OBJECTIVE: The COVID-19 pandemic has drawn attention to the benefit of wastewater-based epidemiology, particularly when case numbers are underreported. Underreporting may be an issue with mpox, where biological reasons and stigma may prevent patients from getting tested. Therefore, we aimed to assess the validity of wastewater surveillance for monitoring mpox virus DNA in wastewater of a Central European city and its association with official case numbers.
METHODS: Wastewater samples were collected between 1 July and 28 August 2022 in the catchment area of Basel, Switzerland, and the number of mpox virus genome copies they contained was determined by real-time quantitative PCR. Logistic regression analyses were used to determine the odds of detectability of mpox virus DNA in wastewater, categorised as detectable or undetectable. Mann-Whitney U tests were used to determine associations between samples that tested positive for the mpox virus and officially reported cases and patients\' recorded symptomatic phases.
RESULTS: Mpox virus DNA was detected in 15 of 39 wastewater samples. The number of positive wastewater samples was associated with the number of symptomatic cases (odds ratio [OR] = 2.18, 95% confidence interval (CI) = 1.38-3.43, p = 0.001). The number of symptomatic cases differed significantly between days with positive versus negative wastewater results (median = 11 and 8, respectively, p = 0.0024).
CONCLUSIONS: Mpox virus DNA was detectable in wastewater, even when officially reported case numbers were low (0-3 newly reported mpox cases corresponding to 6-12 symptomatic patients). Detectability in wastewater was significantly associated with the number of symptomatic patients within the catchment area. These findings illustrate the value of wastewater-based surveillance systems when assessing the prevalence of emerging and circulating infectious diseases.

SARS-CoV-2 pandemic had a significant impact on the society, economy, and health of people around the world with consequences that need to be better understood for future pandemic preparedness. This manuscript provides insights into the usage of pharmaceuticals for pain treatment management throughout SARS-CoV-2 pandemic. Four towns and cities with a total population of > 1 million people covering an area of 2000 km2 in South West England were monitored for twenty-four months. Results showed different patterns in pain pharma usage, with small towns having higher population normalised daily loads (PNDLs) than big cities for majority of pain killers studied. This is likely due to demographics of these cities with smaller cities having older population. Per capita consumption of non-steroidal anti-inflammatory drugs (NSAIDs) increased compared to pre-pandemic usage in line with SARS-CoV-2 infections (ibuprofen and acetaminophen), while body pain drugs (diclofenac and naproxen) decreased in line with restrictions and closure of sports facilities. Changes in population normalised daily intake (PNDI) of pain killers were particularly apparent during the 1st and 3rd national lockdown. Comparison of PNDIs with prescriptions highlighted differences related to medication availability (OTC drugs) and patients\' nonadherence (prescribed drugs). In addition, several instances of direct disposal events across the catchments were observed which raises an issue of lack of pharma compliance and general understanding of potential environmental impacts from pharma usage.

Wastewater-based epidemiology (WBE) is proposed as a cost-effective approach to objectively monitor the antidepressant use but it requires more accurate correction factors (CF) than what had been used in previous studies. Amitriptyline is a popular prescription medicine for treating depression and nerve pain, which could be prone to misuse and need monitoring. The CF of amitriptyline employed in previous WBE studies varied from 10 to 100, leading to substantial disparities between WBE estimates and expected mass of antidepressants in wastewater. Hence, this study aimed to take amitriptyline as a case study and refine the CF by correlating mass loads measured in wastewater from 12.2 million inhabitants collected during the 2016 Census with corresponding annual sales data. The triangulation of WBE data and sales data resulted in a newly-derived CF of 7, which is significantly different from the CF values used in previous studies. The newly derived CF was applied to a secondary, multi-year (2017 to 2020) WBE dataset for validation against sales data in the same period, demonstrating the estimated amitriptyline use (380 ± 320 mg/day/1000 inhabitants) is consistent with sales data (450 ± 190 mg/day/1000 inhabitants). When we applied the new CF to previous studies, the wastewater consumption loads matched better to prescription data than previous WBE estimations. The refined CF of amitriptyline can be used in future WBE studies to improve the accuracy of the consumption estimates.

