Development of a microbial dose response visualization and modelling application for QMRA modelers and educators.

Area of Science:

• Microbiology
• Bioinformatics
• Computational Biology

Background:

• Microbial dose response modeling is crucial for accurate microbial risk assessment.
• The multidisciplinary nature of this field presents a barrier to broader researcher engagement.
• Existing R code for dose response optimization is complex and not widely accessible.

Purpose of the Study:

• To develop a multi-functional, user-friendly software for microbial dose response modeling.
• To expand access to microbial dose response modeling tools for a wider audience.
• To facilitate the visualization and optimization of experimental dose response data.

Main Methods:

• Utilized existing R code from 18 peer-reviewed studies for dose response optimization.
• Developed VizDR software using JavaScript with Python scripts for Rserve intercommunication.
• Implemented Maximum Likelihood Estimation (MLE) for optimizing primary dose response models.

Main Results:

• VizDR provides visualization and optimization capabilities for user experimental data.
• The software outputs statistical analyses of model fits and bootstrapped uncertainty information.
• The underlying R code successfully optimizes two primary dose response models.

Conclusions:

• VizDR enhances accessibility to microbial dose response modeling for a larger research community.
• The software simplifies the process of dose response analysis and visualization.
• VizDR supports improved microbial risk assessment through accessible modeling tools.