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Abstract Background The United Kingdom (UK) government has been recording the births, deaths, and movements of cattle for the last decade. Despite reservations about the accuracy of these data, they represent a large and valuable body of information about the demographics of the UK cattle herd and its contact structure. In this article, a range of demographic data about UK cattle, and particularly their movements, are presented, as well as yearly trends in the patterns of movements. Results A clear seasonal pattern is evident in the number of movements of cattle, as are the reductions in movement volume due to foot and mouth disease outbreaks in 2001 and 2007. The distribution of ages of cattle at their time of death is multimodal, and the impact of the over thirty months rule is marked. Most movements occur between agricultural holdings, markets, and slaughterhouses, and there is a non-random pattern to the types of holdings movements occur between. Most animals move only a short distance and a few times in their life. Most movements between any given pair of holdings only occurred once in the last 10 years, but about a third occurred between 2 and 10 times in that period. There is no clear trend to movement patterns in the UK since 2002. Conclusions Despite a substantial number of regulatory interventions during the last decade, movement patterns show no clear trend since 2002. The observed patterns in the repeatability of movements, the types of holdings involved in movements, the distances and frequencies of cattle movements, and the batch sizes involved give an insight into the structure of the UK cattle industry, and could act as the basis for a predictive model of livestock movements in the UK.

Abstract Background The implementation of national systems for recording the movements of cattle between agricultural holdings in the UK has enabled the development and parameterisation of network-based models for disease spread. These data can be used to form a network in which each cattle-holding location is represented by a single node and links between nodes are formed if there is a movement of cattle between them in the time period selected. However, this approach loses information on the time sequence of events thus reducing the accuracy of model predictions. In this paper, we propose an alternative way of structuring the data which retains information on the sequence of events but which still enables analysis of the structure of the network. The fundamental feature of this network is that nodes are not individual cattle-holding locations but are instead direct movements between pairs of locations. Links are made between nodes when the second node is a subsequent movement from the location that received the first movement. Results Two networks are constructed assuming (i) a 7-day and (ii) a 14-day infectious period using British Cattle Movement Service (BCMS) data from 2004 and 2005. During this time period there were 4,183,670 movements that could be derived from the database. In both networks over 98% of the connected nodes formed a single giant weak component. Degree distributions show scale-free behaviour over a limited range only, due to the heterogeneity of locations: farms, markets, shows, abattoirs. Simulation of the spread of disease across the networks demonstrates that this approach to restructuring the data enables efficient comparison of the impact of transmission rates on disease spread. Conclusion The redefinition of what constitutes a node has provided a means to simulate disease spread using all the information available in the BCMS database whilst providing a network that can be described analytically. This will enable the construction of generic networks with similar properties with which to assess the impact of small changes in network structure on disease dynamics.

The theory of networks has had a huge impact in both the physical and life sciences, shaping our understanding of the interaction between multiple elements in complex systems. In particular, networks have been extensively used in predicting the spread of infectious diseases where individuals, or populations of individuals, interact with a limited set of others-defining the network through which the disease can spread. Here for such disease models we consider three assumptions for capturing the network of movements between populations, and focus on two applied problems supported by detailed data from Great Britain: the commuter movement of workers between local areas (wards) and the permanent movement of cattle between farms. For such metapopulation networks, we show that the identity of individuals responsible for making network connections can have a significant impact on the infection dynamics, with clear implications for detailed public health and veterinary applications. [ABSTRACT FROM AUTHOR]