TY - JOUR
T1 - Time series modelling for wastewater-based epidemiology of COVID-19
T2 - A nationwide study in 40 wastewater treatment plants of Belgium, February 2021 to June 2022
AU - Bertels, Xander
AU - Hanoteaux, Sven
AU - Janssens, Raphael
AU - Maloux, Hadrien
AU - Verhaegen, Bavo
AU - Delputte, Peter
AU - Boogaerts, Tim
AU - van Nuijs, Alexander L.N.
AU - Brogna, Delphine
AU - Linard, Catherine
AU - Marescaux, Jonathan
AU - Didy, Christian
AU - Pype, Rosalie
AU - Roosens, Nancy H.C.
AU - Van Hoorde, Koenraad
AU - Lesenfants, Marie
AU - Lahousse, Lies
N1 - Funding Information:
This work was supported by the Belgian Federal Government (grants COVID-19_SC048 , COVID-19_SC063 , and COVID-19_SC093 ). It was also supported by Société Publique de Gestion de l'Eau (SPGE), E-BIOM , and the University of Namur ( CR-280 ) through two public procurements (MP20.051, MP20.097). SPGE was involved in wastewater sampling and communication of data regarding the Walloon WWTPs. E-BIOM was involved in wastewater analysis. Both SPGE and E-BIOM contributed to the co-authoring review process of the paper. Yet the funders had no role in the design of the study, in the interpretation of data, in drafting the manuscript, or in the decision to publish the results.
Publisher Copyright:
© 2023
PY - 2023/11/15
Y1 - 2023/11/15
N2 - 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.
AB - 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.
KW - ARIMA
KW - COVID-19
KW - Flow rate
KW - PMMoV
KW - Wastewater surveillance
UR - http://www.scopus.com/inward/record.url?scp=85165377284&partnerID=8YFLogxK
U2 - 10.1016/j.scitotenv.2023.165603
DO - 10.1016/j.scitotenv.2023.165603
M3 - Article
C2 - 37474075
AN - SCOPUS:85165377284
SN - 0048-9697
VL - 899
JO - Science of the Total Environment
JF - Science of the Total Environment
M1 - 165603
ER -