Wastewater surveillance locally and globally is important for the investigation of the molecular epidemiological features of SARS-CoV-2 in the environment. The current study investigated the genomic diversity and mutation profile of SARS-CoV-2 variants in wastewater for the period spanning COVID-19 pandemic up to December, 2022. A total of 3618 complete SARS-CoV-2 genome sequences from waste water samples submitted to the GISAID database were retrieved. The SARS-CoV-2 sequences were subjected to pairwise alignment against reference, followed by clade and lineage assignment (based on Nextstrain, GISAID and Pango), distance metric phylogenetic analysis, and detection of substitution mutations. Following GISAID, Nextstrain, and Pango nomenclatures, an overall agreement in clade and lineage determination in wastewater samples was observed. There was successive appearance, dissemination, and disappearance of SARS-CoV-2 lineages along time in wastewater. The SARS-CoV-2 genomes from wastewater were clustered into the variants of concern (VOC) as Alpha GRY (B.1.1.7 + Q.7), Delta GK (B.1.617.2 + AY.*), and Omicron GRA (BA.1*, BA.2* + B.1.1.529, BA.5*). The evolutionary rate was 9.63e-04 substitutions/site/year for SARS-CoV-2 in wastewater. B.1.1.7 was less prevalent than B.1.617.2 in 2021, appeared in succession, and BA.1, BA.2, BA.5 were serially detected in 2022, the latter strain continued to persist in wastewater. The N501Y, E484K/Q, K417N/T, L452R, T478K spike substitutions remained dominant attribute of SARS-CoV-2 VOCs. The study underlines the importance of wastewater surveillance for enumerating spatiotemporal diversity of SARS-CoV-2 variants and mutations, which might pave the way for novel antiviral and vaccine designing towards management and prevention of SARS-CoV-2 infection.

Wastewater-based epidemiology (WBE) is a promising tool for monitoring the spread of SARS-CoV-2 and other pathogens, providing a novel public health strategy to combat disease. In this study, we first analysed nationwide reports of infectious diseases and selected Salmonella, norovirus, and influenza A virus (IAV) as prioritized targets apart from SARS-CoV-2 for wastewater surveillance. Next, the decay rates of Salmonella, norovirus, and IAV in wastewater at various temperatures were established to obtain corrected pathogen concentrations in sewage. We then monitored the concentrations of these pathogens in wastewater treatment plant (WWTP) influents in three cities, establishing a prediction model to estimate the number of infected individuals based on the mass balance between total viral load in sewage and individual viral shedding. From October 2022 to March 2023, we conducted multipathogen wastewater surveillance (MPWS) in a WWTP serving one million people in Xi\'an City, monitoring the concentration dynamics of SARS-CoV-2, Salmonella, norovirus, and IAV in sewage. The infection peaks of each pathogen were different, with Salmonella cases and sewage concentration declining from October to December 2022 and only occasionally detected thereafter. The SARS-CoV-2 concentration rapidly increased from December 5th, peaked on December 26th, and then quickly decreased until the end of the study. Norovirus and IAV were detected in wastewater from January to March 2023, peaking in February and March, respectively. We used the prediction models to estimate the rate of SARS-CoV-2 infection in Xi\'an city, with nearly 90 % of the population infected in urban regions. There was no significant difference between the predicted and actual number of hospital admissions for IAV. We also accurately predicted the number of norovirus cases relative to the reported cases. Our findings highlight the importance of wastewater surveillance in addressing public health priorities, underscoring the need for a novel workflow that links the prediction results of populations with public health interventions and allocation of medical resources at the community level. This approach would prevent medical resource panic squeezes, reduce the severity and mortality of patients, and enhance overall public health outcomes.

The COVID-19 pandemic necessitated rapid real-time surveillance of epidemiological data to advise governments and the public, but the accuracy of these data depends on myriad auxiliary assumptions, not least accurate reporting of cases by the public. Wastewater monitoring has emerged internationally as an accurate and objective means for assessing disease prevalence with reduced latency and less dependence on public vigilance, reliability, and engagement. How public interest aligns with COVID-19 personal testing data and wastewater monitoring is, however, very poorly characterized.
This study aims to assess the associations between internet search volume data relevant to COVID-19, public health care statistics, and national-scale wastewater monitoring of SARS-CoV-2 across South Wales, United Kingdom, over time to investigate how interest in the pandemic may reflect the prevalence of SARS-CoV-2, as detected by national testing and wastewater monitoring, and how these data could be used to predict case numbers.
Relative search volume data from Google Trends for search terms linked to the COVID-19 pandemic were extracted and compared against government-reported COVID-19 statistics and quantitative reverse transcription polymerase chain reaction (RT-qPCR) SARS-CoV-2 data generated from wastewater in South Wales, United Kingdom, using multivariate linear models, correlation analysis, and predictions from linear models.
Wastewater monitoring, most infoveillance terms, and nationally reported cases significantly correlated, but these relationships changed over time. Wastewater surveillance data and some infoveillance search terms generated predictions of case numbers that correlated with reported case numbers, but the accuracy of these predictions was inconsistent and many of the relationships changed over time.
Wastewater monitoring presents a valuable means for assessing population-level prevalence of SARS-CoV-2 and could be integrated with other data types such as infoveillance for increasingly accurate inference of virus prevalence. The importance of such monitoring is increasingly clear as a means of objectively assessing the prevalence of SARS-CoV-2 to circumvent the dynamic interest and participation of the public. Increased accessibility of wastewater monitoring data to the public, as is the case for other national data, may enhance public engagement with these forms of monitoring.

