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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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Maximum sizes of epidemic simulations on the 7-day infection network. An SIR epidemic model is run 1000 times from random start nodes on the 7-day infection network structure, and maximum number of currently infected nodes (for any of the simulations) at that step is plotted against the step number reached in the simulation.
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Figure 9: Maximum sizes of epidemic simulations on the 7-day infection network. An SIR epidemic model is run 1000 times from random start nodes on the 7-day infection network structure, and maximum number of currently infected nodes (for any of the simulations) at that step is plotted against the step number reached in the simulation.

Mentions: In the 7-day infection network, 100 steps of simulation represents from 99 to187 days (median 140) of real time. There are 3,086,787 movements available to infect. The output of the simulations is shown in Table 2. The cumulative maximum for q = 1 represents 27.3% of available nodes. Fig. 8 shows the rate of extinction of simulations for the 7-day infection network, and Fig. 9 shows the maximum size of the epidemic at each step (i.e. the highest number of currently infected movements at that step in any of the 1000 simulations).


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

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

Maximum sizes of epidemic simulations on the 7-day infection network. An SIR epidemic model is run 1000 times from random start nodes on the 7-day infection network structure, and maximum number of currently infected nodes (for any of the simulations) at that step is plotted against the step number reached in the simulation.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 9: Maximum sizes of epidemic simulations on the 7-day infection network. An SIR epidemic model is run 1000 times from random start nodes on the 7-day infection network structure, and maximum number of currently infected nodes (for any of the simulations) at that step is plotted against the step number reached in the simulation.
Mentions: In the 7-day infection network, 100 steps of simulation represents from 99 to187 days (median 140) of real time. There are 3,086,787 movements available to infect. The output of the simulations is shown in Table 2. The cumulative maximum for q = 1 represents 27.3% of available nodes. Fig. 8 shows the rate of extinction of simulations for the 7-day infection network, and Fig. 9 shows the maximum size of the epidemic at each step (i.e. the highest number of currently infected movements at that step in any of the 1000 simulations).

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