A new method for the calculation of centrality measure (Disease Flow Centrality, DFC), which takes into account temporal dynamics of livestock movement networks, is proposed. The method is based on a network traversal algorithm which represents an epidemic process more realistically compared with traditional graph traversal algorithms used in the calculation of centrality measures on static networks. The new approach was tested on networks generated from all the registered movements of cattle in Italy in the years 2007, 2008 and 2009 and the results were compared to those obtained by classical centrality measures. The results show that DFC values often differ substantially from those of other centrality measures and that these DFC values tend to be more unstable in time. The DFC offer several advantages for assessing risk and vulnerability of specific holdings and of an entire network, using recent movement data from national livestock databases. Some examples also indicate how the basic approach in the DFC calculation could be expanded into a more complex epidemic model by incorporating weights and how it could be combined with a geo-spatial perspective. ; JRC.G.7-Digital Citizen Security