Collaborative Research Team Projects – Project 26
The Application of Statistical Methods to Wastewater Analysis
Analysis of wastewater-based surveillance (WBS) data for infectious disease monitoring is a new and rapidly growing field. The proposed research program will build upon our established collaboration with wastewater scientists and data providers to consider new methodological developments in statistical approaches to analysis of WBS data, while also exploring the use of established statistical methods in validating and calibrating responsive analytical methods for WBS data.
Why Build a Statistical Framework for Inquiry?
Canadian statisticians have made important contributions to many areas of research in environmetrics, and the need for training of highly qualified personnel is critical given the increasing availability of environmental monitoring and surveillance data. The sampling of wastewater represents an unintrusive means of gathering data on the presence of SARS-CoV-2 within a community. However, little work has been done in testing the robustness and predictive performance of models for analysis of WBS data. We aim to address this knowledge gap by building a statistical framework for inquiry, motivated by a partnership with wastewater scientists and open data from provincial and national WBS programs.
The methods developed here will equip health decision-makers with a crucial tool to effectively address health threats affecting communities based on information from WBS data. The methodological development will enhance the tools available broadly for environmetrics, and the talent trained will have considerable transferable skills related to spatial methods, sampling, time series, as well as working in collaborative environments.
Research Objectives and Projects
The aim of this research program is to provide statisticians and scientists with a comprehensive analytical toolkit. Our research program will establish a baseline for model performance and assessment when using environmentally monitored wastewater viral signals to predict hospitalizations due to respiratory illnesses such as Covid-19, influenza, and respiratory syncytial virus (RSV).
A second goal, to be developed in conjunction with primary stakeholders including public health authorities and wastewater scientists working in universities across Canada, is to build much needed tools for spatio-temporal analyses with general applications in modelling environmental processes from a Canadian perspective. We also plan to support wastewater engineers in their development of best practices guidelines for Canada, which is underway, through validation and testing of methods.
This type of analysis and validation is needed to provide confidence for health policy decision makers using WBS data. Our overall goal is to enhance robustness of statistical methods and address knowledge gaps in application of statistical methodology to WBS data.
Our proposed projects fall under the following four categories:
- Selection of appropriate models
- Extension of preferential sampling methodology
- Joint modelling applications
- Calibration, validation, missing data investigations
People Behind the Project
Charmaine B. Dean | University of Waterloo
X. Joan Hu | Simon Fraser University
Robert Delatolla | University of Ottawa
M. Elizabeth Renouf | University of Ottawa and University of Waterloo