tetano
Editor, Senior Moderator
Sci Rep
. 2022 Aug 5;12(1):13490.
doi: 10.1038/s41598-022-17543-y.
Statistical framework to support the epidemiological interpretation of SARS-CoV-2 concentration in municipal wastewater
Xiaotian Dai[SUP] 1 [/SUP], David Champredon[SUP] 2 [/SUP], Aamir Fazil[SUP] 2 [/SUP], Chand S Mangat[SUP] 3 [/SUP], Shelley W Peterson[SUP] 3 [/SUP], Edgard M Mejia[SUP] 3 [/SUP], Xuewen Lu[SUP] 1 [/SUP], Thierry Chekouo[SUP] 4 [/SUP]
Affiliations
Abstract
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.
. 2022 Aug 5;12(1):13490.
doi: 10.1038/s41598-022-17543-y.
Statistical framework to support the epidemiological interpretation of SARS-CoV-2 concentration in municipal wastewater
Xiaotian Dai[SUP] 1 [/SUP], David Champredon[SUP] 2 [/SUP], Aamir Fazil[SUP] 2 [/SUP], Chand S Mangat[SUP] 3 [/SUP], Shelley W Peterson[SUP] 3 [/SUP], Edgard M Mejia[SUP] 3 [/SUP], Xuewen Lu[SUP] 1 [/SUP], Thierry Chekouo[SUP] 4 [/SUP]
Affiliations
- PMID: 35931713
- DOI: 10.1038/s41598-022-17543-y
Abstract
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.