Statistical analysis of three data sources for Covid-19 monitoring in Rhineland-Palatinate, Germany.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
03 May 2024
03 May 2024
Historique:
received:
08
08
2023
accepted:
29
04
2024
medline:
4
5
2024
pubmed:
4
5
2024
entrez:
3
5
2024
Statut:
epublish
Résumé
In Rhineland-Palatinate, Germany, a system of three data sources has been established to track the Covid-19 pandemic. These sources are the number of Covid-19-related hospitalizations, the Covid-19 genecopies in wastewater, and the prevalence derived from a cohort study. This paper presents an extensive comparison of these parameters. It is investigated whether wastewater data and a cohort study can be valid surrogate parameters for the number of hospitalizations and thus serve as predictors for coming Covid-19 waves. We observe that this is possible in general for the cohort study prevalence, while the wastewater data suffer from a too large variability to make quantitative predictions by a purely data-driven approach. However, the wastewater data and the cohort study prevalence are able to detect hospitalizations waves in a qualitative manner. Furthermore, a detailed comparison of different normalization techniques of wastewater data is provided.
Identifiants
pubmed: 38702453
doi: 10.1038/s41598-024-60973-z
pii: 10.1038/s41598-024-60973-z
doi:
Substances chimiques
Wastewater
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
10245Informations de copyright
© 2024. The Author(s).
Références
Feng, S. et al. Evaluation of sampling, analysis, and normalization methods for SARS-CoV-2 concentrations in wastewater to assess COVID-19 burdens in wisconsin communities. ACS ES &T Water 1, 1955–1965. https://doi.org/10.1021/acsestwater.1c00160 (2021).
doi: 10.1021/acsestwater.1c00160
Diamond, M. B. et al. Wastewater surveillance of pathogens can inform public health responses. Nat. Med. 28, 1992–1995. https://doi.org/10.1038/s41591-022-01940-x (2022).
doi: 10.1038/s41591-022-01940-x
pubmed: 36076085
Duvallet, C. et al. Nationwide trends in COVID-19 cases and SARS-CoV-2 RNA wastewater concentrations in the united states. ACS Es &t Water 2, 1899–1909. https://doi.org/10.1021/acsestwater.1c00434 (2022).
doi: 10.1021/acsestwater.1c00434
Hewitt, J. et al. Sensitivity of wastewater-based epidemiology for detection of SARS-CoV-2 RNA in a low prevalence setting. Water Res. 211, 118032. https://doi.org/10.1016/j.watres.2021.118032 (2022).
doi: 10.1016/j.watres.2021.118032
pubmed: 35042077
pmcid: 8720482
Langeveld, J. et al. Normalisation of SARS-CoV-2 concentrations in wastewater: The use of flow, electrical conductivity and crAssphage. Sci. Total Environ. 865, 161196. https://doi.org/10.1016/j.scitotenv.2022.161196 (2023).
doi: 10.1016/j.scitotenv.2022.161196
pubmed: 36581271
Cariti, F. et al. Wastewater reveals the spatiotemporal spread of SARS-CoV-2 in the canton of ticino (switzerland) during the onset of the COVID-19 pandemic. Acs Es &T Water 2, 2194–2200. https://doi.org/10.1021/acsestwater.2c00082 (2022).
doi: 10.1021/acsestwater.2c00082
Wade, M. J. et al. Understanding and managing uncertainty and variability for wastewater monitoring beyond the pandemic: Lessons learned from the united kingdom national COVID-19 surveillance programmes. J. Hazard. Mater. 424, 127456. https://doi.org/10.1016/j.jhazmat.2021.127456 (2022).
doi: 10.1016/j.jhazmat.2021.127456
pubmed: 34655869
Robert Koch-Institut. SARS-CoV-2-PCR-testungen in deutschland. (2023). https://doi.org/10.5281/zenodo.7646187 .
Ministerium für Wissenschaft und Gesundheit Rheinland-Pfalz. Gesundheitsminister Clemens Hoch: LUA veröffentlicht Messdaten zum Corona-Abwassermonitoring Rheinland-Pfalz. (2022). https://mwg.rlp.de/service/pressemitteilungen/detail/gesundheitsminister-clemens-hoch-lua-veroeffentlicht-messdaten-zum-corona-abwassermonitoring-rheinland-pfalz .
Robert Koch Insitut. Wochenberichte zu COVID-19. (2021–2023). https://www.rki.de/DE/Content/InfAZ/N/Neuartiges_Coronavirus/Situationsberichte/Wochenbericht/Wochenberichte_Tab.html?nn=13490888 .
Bundesministerium für Gesundheit. Hospitalisierungsinzidenz. (2021). https://www.bundesgesundheitsministerium.de/coronavirus/hospitalisierungsinzidenz .
Bundesministerium für Gesundheit. Nationale Teststrategie SARS-CoV-2. (2022). https://www.bundesgesundheitsministerium.de/fileadmin/Dateien/3_Downloads/C/Coronavirus/Teststrategie/NationaleTeststrategie_Schaubild.pdf .
Wälde, K. How to remove the testing bias in CoV-2 statistics. medRxiv 2020-10 (2020). https://doi.org/10.1101/2020.10.14.20212431 .
