Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/22844
Appears in Collections:Aquaculture Journal Articles
Peer Review Status: Refereed
Title: Broadwick: a framework for computational epidemiology
Authors: O'Hare, Anthony
Lycett, Samantha J
Doherty, Thomas
Salvador, Liliana C M
Kao, Rowland R
Contact Email: anthony.ohare@stir.ac.uk
Keywords: Epidemiology
Modelling
Framework
Modularity
Issue Date: 4-Feb-2016
Publisher: BioMed Central
Citation: O'Hare A, Lycett SJ, Doherty T, Salvador LCM & Kao RR (2016) Broadwick: a framework for computational epidemiology, BMC Bioinformatics, 17 (1), Art. No.: 65.
Abstract: Background  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.
Type: Journal Article
URI: http://hdl.handle.net/1893/22844
DOI Link: http://dx.doi.org/10.1186/s12859-016-0903-2
Rights: This 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.
Affiliation: Complex Systems
University of Glasgow
University of Glasgow
University of Glasgow
University of Glasgow

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