tetano
Editor, Senior Moderator
Sci Total Environ
. 2022 Oct 11;159358.
doi: 10.1016/j.scitotenv.2022.159358. Online ahead of print.
Regional and temporal differences in the relation between SARS-CoV-2 biomarkers in wastewater and estimated infection prevalence - Insights from long-term surveillance
Björn Helm[SUP] 1 [/SUP], Michael Geissler[SUP] 2 [/SUP], Robin Mayer[SUP] 3 [/SUP], Sara Schubert[SUP] 4 [/SUP], Reinhard Oertel[SUP] 5 [/SUP], Roger Dumke[SUP] 2 [/SUP], Alexander Dalpke[SUP] 6 [/SUP], Ali El-Armouche[SUP] 7 [/SUP], Bertold Renner[SUP] 5 [/SUP], Peter Krebs[SUP] 3 [/SUP]
Affiliations
Abstract
Wastewater-based epidemiology provides a conceptual framework for the evaluation of the prevalence of public health related biomarkers. In the context of the Coronavirus disease-2019, wastewater monitoring emerged as a complementary tool for epidemic management. In this study, we evaluated data from six wastewater treatment plants in the region of Saxony, Germany. The study period lasted from February to December 2021 and covered the third and fourth regional epidemic waves. We collected 1065 daily composite samples and analyzed SARS-CoV-2 RNA concentrations using reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Regression models quantify the relation between RNA concentrations and disease prevalence. We demonstrated that the relation is site and time specific. Median loads per diagnosed case differed by a factor of 3-4 among sites during both waves and were on average 45 % higher during the third wave. In most cases, log-log-transformed data achieved better regression performance than non-transformed data and local calibration outperformed global models for all sites. The inclusion of lag/lead time, discharge and detection probability improved model performance in all cases significantly, but the importance of these components was also site and time specific. In all cases, models with lag/lead time and log-log-transformed data obtained satisfactory goodness-of-fit with adjusted coefficients of determination higher than 0.5. Back-estimation of testing efficiency from wastewater data confirmed state-wide prevalence estimation from individual testing statistics, but revealed pronounced differences throughout the epidemic waves and among the different sites.
Keywords: Model selection; Prevalence estimation; Regression modelling; SARS-CoV-2; Testing efficiency; Wastewater-based epidemiology.
. 2022 Oct 11;159358.
doi: 10.1016/j.scitotenv.2022.159358. Online ahead of print.
Regional and temporal differences in the relation between SARS-CoV-2 biomarkers in wastewater and estimated infection prevalence - Insights from long-term surveillance
Björn Helm[SUP] 1 [/SUP], Michael Geissler[SUP] 2 [/SUP], Robin Mayer[SUP] 3 [/SUP], Sara Schubert[SUP] 4 [/SUP], Reinhard Oertel[SUP] 5 [/SUP], Roger Dumke[SUP] 2 [/SUP], Alexander Dalpke[SUP] 6 [/SUP], Ali El-Armouche[SUP] 7 [/SUP], Bertold Renner[SUP] 5 [/SUP], Peter Krebs[SUP] 3 [/SUP]
Affiliations
- PMID: 36240928
- PMCID: PMC9554318
- DOI: 10.1016/j.scitotenv.2022.159358
Abstract
Wastewater-based epidemiology provides a conceptual framework for the evaluation of the prevalence of public health related biomarkers. In the context of the Coronavirus disease-2019, wastewater monitoring emerged as a complementary tool for epidemic management. In this study, we evaluated data from six wastewater treatment plants in the region of Saxony, Germany. The study period lasted from February to December 2021 and covered the third and fourth regional epidemic waves. We collected 1065 daily composite samples and analyzed SARS-CoV-2 RNA concentrations using reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Regression models quantify the relation between RNA concentrations and disease prevalence. We demonstrated that the relation is site and time specific. Median loads per diagnosed case differed by a factor of 3-4 among sites during both waves and were on average 45 % higher during the third wave. In most cases, log-log-transformed data achieved better regression performance than non-transformed data and local calibration outperformed global models for all sites. The inclusion of lag/lead time, discharge and detection probability improved model performance in all cases significantly, but the importance of these components was also site and time specific. In all cases, models with lag/lead time and log-log-transformed data obtained satisfactory goodness-of-fit with adjusted coefficients of determination higher than 0.5. Back-estimation of testing efficiency from wastewater data confirmed state-wide prevalence estimation from individual testing statistics, but revealed pronounced differences throughout the epidemic waves and among the different sites.
Keywords: Model selection; Prevalence estimation; Regression modelling; SARS-CoV-2; Testing efficiency; Wastewater-based epidemiology.