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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., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Delphine Brogna reports financial support was provided by E-BIOM. Catherine Linard reports financial support was provided by E-BIOM. Christian Didy reports financial support was provided by SPGE. Rosalie Pype reports financial support was provided by SPGE. Jonathan Marescaux reports financial support was provided by E-BIOM. Sven Hanoteaux reports a relationship with Sciensano that includes: employment. Raphael Janssens reports a relationship with Sciensano that includes: employment. Hadrien Maloux reports a relationship with Sciensano that includes: employment. Bavo Verhaegen reports a relationship with Sciensano that includes: employment. Jonathan Marescaux reports a relationship with E-BIOM that includes: employment. Koenraad Van Hoorde reports a relationship with Sciensano that includes: employment. Marie Lesenfants reports a relationship with Sciensano that includes: employment. Rosalie Pype reports a relationship with SPGE that includes: employment. Christian Didy reports a relationship with SPGE that includes: employment., (Copyright © 2023. Published by Elsevier B.V.)

Wastewater-based epidemiology (WBE) includes the analysis of human metabolic biomarkers of xenobiotics in influent wastewater. WBE complements existing drug utilization approaches and provides objective, spatio-temporal information on the consumption of pharmaceuticals in the general population. This approach was applied to 24-h composite influent wastewater samples from Leuven, Belgium. Daily samples were analysed from September 2019 to December 2019 (n = 76), and on three days of the week (Monday, Wednesday, Saturday) from January 2020 to April 2022 (n = 367). Sample analysis consisted of 96-well solid-phase extraction and liquid chromatography coupled to tandem mass spectrometry. Measured concentrations of 21 biomarkers for antidepressant and opioid use were converted to population-normalized mass loads (PNML) by considering the flow rate and catchment population. To capture population movements, mobile phone data was used. Amitriptyline, hydroxy-bupropion, norcitalopram, citalopram, normirtazapine, trazodone, O-desmethylvenlafaxine, codeine, 2-ethylidene-1,5-dimethyl-3,3-diphenylpyrrolidine (EDDP), methadone, morphine, O-desmethyltramadol, and tramadol were included in the temporal assessment since concentrations were above the lower limit of quantification. The PNML of most biomarkers increased (with 3-119 %) throughout the sampling period. The population disruption during the COVID-19 pandemic led to a major change in the socio-demographics of the catchment area, resulting in temporal differences in the PNML of the different biomarkers. As such, higher PNML were observed during the different lockdown phases, which were characterized by the outflow of university students and a decreasing commuting in and out the catchment area. The effects of the fluctuating socio-demographics of the catchment population were further evidenced by the different week-weekend pattern of PNMLs over the course of the sampling campaign. Mean parent/metabolite ratios (i.e., citalopram/norcitalopram, tramadol/O-desmethyltramadol, venlafaxine/O-desmethylvenlafaxine, and methadone/EDDP) remained relatively stable throughout the entire sampling campaign (RSD% below 25 % for all ratios, except for methadone/EDDP) and therefore were not affected by this population change., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Wastewater-based surveillance (WBS) for SARS-CoV-2 RNA is a promising complementary approach to monitor community viral circulation. A myriad of factors, however, can influence RNA concentrations in wastewater, impeding its epidemiological value. This article aims to provide an overview and discussion of factors up to the sampling stage that impact SARS-CoV-2 RNA concentration estimates in wastewater. To this end, a systematic review was performed in three databases (MEDLINE, Web of Science and Embase) and two preprint servers (MedRxiv and BioRxiv). Two authors independently screened and selected articles published between January 1, 2019 and May 4, 2021. A total of 22 eligible articles were included in this systematic review. The following factors up to sampling were identified to have an influence on SARS-CoV-2 RNA concentrations in wastewater and its interpretation: (i) shedding-related factors, including faecal shedding parameters (i.e. shedding pattern, recovery, rate, and load distribution), (ii) population size, (iii) in-sewer factors, including solid particles, organic load, travel time, flow rate, wastewater pH and temperature, and (iv) sampling strategy. In conclusion, factors influencing SARS-CoV-2 RNA concentration estimates in wastewater were identified and research gaps were discussed. The identification of these factors supports the need for further research on WBS for COVID-19., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)