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.
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