Weir Mark H, Mitchell Jade, Flynn William, Pope Joanna M
Division of Environmental Health Sciences, College of Public Health, The Ohio State University, 426 Cunz Hall, 1841, Neil Ave, Columbus, OH, 43210, USA.
Department of Civil Environmental and Geodetic Engineering, College of Engineering, The Ohio State University, 2070 Neil Ave., Columbus, OH, 43210, USA.
Environ Model Softw. 2017 Feb;88:74-83. doi: 10.1016/j.envsoft.2016.11.011. Epub 2016 Nov 24.
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 optimization code used in 18 peer-reviewed research studies to develop a multi-functional dose response software. The underlying 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.
微生物剂量反应建模对于精确的微生物风险评估至关重要。剂量反应建模是一个本质上跨学科的领域，它整合了来自不同科学领域的知识和数据。这种跨学科性质对将微生物剂量反应建模扩展到新的研究人员和建模人员群体构成了关键挑战。本研究采用了在18项同行评审研究中使用的剂量反应优化代码来开发多功能剂量反应软件。底层代码使用最大似然估计（MLE）方法对两个主要的剂量反应模型进行优化，并输出模型拟合的统计分析和自抽样不确定性信息。开发了VizDR（可视化剂量反应）以向更广泛的受众提供微生物剂量反应建模功能。VizDR用JavaScript编写，带有用于与Rserve进行交互通信的底层Python脚本。VizDR允许对剂量反应模型进行可视化，并对用户自己的实验数据进行优化。
