Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/22844
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dc.contributor.authorO'Hare, Anthonyen_UK
dc.contributor.authorLycett, Samantha Jen_UK
dc.contributor.authorDoherty, Thomasen_UK
dc.contributor.authorSalvador, Liliana C Men_UK
dc.contributor.authorKao, Rowland Ren_UK
dc.date.accessioned2016-02-27T02:22:14Z-
dc.date.available2016-02-27T02:22:14Z-
dc.date.issued2016-02-04en_UK
dc.identifier.other65en_UK
dc.identifier.urihttp://hdl.handle.net/1893/22844-
dc.description.abstractBackground  Modelling disease outbreaks often involves integrating the wealth of data that are gathered during modern outbreaks into complex mathematical or computational models of transmission. Incorporating these data into simple compartmental epidemiological models is often challenging, requiring the use of more complex but also more efficient computational models. In this paper we introduce a new framework that allows for a more systematic and user-friendly way of building and running epidemiological models that efficiently handles disease data and reduces much of the boilerplate code that usually associated to these models. We introduce the framework by developing an SIR model on a simple network as an example.  Results  We develop Broadwick, a modular, object-oriented epidemiological framework that efficiently handles large epidemiological datasets and provides packages for stochastic simulations, parameter inference using Approximate Bayesian Computation (ABC) and Markov Chain Monte Carlo (MCMC) methods. Each algorithm used is fully customisable with sensible defaults that are easily overridden by custom algorithms as required.  Conclusion  Broadwick is an epidemiological modelling framework developed to increase the productivity of researchers by providing a common framework with which to develop and share complex models. It will appeal to research team leaders as it allows for models to be created prior to a disease outbreak and has the ability to handle large datasets commonly found in epidemiological modelling.en_UK
dc.language.isoenen_UK
dc.publisherBioMed Centralen_UK
dc.relationO'Hare A, Lycett SJ, Doherty T, Salvador LCM & Kao RR (2016) Broadwick: a framework for computational epidemiology, BMC Bioinformatics, 17 (1), Art. No.: 65. https://doi.org/10.1186/s12859-016-0903-2.en_UK
dc.rightsThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://​creativecommons.​org/​licenses/​by/​4.​0/​), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://​creativecommons.​org/​publicdomain/​zero/​1.​0/​) applies to the data made available in this article, unless otherwise stated.en_UK
dc.subjectEpidemiologyen_UK
dc.subjectModellingen_UK
dc.subjectFrameworken_UK
dc.subjectModularityen_UK
dc.titleBroadwick: a framework for computational epidemiologyen_UK
dc.typeJournal Articleen_UK
dc.identifier.doi10.1186/s12859-016-0903-2en_UK
dc.identifier.pmid26846686en_UK
dc.citation.jtitleBMC Bioinformaticsen_UK
dc.citation.issn1471-2105en_UK
dc.citation.volume17en_UK
dc.citation.issue1en_UK
dc.citation.publicationstatusPublisheden_UK
dc.citation.peerreviewedRefereeden_UK
dc.type.statusVoR - Version of Recorden_UK
dc.author.emailanthony.ohare@stir.ac.uken_UK
dc.citation.date04/02/2016en_UK
dc.contributor.affiliationComplex Systems - LEGACYen_UK
dc.contributor.affiliationUniversity of Glasgowen_UK
dc.contributor.affiliationUniversity of Glasgowen_UK
dc.contributor.affiliationUniversity of Glasgowen_UK
dc.contributor.affiliationUniversity of Glasgowen_UK
dc.identifier.isi000369583800003en_UK
dc.identifier.scopusid2-s2.0-84956855271en_UK
dc.identifier.wtid579233en_UK
dc.contributor.orcid0000-0003-2561-9582en_UK
dc.date.accepted2016-01-21en_UK
dc.date.firstcompliantdepositdate2016-02-17en_UK
dc.description.refREF Compliant by Deposit in Stirling's Repositoryen_UK
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