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dc.contributor.authorBussell, Elliott H
dc.contributor.authorCunniffe, Nik
dc.date.accessioned2022-01-28T14:38:20Z
dc.date.available2022-01-28T14:38:20Z
dc.date.issued2022-01
dc.date.submitted2021-09-10
dc.identifier.issn1742-5689
dc.identifier.otherrsif20210718
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/332996
dc.description.abstractEpidemics can particularly threaten certain sub-populations. For example, for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the elderly are often preferentially protected. For diseases of plants and animals, certain sub-populations can drive mitigation because they are intrinsically more valuable for ecological, economic, socio-cultural or political reasons. Here, we use optimal control theory to identify strategies to optimally protect a 'high-value' sub-population when there is a limited budget and epidemiological uncertainty. We use protection of the Redwood National Park in California in the face of the large ongoing state-wide epidemic of sudden oak death (caused by Phytophthora ramorum) as a case study. We concentrate on whether control should be focused entirely within the National Park itself, or whether treatment of the growing epidemic in the surrounding 'buffer region' can instead be more profitable. We find that, depending on rates of infection and the size of the ongoing epidemic, focusing control on the high-value region is often optimal. However, priority should sometimes switch from the buffer region to the high-value region only as the local outbreak grows. We characterize how the timing of any switch depends on epidemiological and logistic parameters, and test robustness to systematic misspecification of these factors due to imperfect prior knowledge.
dc.languageen
dc.publisherThe Royal Society
dc.subjectLife Sciences–Mathematics interface
dc.subjectResearch articles
dc.subjectoptimal control theory
dc.subjectsudden oak death
dc.subjectPhytophthora ramorum
dc.subjectpolicy plot
dc.subjectbuffer region
dc.subjectparameter uncertainty
dc.titleOptimal strategies to protect a sub-population at risk due to an established epidemic.
dc.typeArticle
dc.date.updated2022-01-28T14:38:19Z
prism.issueIdentifier186
prism.publicationNameJ R Soc Interface
prism.volume19
dc.identifier.doi10.17863/CAM.80420
dcterms.dateAccepted2021-12-16
rioxxterms.versionofrecord10.1098/rsif.2021.0718
rioxxterms.versionAO
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/
dc.contributor.orcidCunniffe, Nik [0000-0002-3533-8672]
dc.identifier.eissn1742-5662
pubs.funder-project-idBiotechnology and Biological Sciences Research Council (1643594)
cam.issuedOnline2022-01-12


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