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Construction of networks with intrinsic temporal structure from UK cattle movement data.

Heath MF, Vernon MC, Webb CR - BMC Vet. Res. (2008)

Bottom Line: However, this approach loses information on the time sequence of events thus reducing the accuracy of model predictions.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: University of Cambridge, Department of Veterinary Medicine, Madingley Road, Cambridge, UK. mfh2@cam.ac.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.

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The sixteen triad classes for the triad census. Triads that are not allowed by the logic of the networks generated here, are shown with pale blue nodes.
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Figure 7: The sixteen triad classes for the triad census. Triads that are not allowed by the logic of the networks generated here, are shown with pale blue nodes.

Mentions: Mutual dyads can only occur when reciprocal movements between two locations are made on the same day. Such events are rare, but logically occur in equal numbers in the two networks. The reciprocity of both the 7-day infection and 14-day infection networks is low (11.7 and 11.1 × 10-4, respectively). Similarly, in the triad census [9], 102 and 030C triads (see Fig. 7 for the structures of triad classes) are same-day events and have the same count in both networks. 030C triads are rare, and are the only triangles found, so the clustering coefficients are low (4.6 and 4.4 × 10-6, respectively). It is noteworthy that the triads 030T, 201, 120D, 120U, 120C, 210 and 300 are all forbidden by the rules for connecting nodes, and all yield zero counts in the partial triad censuses. These triads are shown with pale blue nodes in Fig. 7.


Construction of networks with intrinsic temporal structure from UK cattle movement data.

Heath MF, Vernon MC, Webb CR - BMC Vet. Res. (2008)

The sixteen triad classes for the triad census. Triads that are not allowed by the logic of the networks generated here, are shown with pale blue nodes.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2335102&req=5

Figure 7: The sixteen triad classes for the triad census. Triads that are not allowed by the logic of the networks generated here, are shown with pale blue nodes.
Mentions: Mutual dyads can only occur when reciprocal movements between two locations are made on the same day. Such events are rare, but logically occur in equal numbers in the two networks. The reciprocity of both the 7-day infection and 14-day infection networks is low (11.7 and 11.1 × 10-4, respectively). Similarly, in the triad census [9], 102 and 030C triads (see Fig. 7 for the structures of triad classes) are same-day events and have the same count in both networks. 030C triads are rare, and are the only triangles found, so the clustering coefficients are low (4.6 and 4.4 × 10-6, respectively). It is noteworthy that the triads 030T, 201, 120D, 120U, 120C, 210 and 300 are all forbidden by the rules for connecting nodes, and all yield zero counts in the partial triad censuses. These triads are shown with pale blue nodes in Fig. 7.

Bottom Line: However, this approach loses information on the time sequence of events thus reducing the accuracy of model predictions.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: University of Cambridge, Department of Veterinary Medicine, Madingley Road, Cambridge, UK. mfh2@cam.ac.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.

Show MeSH
Related in: MedlinePlus