title: Broadwick: a framework for computational epidemiology.
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.
  
  
  CONCLUSIONS
  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.
authors: Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, UK. anthony.ohare@stir.ac.uk.|School of Natural Sciences, University of Stirling, Stirling, UK. anthony.ohare@stir.ac.uk.
authors: Anthony
authors: A
authors: O'Hare
authors: Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, UK. samantha.lycett@ed.ac.uk.|The Roslin Institute, University of Edinburgh, Easter Bush, Midlothian. EH25 9RG, UK. samantha.lycett@ed.ac.uk.
authors: Samantha J
authors: SJ
authors: Lycett
authors: Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, UK. Tom.doherty@glasgow.ac.uk.
authors: Thomas
authors: T
authors: Doherty
authors: Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, UK. liliana.salvador@glasgow.ac.uk.
authors: Liliana C M
authors: LC
authors: Salvador
authors: Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, UK. rowland.kao@glasgow.ac.uk.
authors: Rowland R
authors: RR
authors: Kao
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D003198:Computer Simulation; D016021:Epidemiologic Studies; D005828:Genetics, Population; D006801:Humans; D008390:Markov Chains; D008962:Models, Theoretical; D009010:Monte Carlo Method