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Home > Biblio > Time series modelling for wastewater-based epidemiology of COVID-19: A nationwide study in 40 wastewater treatment plants of Belgium, February 2021 to June 2022

Time series modelling for wastewater-based epidemiology of COVID-19: A nationwide study in 40 wastewater treatment plants of Belgium, February 2021 to June 2022

Ziekten en gezondheid in kaart brengen  
[1]
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Public Access

Published

Peer reviewed scientific article

Engels

DOI : https://doi.org/10.1016/j.scitotenv.2023.165603 [2]

Auteurs

Xander Bertels [3]; Sven Hanoteaux [4]; Raphael Janssens [5]; Hadrien Maloux [6]; Bavo Verhaegen [7]; Peter Delputte [8]; Tim Boogaerts [9]; Alexander L.N. van Nuijs [10]; Delphine Brogna [11]; Catherine Linard [12]; Jonathan Marescaux [13]; Christian Didy [14]; Rosalie Pype [15]; Nancy Roosens [16]; Koenraad Van Hoorde [17]; Marie Lesenfants [18]; Lies Lahousse [19]

Trefwoorden

  1. ARIMA [20]
  2. COVID-19 [21]
  3. Flow rate [22]
  4. PMMoV [23]
  5. wastewater surveillance [24]
Article written during project(s) : 
CoVWWSurv Nationaal epidemiologische surveillance van afvalwater [25]

Samenvatting:

Background: Wastewater-based epidemiology (WBE) has been implemented to monitor surges of COVID-19. Yet, multiple factors impede the usefulness of WBE and quantitative adjustment may be required. Aim: We aimed to model the relationship between WBE data and incident COVID-19 cases, while adjusting for confounders and autocorrelation. Methods: This nationwide WBE study includes data from 40 wastewater treatment plants (WWTPs) in Belgium (02/2021–06/2022). We applied ARIMA-based modelling to assess the effect of daily flow rate, pepper mild mottle virus (PMMoV) concentration, a measure o…
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Samenvatting

Background: Wastewater-based epidemiology (WBE) has been implemented to monitor surges of COVID-19. Yet,
multiple factors impede the usefulness of WBE and quantitative adjustment may be required.
Aim: We aimed to model the relationship between WBE data and incident COVID-19 cases, while adjusting for
confounders and autocorrelation.
Methods: This nationwide WBE study includes data from 40 wastewater treatment plants (WWTPs) in Belgium
(02/2021–06/2022). We applied ARIMA-based modelling to assess the effect of daily flow rate, pepper mild
mottle virus (PMMoV) concentration, a measure of human faeces in wastewater, and variants (alpha, delta, and
omicron strains) on SARS-CoV-2 RNA levels in wastewater. Secondly, adjusted WBE metrics at different lag times
were used to predict incident COVID-19 cases. Model selection was based on AICc minimization.
Results: In 33/40 WWTPs, RNA levels were best explained by incident cases, flow rate, and PMMoV. Flow rate
and PMMoV were associated with -13.0 % (95 % prediction interval: -26.1 to +0.2 %) and +13.0 % (95 %
prediction interval: +5.1 to +21.0 %) change in RNA levels per SD increase, respectively. In 38/40 WWTPs,
variants did not explain variability in RNA levels independent of cases. Furthermore, our study shows that RNA
levels can lead incident cases by at least one week in 15/40 WWTPs. The median population size of leading
WWTPs was 85.1 % larger than that of non‑leading WWTPs. In 17/40 WWTPs, however, RNA levels did not lead
or explain incident cases in addition to autocorrelation.
Conclusion: This study provides quantitative insights into key determinants of WBE, including the effects of
wastewater flow rate, PMMoV, and variants. Substantial inter-WWTP variability was observed in terms of
explaining incident cases. These findings are of practical importance to WBE practitioners and show that the
early-warning potential of WBE is WWTP-specific and needs validation.

Associated health topics:

Ziekten en gezondheid in kaart brengen [26]
Coronavirus [27]

Source URL:https://sciensano.be/nl/biblio/time-series-modelling-wastewater-based-epidemiology-covid-19-a-nationwide-study-40-wastewater

Links
[1] https://sciensano.be/sites/default/files/bertels_et_al._2023_stoten.pdf [2] https://doi.org/10.1016/j.scitotenv.2023.165603 [3] https://sciensano.be/nl/biblio?f%5Bauthor%5D=184831&f%5Bsearch%5D=Xander%20Bertels [4] https://sciensano.be/nl/user/799503/biblio [5] https://sciensano.be/nl/people/raphael-janssens/biblio [6] https://sciensano.be/nl/people/hadrien-maloux/biblio [7] https://sciensano.be/nl/people/bavo-verhaegen/biblio [8] https://sciensano.be/nl/biblio?f%5Bauthor%5D=184835&f%5Bsearch%5D=Peter%20Delputte [9] https://sciensano.be/nl/biblio?f%5Bauthor%5D=184836&f%5Bsearch%5D=Tim%20Boogaerts [10] https://sciensano.be/nl/biblio?f%5Bauthor%5D=184837&f%5Bsearch%5D=Alexander%20L.N.%20van%20Nuijs [11] https://sciensano.be/nl/biblio?f%5Bauthor%5D=184838&f%5Bsearch%5D=Delphine%20Brogna [12] https://sciensano.be/nl/biblio?f%5Bauthor%5D=184839&f%5Bsearch%5D=Catherine%20Linard [13] https://sciensano.be/nl/biblio?f%5Bauthor%5D=184840&f%5Bsearch%5D=Jonathan%20Marescaux [14] https://sciensano.be/nl/biblio?f%5Bauthor%5D=184841&f%5Bsearch%5D=Christian%20Didy [15] https://sciensano.be/nl/biblio?f%5Bauthor%5D=184842&f%5Bsearch%5D=Rosalie%20Pype [16] https://sciensano.be/nl/people/nancy-roosens/biblio [17] https://sciensano.be/nl/people/koenraad-van-hoorde/biblio [18] https://sciensano.be/nl/people/marie-lesenfants/biblio [19] https://sciensano.be/nl/biblio?f%5Bauthor%5D=184845&f%5Bsearch%5D=Lies%20Lahousse [20] https://sciensano.be/nl/biblio?f%5Bkeyword%5D=38003&f%5Bsearch%5D=ARIMA [21] https://sciensano.be/nl/biblio?f%5Bkeyword%5D=36336&f%5Bsearch%5D=COVID-19 [22] https://sciensano.be/nl/biblio?f%5Bkeyword%5D=38005&f%5Bsearch%5D=Flow%20rate [23] https://sciensano.be/nl/biblio?f%5Bkeyword%5D=38004&f%5Bsearch%5D=PMMoV [24] https://sciensano.be/nl/biblio?f%5Bkeyword%5D=37124&f%5Bsearch%5D=wastewater%20surveillance [25] https://sciensano.be/nl/projecten/nationaal-epidemiologische-surveillance-van-afvalwater [26] https://sciensano.be/nl/gezondheidsonderwerpen/ziekten-en-gezondheid-kaart-brengen [27] https://sciensano.be/nl/gezondheidsonderwerpen/coronavirus