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dc.contributor.authorBussell, EH
dc.contributor.authorDangerfield, CE
dc.contributor.authorGilligan, CA
dc.contributor.authorCunniffe, NJ
dc.date.accessioned2019-01-25T00:30:14Z
dc.date.available2019-01-25T00:30:14Z
dc.date.issued2019-07-08
dc.identifier.issn0962-8436
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/288396
dc.description.abstractMathematical models provide a rational basis to inform how, where and when to control disease. Assuming an accurate spatially explicit simulation model can be fitted to spread data, it is straightforward to use it to test the performance of a range of management strategies. However, the typical complexity of simulation models and the vast set of possible controls mean that only a small subset of all possible strategies can ever be tested. An alternative approach-optimal control theory-allows the best control to be identified unambiguously. However, the complexity of the underpinning mathematics means that disease models used to identify this optimum must be very simple. We highlight two frameworks for bridging the gap between detailed epidemic simulations and optimal control theory: open-loop and model predictive control. Both these frameworks approximate a simulation model with a simpler model more amenable to mathematical analysis. Using an illustrative example model, we show the benefits of using feedback control, in which the approximation and control are updated as the epidemic progresses. Our work illustrates a new methodology to allow the insights of optimal control theory to inform practical disease management strategies, with the potential for application to diseases of humans, animals and plants. This article is part of the theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'. This theme issue is linked with the earlier issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'.
dc.format.mediumPrint
dc.languageeng
dc.publisherThe Royal Society
dc.subjectHumans
dc.subjectDisease Outbreaks
dc.subjectCommunicable Disease Control
dc.subjectModels, Biological
dc.subjectComputer Simulation
dc.titleApplying optimal control theory to complex epidemiological models to inform real-world disease management.
dc.typeArticle
prism.issueIdentifier1776
prism.publicationDate2019
prism.publicationNamePhilos Trans R Soc Lond B Biol Sci
prism.startingPage20180284
prism.volume374
dc.identifier.doi10.17863/CAM.35694
dcterms.dateAccepted2018-11-29
rioxxterms.versionofrecord10.1098/rstb.2018.0284
rioxxterms.versionAM
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2019-07
dc.contributor.orcidDangerfield, Ciara [0000-0002-0055-1270]
dc.contributor.orcidGilligan, Christopher [0000-0002-6845-0003]
dc.contributor.orcidCunniffe, Nik [0000-0002-3533-8672]
dc.identifier.eissn1471-2970
rioxxterms.typeJournal Article/Review
pubs.funder-project-idBiotechnology and Biological Sciences Research Council (1643594)
cam.issuedOnline2019-05-20
cam.orpheus.successThu Jan 30 10:53:18 GMT 2020 - Embargo updated
rioxxterms.freetoread.startdate2019-07-31


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