Wastewater-based surveillance is increasingly recognized as an important approach to monitoring population-level antimicrobial resistance (AMR). In this exploratory study, we examined the use of metagenomics to evaluate AMR using untreated wastewater samples routinely collected by the Niger national polio surveillance program. Forty-eight stored samples from two seasons each year over 4 years (2016-2019) in three regions were selected for inclusion in this study and processed using unbiased DNA deep sequencing. Normalized number of reads of genetic determinants for different antibiotic classes were compared over time, by season, and by location. Correlations in resistance were examined among classes. Changes in reads per million per year were demonstrated for several classes, including decreases over time in resistance determinants for phenicols (-3.3, 95% CI: -8.7 to -0.1, P = 0.029) and increases over time for aminocoumarins (3.8, 95% CI: 0.0 to 11.4, P = 0.043), fluoroquinolones (6.8, 95% CI: 0.0 to 20.5, P = 0.048), and beta-lactams (0.85, 95% CI: 0.1 to 1.7, P = 0.006). Sulfonamide resistance was higher in the post-rainy season compared with the dry season (5.2-fold change, 95% CI: 3.4 to 7.9, P < 0.001). No differences were detected when comparing other classes by season or by site for any antibiotic class. Positive correlations were identified in genetic determinants of resistance among several antibiotic classes. These results demonstrate the potential utility of leveraging existing wastewater sample collection in this setting for AMR surveillance.

Wastewater testing of SARS-CoV-2 has been adopted globally and has shown to be a useful, non-intrusive surveillance method for monitoring COVID-19 trends. In Singapore, wastewater surveillance has been widely implemented across various sites and has facilitated timely COVID-19 management and response. From April 2020 to February 2022, SARS-CoV-2 RNA concentrations in wastewater monitored across three populations, nationally, in the community, and in High Density Living Environments (HDLEs) were aggregated into indices and compared with reported COVID-19 cases and hospitalisations. Temporal trends and associations of these indices were compared descriptively and quantitatively, using Poisson Generalised Linear Models and Generalised Additive Models. National vaccination rates and vaccine breakthrough infection rates were additionally considered as confounders to shedding. Fitted models quantified the temporal associations between the indices and cases and COVID-related hospitalisations. At the national level, the wastewater index was a leading indicator of COVID-19 cases (p-value <0.001) of one week, and a contemporaneous association with hospitalisations (p-value <0.001) was observed. At finer levels of surveillance, the community index was observed to be contemporaneously associated with COVID-19 cases (p-value <0.001) and had a lagging association of 1-week in HDLEs (p-value <0.001). These temporal differences were attributed to differences in testing routines for different sites during the study period and the timeline of COVID-19 progression in infected persons. Overall, this study demonstrates the utility of wastewater surveillance in understanding underlying COVID-19 transmission and shedding levels, particularly for areas with falling or low case ascertainment. In such settings, wastewater surveillance showed to be a lead indicator of COVID-19 cases. The findings also underscore the potential of wastewater surveillance for monitoring other infectious diseases threats.

COVID-19 is an ongoing public health threat worldwide driven by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Wastewater surveillance has emerged as a complementary tool to clinical surveillance to control the COVID-19 pandemic. With the emergence of new variants of SARS-CoV-2, accumulated mutations that occurred in the SARS-CoV-2 genome raise new challenges for RT-qPCR diagnosis used in wastewater surveillance. There is a pressing need to develop refined methods for modifying primer/probes to better detect these emerging variants in wastewater. Here, we exemplified this process by focusing on the Omicron variants, for which we have developed and validated a modified detection method. We first modified the primers/probe mismatches of three assays commonly used in wastewater surveillance according to in silico analysis results for the mutations of 882 sequences collected during the fifth-wave outbreak in Hong Kong, and then evaluated them alongside the seven original assays. The results showed that five of seven original assays had better sensitivity for detecting Omicron variants, with the limits of detection (LoDs) ranging from 1.53 to 2.76 copies/μL. UCDC-N1 and Charité-E sets had poor performances, having LoDs higher than 10 copies/μL and false-positive/false-negative results in wastewater testing, probably due to the mismatch and demonstrating the need for modification of primer/probe sequences. The modified assays exhibited higher sensitivity and specificity, along with better reproducibility in detecting 81 wastewater samples. In addition, the sequencing results of six wastewater samples by Illumina also validated the presence of mismatches in the primer/probe binding sites of the three assays. This study highlights the importance of re-configuration of the primer-probe sets and refinements for the sequences to ensure the diagnostic effectiveness of RT-qPCR detection.

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.
OBJECTIVE: 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.
CONCLUSIONS: 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.