Spatial analyses of wildlife contact networks

Stephen Davis, Babak Abbasi, Shrupa Shah, Sandra Telfer, Mike Begon

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45 Citations (Scopus)
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Datasets from which wildlife contact networks of epidemiological importance can be inferred are becoming increasingly common. A largely unexplored facet of these data is finding evidence of spatial constraints on who has contact with whom, despite theoretical epidemiologists having long realized spatial constraints can play a critical role in infectious disease dynamics. A graph dissimilarity measure is proposed to quantify how close an observed contact network is to being purely spatial whereby its edges are completely determined by the spatial arrangement of its nodes. Statistical techniques are also used to fit a series of mechanistic models for contact rates between individuals to the binary edge data representing presence or absence of observed contact. These are the basis for a second measure that quantifies the extent to which contacts are being mediated by distance. We apply these methods to a set of 128 contact networks of field voles (Microtus agrestis) inferred from mark-recapture data collected over 7 years and from four sites. Large fluctuations in vole abundance allow us to demonstrate that the networks become increasingly similar to spatial proximity graphs as vole density increases. The average number of contacts, 〈k〉, was (i) positively correlated with vole density across the range of observed densities and (ii) for two of the four sites a saturating function of density. The implications for pathogen persistence in wildlife may be that persistence is relatively unaffected by fluctuations in host density because at low density 〈k〉 is low but hosts move more freely, and at high density 〈k〉 is high but transmission is hampered by local build-up of infected or recovered animals.

Original languageEnglish
Pages (from-to)1-11
Number of pages11
JournalJournal of the Royal Society Interface
Issue number102
Early online date19 Nov 2014
Publication statusPublished - 6 Jan 2015

Bibliographical note

We thank Kathy Horadam for useful discussions about the graph dissimilarity measures and Meggan Craft for reading an early draft of the manuscript and for her helpful advice and encouragement.


  • dissimilarity measure
  • epidemiology
  • field vole
  • graph
  • mathematical model
  • Microtus agrestis


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