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Statistical framework to support the epidemiological interpretation of SARS-CoV-2 concentration in municipal wastewater.
Dai, Xiaotian; Champredon, David; Fazil, Aamir; Mangat, Chand S; Peterson, Shelley W; Mejia, Edgard M; Lu, Xuewen; Chekouo, Thierry.
  • Dai X; Department of Mathematics and Statistics, University of Calgary, Calgary, AB, Canada.
  • Champredon D; Public Health Risk Sciences Division, National Microbiology Laboratory, Public Health Agency of Canada, Guelph, ON, Canada.
  • Fazil A; Public Health Risk Sciences Division, National Microbiology Laboratory, Public Health Agency of Canada, Guelph, ON, Canada.
  • Mangat CS; One Health Division, National Microbiology Laboratory, Public Health Agency of Canada, Winnipeg, MB, Canada.
  • Peterson SW; One Health Division, National Microbiology Laboratory, Public Health Agency of Canada, Winnipeg, MB, Canada.
  • Mejia EM; One Health Division, National Microbiology Laboratory, Public Health Agency of Canada, Winnipeg, MB, Canada.
  • Lu X; Department of Mathematics and Statistics, University of Calgary, Calgary, AB, Canada.
  • Chekouo T; Department of Mathematics and Statistics, University of Calgary, Calgary, AB, Canada. thierry.chekouotekou@ucalgary.ca.
Sci Rep ; 12(1): 13490, 2022 08 05.
Article in English | MEDLINE | ID: covidwho-2077088
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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: SARS-CoV-2 / COVID-19 Type of study: Observational study / Prognostic study Limits: Humans Country/Region as subject: North America Language: English Journal: Sci Rep Year: 2022 Document Type: Article Affiliation country: S41598-022-17543-y

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Full text: Available Collection: International databases Database: MEDLINE Main subject: SARS-CoV-2 / COVID-19 Type of study: Observational study / Prognostic study Limits: Humans Country/Region as subject: North America Language: English Journal: Sci Rep Year: 2022 Document Type: Article Affiliation country: S41598-022-17543-y