Development of a microbial dose response visualization and modelling application for QMRA modelers and educators. (February 2017)

• Record Type: Journal Article
• Authors: Weir, Mark H.; Mitchell, Jade; Flynn, William; Pope, Joanna M.
• Is Part Of: Environmental modelling & software. Volume 88(2017)
• Page Start: 74
• Page End: 83
• Publication Date: 2017-02
• Subjects: QMRA -- Microbial dose response -- Optimization -- Software -- Wiki; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2016.11.011
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Microbial dose response modelling is vital to a well-characterized microbial risk estimate. Dose response modelling is an inherently multidisciplinary field, which collates knowledge and data from disparate scientific fields. This multidisciplinary nature presents a key challenge to the expansion of microbial dose response modelling into new groups of researchers and modelers. This research employs a dose response optimizationR code used in 18 peer-reviewed research studies to develop a multi-functional dose response software. The underlyingR code performs an optimization of the two primary dose response models using the MLE method and outputs statistical analyses of the fits and bootstrapped uncertainty information for the models. VizDR (Visual Dose Response) was developed to provide microbial dose response modelling capabilities to a larger audience. VizDR is programmed in JavaScript with underlying Python scripts for intercommunication with Rserve. VizDR allows for dose response model visualization and optimization of a user's own experimental data. Highlights: An algorithm was developed to interface with a dose response optimizationR code. TheR code was used in 18 separate peer-reviewed dose response modelling articles. VizDR will allow more researchers to learn and engage in QMRA modelling.