Network measures efficiency as predictors for disease transmission in spatial farm networks
(English)Manuscript (preprint) (Other academic)
Networks can be categorised using different measures of connectivity and topology of the network. We have examined if such network measures can be used as predictors of disease transmission in networks. In this study, virtual networks with a wide range of different structures are generated using the SpecNet algorithm. Measures are calculated for both the network as a whole and for individual nodes. The virtual networks generated a large variation in number of infected farms. In general, a large variation was still present for networks with equal value of a measure which implies that single network measures may not be sufficient as predictor for spread of disease. Yet, mean degree and the average clustering coefficient were the global network measures that could best explain the variation in the number of infected farms of a network. At the local level the degree and the clustering coefficient of the initially infected farm explain most of the variation in the number of infected farms. Hence, our results also points out that one should also consider the characteristics of the initially infected farm when predicting the spread of a disease.
IdentifiersURN: urn:nbn:se:liu:diva-76375OAI: oai:DiVA.org:liu-76375DiVA: diva2:514147