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dc.contributor.authorHamede, Rodrigo
dc.contributor.authorBashford, Jim
dc.contributor.authorJones, Menna
dc.contributor.authorMcCallum, Hamish
dc.date.accessioned2017-05-03T15:37:40Z
dc.date.available2017-05-03T15:37:40Z
dc.date.issued2012
dc.date.modified2013-06-21T03:40:12Z
dc.identifier.issn0021-8901
dc.identifier.doi10.1111/j.1365-2664.2011.02103.x
dc.identifier.urihttp://hdl.handle.net/10072/51795
dc.description.abstract1. 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.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.languageEnglish
dc.language.isoeng
dc.publisherWiley-Blackwell Publishing
dc.publisher.placeUnited Kingdom
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofpagefrom447
dc.relation.ispartofpageto456
dc.relation.ispartofissue2
dc.relation.ispartofjournalJournal of Applied Ecology
dc.relation.ispartofvolume49
dc.rights.retentionY
dc.subject.fieldofresearchPopulation Ecology
dc.subject.fieldofresearchVeterinary Epidemiology
dc.subject.fieldofresearchEcological Applications not elsewhere classified
dc.subject.fieldofresearchEcological Applications
dc.subject.fieldofresearchEnvironmental Science and Management
dc.subject.fieldofresearchEcology
dc.subject.fieldofresearchcode060207
dc.subject.fieldofresearchcode070704
dc.subject.fieldofresearchcode050199
dc.subject.fieldofresearchcode0501
dc.subject.fieldofresearchcode0502
dc.subject.fieldofresearchcode0602
dc.titleSimulating devil facial tumour disease outbreaks across empirically derived contact networks
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.date.issued2012
gro.hasfulltextNo Full Text
gro.griffith.authorMcCallum, Hamish


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