Boehm, A. B., Wolfe, M. K., White, B., Hughes, B. & Duong, D. Divergence of wastewater SARS-CoV-2 and reported laboratory-confirmed COVID-19 incident case data coincident with wide-spread availability of at-home COVID-19 antigen tests. PeerJ 11, e15631. https://doi.org/10.7717/peerj.15631 (2023).
doi: 10.7717/peerj.15631
pubmed: 37397016
pmcid: 10312197
Maal-Bared, R. et al. Does normalization of SARS-CoV-2 concentrations by Pepper Mild Mottle Virus improve correlations and lead time between wastewater surveillance and clinical data in Alberta (Canada): Comparing twelve SARS-CoV-2 normalization approaches. Sci. Total Environ. 856, 158964. https://doi.org/10.1016/j.scitotenv.2022.158964 (2023).
doi: 10.1016/j.scitotenv.2022.158964
pubmed: 36167131
Ciannella, S., González-Fernández, C. & Gomez-Pastora, J. Recent progress on wastewater-based epidemiology for COVID-19 surveillance: A systematic review of analytical procedures and epidemiological modeling. Sci. Total Environ. 878, 162953. https://doi.org/10.1016/j.scitotenv.2023.162953 (2023).
doi: 10.1016/j.scitotenv.2023.162953
pubmed: 36948304
pmcid: 10028212
Tang, L. et al. Exploration on wastewater-based epidemiology of SARS-CoV-2: Mimic relative quantification with endogenous biomarkers as internal reference. Heliyon 9, (2023). https://doi.org/10.1016/j.heliyon.2023.e15705 .
Nourbakhsh, S. et al. A wastewater-based epidemic model for SARS-CoV-2 with application to three Canadian cities. Epidemics 39, 100560. https://doi.org/10.1016/j.epidem.2022.100560 (2022).
doi: 10.1016/j.epidem.2022.100560
pubmed: 35462206
pmcid: 8993419
Robert Koch Institute. Systematic surveillance for SARS-CoV-2 in wastewater. (2023). https://www.rki.de/EN/Content/Institute/DepartmentsUnits/InfDiseaseEpidem/Div32/WastewaterSurveillance/WastewaterSurveillance.html
Kitajima, M., Sassi, H. P. & Torrey, J. R. Pepper mild mottle virus as a water quality indicator. NPJ Clean Water 1, (2018). https://doi.org/10.1038/s41545-018-0019-5 .
German Meteorological Service. Klimadaten zum direkten Download. (2023). https://www.dwd.de/DE/leistungen/cdc/cdc_ueberblick-klimadaten.html
Rheinland-Pfalz, L. SARS-CoV-2-Abwassermonitoring für Rheinland-Pfalz. (2023). https://lua.rlp.de/unsere-themen/humanmedizin/daten-zu-atemwegserkrankungen/abwassermonitoring
Wild, P. Vorstellung von SentiSurv RLP. (2023). https://www.unimedizin-mainz.de/sentisurv/ueber-sentisurv-rlp/vorstellung-von-sentisurv-rlp.html
Wild, P. Dashboard SentiSurv RLP. (2023). https://www.unimedizin-mainz.de/SentiSurv-RLP/dashboard/index.html
Van den Bossche, J. et al. Geopandas/geopandas: v0.14.0. (2023). https://doi.org/10.5281/zenodo.8348034 .
Wilson, E. B. Probable inference, the law of succession, and statistical inference. J. Am. Stat. Assoc. 22, 209–212. https://doi.org/10.1080/01621459.1927.10502953 (1927).
doi: 10.1080/01621459.1927.10502953
Breiman, L. Random forests. Mach. Learn. 45, 5–32. https://doi.org/10.1023/a:1010933404324 (2001).
doi: 10.1023/a:1010933404324
R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2023).
Wickham, H. et al. Welcome to the tidyverse. J. Open Source Softw. 4, 1686 (2019). https://doi.org/10.21105/joss.01686 .
Liaw, A. & Wiener, M. Classification and regression by randomForest. R News 2, 18–22 (2002).
Molnar, C., Bischl, B. & Casalicchio, G. Iml: An r package for interpretable machine learning. J. Open Source Softw. 3, 786 (2018). https://doi.org/10.21105/joss.00786 .
Sandoval, S., Bertrand-Krajewski, J.-L., Caradot, N., Hofer, T. & Gruber, G. Performance and uncertainties of TSS stormwater sampling strategies from online time series. Water Sci. Technol. 78, 1407–1416. https://doi.org/10.2166/wst.2018.415 (2018).
doi: 10.2166/wst.2018.415
pubmed: 30388097
Mercier, E. et al. Municipal and neighbourhood level wastewater surveillance and subtyping of an influenza virus outbreak. Sci. Rep. 12, (2022). https://doi.org/10.1038/s41598-022-20076-z .
Wolfe, M. K. et al. Wastewater-based detection of two influenza outbreaks. Environ. Sci. Technol. Lett. 9, 687–692. https://doi.org/10.1021/acs.estlett.2c00350 (2022).
doi: 10.1021/acs.estlett.2c00350
Xagoraraki, I. & O’Brien, E. Wastewater-based epidemiology for early detection of viral outbreaks. In Women in Water Quality 75–97 (Springer International Publishing, 2019). https://doi.org/10.1007/978-3-030-17819-2_5 .
McCall, C., Wu, H., Miyani, B. & Xagoraraki, I. Identification of multiple potential viral diseases in a large urban center using wastewater surveillance. Water Res. 184, 116160. https://doi.org/10.1016/j.watres.2020.116160 (2020).
doi: 10.1016/j.watres.2020.116160
pubmed: 32738707
pmcid: 7342010