Statistical framework to support the epidemiological interpretation of SARS-CoV-2 concentration in municipal wastewater

Xiaotian Dai, David Champredon, Aamir Fazil, Chand S. Mangat, Shelley W. Peterson, Edgard M. Mejia, Xuewen Lu, Thierry Chekouo

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


The ribonucleic acid (RNA) of the severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) is detectable in municipal wastewater as infected individuals can shed the virus in their feces. Viral concentration in wastewater can inform the severity of the COVID-19 pandemic but observations can be noisy and sparse and hence hamper the epidemiological interpretation. Motivated by a Canadian nationwide wastewater surveillance data set, unlike previous studies, we propose a novel Bayesian statistical framework based on the theories of functional data analysis to tackle the challenges embedded in the longitudinal wastewater monitoring data. By employing this framework to analyze the large-scale data set from the nationwide wastewater surveillance program covering 15 sampling sites across Canada, we successfully detect the true trends of viral concentration out of noisy and sparsely observed viral concentrations, and accurately forecast the future trajectory of viral concentrations in wastewater. Along with the excellent performance assessment using simulated data, this study shows that the proposed novel framework is a useful statistical tool and has a significant potential in supporting the epidemiological interpretation of noisy viral concentration measurements from wastewater samples in a real-life setting.

Original languageEnglish (US)
Article number13490
JournalScientific reports
Issue number1
StatePublished - Dec 2022
Externally publishedYes

Bibliographical note

Funding Information:
We thank members of the PHAC’s Wastewater Surveillance unit for laboratory analysis, and Statistics Canada’s Canadian Wastewater Survey team and municipal partners for providing temperature and influent flow data.

Publisher Copyright:
© 2022, The Author(s).

PubMed: MeSH publication types

  • Journal Article


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