Broadwick: a framework for computational epidemiology

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 M...

• Type: Journal
• Year: 2016
• Where: BMCBI
• Authors: Anthony O'Hare, Samantha Lycett, Thomas Doherty, Liliana C. M. Salvador, Rowland R. Kao
• Keywords: BMCBI 2016