Number of COVID-19 cases required in a population to detect SARS-CoV-2 RNA in wastewater in the province of Alberta, Canada : Sensitivity assessment
Copyright © 2022. Published by Elsevier B.V..
With a unique and large size of testing results of 1,842 samples collected from 12 wastewater treatment plants (WWTP) for 14 months through from low to high prevalence of COVID-19, the sensitivity of RT-qPCR detection of SARS-CoV-2 RNA in wastewater that correspond to the communities was computed by using Probit analysis. This study determined the number of new COVID-19 cases per 100,000 population required to detect SARS-CoV-2 RNA in wastewater at defined probabilities and provided an evidence-based framework of wastewater-based epidemiology surveillance (WBE). Input data were positive and negative test results of SARS-CoV-2 RNA in wastewater samples and the corresponding new COVID-19 case rates per 100,000 population served by each WWTP. The analyses determined that RT-qPCR-based SARS-CoV-2 RNA detection threshold at 50%, 80% and 99% probability required a median of 8 (range: 4-19), 18 (9-43), and 38 (17-97) of new COVID-19 cases /100,000, respectively. Namely, the positive detection rate at 50%, 80% and 99% probability were 0.01%, 0.02%, and 0.04% averagely for new cases in the population. This study improves understanding of the performance of WBE SARS-CoV-2 RNA detection using the large datasets and prolonged study period. Estimated COVID-19 burden at a community level that would result in a positive detection of SARS-CoV-2 in wastewater is critical to support WBE application as a supplementary warning/monitoring system for COVID-19 prevention and control.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:125 |
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| Enthalten in: |
Journal of environmental sciences (China) - 125(2023) vom: 05. März, Seite 843-850 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Li, Qiaozhi [VerfasserIn] |
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| Links: |
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| Themen: |
COVID-19 cases |
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| Anmerkungen: |
Date Completed 16.11.2022 Date Revised 30.01.2023 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.jes.2022.04.047 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a Copyright © 2022. Published by Elsevier B.V. | ||
| 520 | |a With a unique and large size of testing results of 1,842 samples collected from 12 wastewater treatment plants (WWTP) for 14 months through from low to high prevalence of COVID-19, the sensitivity of RT-qPCR detection of SARS-CoV-2 RNA in wastewater that correspond to the communities was computed by using Probit analysis. This study determined the number of new COVID-19 cases per 100,000 population required to detect SARS-CoV-2 RNA in wastewater at defined probabilities and provided an evidence-based framework of wastewater-based epidemiology surveillance (WBE). Input data were positive and negative test results of SARS-CoV-2 RNA in wastewater samples and the corresponding new COVID-19 case rates per 100,000 population served by each WWTP. The analyses determined that RT-qPCR-based SARS-CoV-2 RNA detection threshold at 50%, 80% and 99% probability required a median of 8 (range: 4-19), 18 (9-43), and 38 (17-97) of new COVID-19 cases /100,000, respectively. Namely, the positive detection rate at 50%, 80% and 99% probability were 0.01%, 0.02%, and 0.04% averagely for new cases in the population. This study improves understanding of the performance of WBE SARS-CoV-2 RNA detection using the large datasets and prolonged study period. Estimated COVID-19 burden at a community level that would result in a positive detection of SARS-CoV-2 in wastewater is critical to support WBE application as a supplementary warning/monitoring system for COVID-19 prevention and control | ||
| 650 | 4 | |a Journal Article | |
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| 650 | 4 | |a Probability | |
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| 700 | 1 | |a Gao, Tiejun |e verfasserin |4 aut | |
| 700 | 1 | |a Qiu, Yuanyuan |e verfasserin |4 aut | |
| 700 | 1 | |a Ellehoj, Erik |e verfasserin |4 aut | |
| 700 | 1 | |a Yu, Jiaao |e verfasserin |4 aut | |
| 700 | 1 | |a Diggle, Mathew |e verfasserin |4 aut | |
| 700 | 1 | |a Tipples, Graham |e verfasserin |4 aut | |
| 700 | 1 | |a Maal-Bared, Rasha |e verfasserin |4 aut | |
| 700 | 1 | |a Hinshaw, Deena |e verfasserin |4 aut | |
| 700 | 1 | |a Sikora, Christopher |e verfasserin |4 aut | |
| 700 | 1 | |a Ashbolt, Nicholas J |e verfasserin |4 aut | |
| 700 | 1 | |a Talbot, James |e verfasserin |4 aut | |
| 700 | 1 | |a Hrudey, Steve E |e verfasserin |4 aut | |
| 700 | 1 | |a Pang, Xiaoli |e verfasserin |4 aut | |
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