<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Xander Bertels</style></author><author><style face="normal" font="default" size="100%">Sven Hanoteaux</style></author><author><style face="normal" font="default" size="100%">Raphael Janssens</style></author><author><style face="normal" font="default" size="100%">Hadrien Maloux</style></author><author><style face="normal" font="default" size="100%">Bavo Verhaegen</style></author><author><style face="normal" font="default" size="100%">Peter Delputte</style></author><author><style face="normal" font="default" size="100%">Tim Boogaerts</style></author><author><style face="normal" font="default" size="100%">Alexander L.N. van Nuijs</style></author><author><style face="normal" font="default" size="100%">Delphine Brogna</style></author><author><style face="normal" font="default" size="100%">Catherine Linard</style></author><author><style face="normal" font="default" size="100%">Jonathan Marescaux</style></author><author><style face="normal" font="default" size="100%">Christian Didy</style></author><author><style face="normal" font="default" size="100%">Rosalie Pype</style></author><author><style face="normal" font="default" size="100%">Nancy Roosens</style></author><author><style face="normal" font="default" size="100%">Koenraad Van Hoorde</style></author><author><style face="normal" font="default" size="100%">Marie Lesenfants</style></author><author><style face="normal" font="default" size="100%">Lies Lahousse</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">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</style></title><secondary-title><style face="normal" font="default" size="100%">Science of The Total Environment</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">ARIMA</style></keyword><keyword><style  face="normal" font="default" size="100%">COVID-19</style></keyword><keyword><style  face="normal" font="default" size="100%">Flow rate</style></keyword><keyword><style  face="normal" font="default" size="100%">PMMoV</style></keyword><keyword><style  face="normal" font="default" size="100%">wastewater surveillance</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2023</style></year><pub-dates><date><style  face="normal" font="default" size="100%">19-07-2023</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">899</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Background: Wastewater-based epidemiology (WBE) has been implemented to monitor surges of COVID-19. Yet,&lt;br&gt;
multiple factors impede the usefulness of WBE and quantitative adjustment may be required.&lt;br&gt;
Aim: We aimed to model the relationship between WBE data and incident COVID-19 cases, while adjusting for&lt;br&gt;
confounders and autocorrelation.&lt;br&gt;
Methods: This nationwide WBE study includes data from 40 wastewater treatment plants (WWTPs) in Belgium&lt;br&gt;
(02/2021–06/2022). We applied ARIMA-based modelling to assess the effect of daily flow rate, pepper mild&lt;br&gt;
mottle virus (PMMoV) concentration, a measure of human faeces in wastewater, and variants (alpha, delta, and&lt;br&gt;
omicron strains) on SARS-CoV-2 RNA levels in wastewater. Secondly, adjusted WBE metrics at different lag times&lt;br&gt;
were used to predict incident COVID-19 cases. Model selection was based on AICc minimization.&lt;br&gt;
Results: In 33/40 WWTPs, RNA levels were best explained by incident cases, flow rate, and PMMoV. Flow rate&lt;br&gt;
and PMMoV were associated with -13.0 % (95 % prediction interval: -26.1 to +0.2 %) and +13.0 % (95 %&lt;br&gt;
prediction interval: +5.1 to +21.0 %) change in RNA levels per SD increase, respectively. In 38/40 WWTPs,&lt;br&gt;
variants did not explain variability in RNA levels independent of cases. Furthermore, our study shows that RNA&lt;br&gt;
levels can lead incident cases by at least one week in 15/40 WWTPs. The median population size of leading&lt;br&gt;
WWTPs was 85.1 % larger than that of non‑leading WWTPs. In 17/40 WWTPs, however, RNA levels did not lead&lt;br&gt;
or explain incident cases in addition to autocorrelation.&lt;br&gt;
Conclusion: This study provides quantitative insights into key determinants of WBE, including the effects of&lt;br&gt;
wastewater flow rate, PMMoV, and variants. Substantial inter-WWTP variability was observed in terms of&lt;br&gt;
explaining incident cases. These findings are of practical importance to WBE practitioners and show that the&lt;br&gt;
early-warning potential of WBE is WWTP-specific and needs validation.&lt;/p&gt;
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