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dc.contributor.authorCunniffe, Niken
dc.contributor.authorLaranjeira, FFen
dc.contributor.authorNeri, Francoen
dc.contributor.authorDeSimone, Ralphen
dc.contributor.authorGilligan, Christopheren
dc.date.accessioned2016-12-01T12:06:45Z
dc.date.available2016-12-01T12:06:45Z
dc.date.issued2014-08-07en
dc.identifier.issn1553-734X
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/261378
dc.description.abstractA spatially-explicit, stochastic model is developed for Bahia bark scaling, a threat to citrus production in north-eastern Brazil, and is used to assess epidemiological principles underlying the cost-effectiveness of disease control strategies. The model is fitted via Markov chain Monte Carlo with data augmentation to snapshots of disease spread derived from a previously-reported multi-year experiment. Goodness-of-fit tests strongly supported the fit of the model, even though the detailed etiology of the disease is unknown and was not explicitly included in the model. Key epidemiological parameters including the infection rate, incubation period and scale of dispersal are estimated from the spread data. This allows us to scale-up the experimental results to predict the effect of the level of initial inoculum on disease progression in a typically-sized citrus grove. The efficacies of two cultural control measures are assessed: altering the spacing of host plants, and roguing symptomatic trees. Reducing planting density can slow disease spread significantly if the distance between hosts is sufficiently large. However, low density groves have fewer plants per hectare. The optimum density of productive plants is therefore recovered at an intermediate host spacing. Roguing, even when detection of symptomatic plants is imperfect, can lead to very effective control. However, scouting for disease symptoms incurs a cost. We use the model to balance the cost of scouting against the number of plants lost to disease, and show how to determine a roguing schedule that optimises profit. The trade-offs underlying the two optima we identify-the optimal host spacing and the optimal roguing schedule-are applicable to many pathosystems. Our work demonstrates how a carefully parameterised mathematical model can be used to find these optima. It also illustrates how mathematical models can be used in even this most challenging of situations in which the underlying epidemiology is ill-understood.
dc.description.sponsorshipFFL was funded via a CNPq Fellowship (Brazil's National Council for Scientific and Technological Development, see http://memoria.cnpq.br/english/cnpq/index.htm). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
dc.languageENGen
dc.language.isoenen
dc.publisherPublic Library of Science (PLoS)
dc.rightsAttribution 4.0 Internationalen
dc.rightsAttribution 4.0 Internationalen
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectCitrusen
dc.subjectComputational Biologyen
dc.subjectMarkov Chainsen
dc.subjectModels, Biologicalen
dc.subjectModels, Statisticalen
dc.subjectMonte Carlo Methoden
dc.subjectPlant Diseasesen
dc.titleCost-effective control of plant disease when epidemiological knowledge is incomplete: modelling Bahia bark scaling of citrus.en
dc.typeArticle
prism.issueIdentifier8en
prism.numbere1003753en
prism.publicationDate2014en
prism.publicationNamePLoS Computational Biologyen
prism.volume10en
dc.identifier.doi10.17863/CAM.6545
dcterms.dateAccepted2014-06-11en
rioxxterms.versionofrecord10.1371/journal.pcbi.1003753en
rioxxterms.versionVoRen
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/en
rioxxterms.licenseref.startdate2014-08-07en
dc.contributor.orcidCunniffe, Nik [0000-0002-3533-8672]
dc.contributor.orcidGilligan, Christopher [0000-0002-6845-0003]
dc.identifier.eissn1553-7358
rioxxterms.typeJournal Article/Reviewen
cam.issuedOnline2014-08-07en
datacite.issupplementedby.doi10.1371/journal.pcbi.1003753.s001en


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International