarios: onewhen there was immunity and another without immunity. The main hypothesis presented here isthat if the focus of infections starts on an individual located in the largest community, it would resultin faster and larger spread of the disease, the betweenness centrality was used in thesecommunities to choose the individual to start the focus of infection. It is also investigated whetherthere was any preferential aggregation due to parity, which is related to the number of calves a cowhas had. It was found that the size of the community did not have a high influence in the rate ofinfections, meanwhile the overall centrality in the whole networks and the presence of certainindividuals in the cliques seemed to play a bigger role. The results indicated some preferentialrelationship due to parity, although it is unclear if there is a specific parity that tends to aggregatemore than others.The two networks had different structures, making it difficult to generalize results and make arecommendation to farmers