Simulating devil facial tumour disease outbreaks across empirically derived contact networks

  1. Rodrigo Hamede1,*,
  2. Jim Bashford1,
  3. Menna Jones1,
  4. Hamish McCallum2

Article first published online: 23 JAN 2012

DOI: 10.1111/j.1365-2664.2011.02103.x

Additional Information

How to Cite

Hamede, R., Bashford, J., Jones, M. and McCallum, H. (2012), Simulating devil facial tumour disease outbreaks across empirically derived contact networks. Journal of Applied Ecology. doi: 10.1111/j.1365-2664.2011.02103.x

Author Information

  1. 1

    School of Zoology, University of Tasmania, Private Bag 5 Hobart 7001 Tasmania Australia

  2. 2

    School of Environment, Griffith University, 170 Kessels Rd Nathan 4111 Queensland Australia

* Correspondence author. E-mail: rkhamede@utas.edu.au

Publication History

  1. Article first published online: 23 JAN 2012
  2. Received 6 July 2011; accepted 1 December 2011 Handling Editor: Eric Morgan

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Keywords:

  • contact heterogeneity;
  • contact networks;
  • epidemiological models;
  • extinction;
  • network model;
  • R0;
  • tasmanian devil facial tumour disease;
  • transmission;
  • wildlife disease

Summary

1. Understanding the nature and characteristics of contact heterogeneities is crucial for predicting the epidemic behaviour of infectious diseases. Nonetheless, few studies include contact heterogeneities when modelling disease outbreaks in wildlife, which differ in their population impact from human diseases.

2. We use empirical estimates of contact heterogeneities and network metrics to simulate outbreaks of devil facial tumour disease (DFTD), an extinction-threatening infectious cancer. We incorporate tuneable algorithms, with a range of transmission rates and latent periods of DFTD, to grow devil population networks capable of reproducing observed aspects of devil ecology, demographic and seasonal-based mixing preferences. The outputs of the network model are compared with a stochastic mean-field model, in which every individual is equally likely to pass or acquire infection through time.

3. Our network model predicts a lower epidemic threshold for DFTD compared with the stochastic mean-field model. While host extinction probabilities are similar in both models, the network model predicts faster devil extinction and higher DFTD extinction probabilities, particularly for intermediate levels of transmissibility.

4. While the time taken to devil extinction increases with the longer estimate of latent period, probabilities of both, disease and devil extinction, are greater with the shorter latent period. Host–pathogen coexistence is strictly subject to the longest plausible estimate of latent period and low transmissibility.

5.Synthesis and applications. In the particular case of DFTD, incorporating observed host network structure has only a modest effect on the outcome of the host pathogen interaction. In general, however, non-random network structure may have major implications for the management of wildlife diseases. Our results suggest that this is particularly likely for pathogens in which the probability of transmission given a contact is intermediate. Our approach provides a template for using empirically obtained data on contact networks to develop models to explore the extent to which network structure influences R0, the probability of extinction and the mean time until extinction.

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