Wastewater-based Epidemiology Research Articles

Machine Learning–Based Source Identification in Sewer Networks

Motivated by the valuable epidemiological information it reveals, wastewater surveillance has received significant attention in recent years. Furthermore, monitoring the water quality in sewer systems has been shown to provide useful information to support wastewater treatment operations. Yet, a critical need still exists for developing novel approaches for rapid and efficient source identification of chemical and biological species of interest in sewer systems. A limited number of source identification approaches have been proposed in previous literature, and the majority of these approaches employed various simplifying assumptions that limit their usage in real-life applications. In this study, a machine learning–based simulation-optimization framework was developed to determine the characteristics (i.e., concentration and loading pattern) of multiple simultaneous injection sources in sewer systems. The simulation was conducted using a surrogate model in the form of a multilayer perceptron neural network, which was trained using simulation results derived from the Storm Water Management Model (SWMM). The simulation model was then coupled with a genetic algorithm to reveal the characteristics of multiple sources that reproduce the concentration patterns observed at one or more monitoring locations in the sewer system. The proposed framework was applied to a range of injection scenarios and was able to identify the characteristics of multiple simultaneous injection sources under different conditions. The results showed that the residence time plays a significant role in the identifiability of the injection source location. The proposed framework is applicable to a wide number of source identification applications, including contamination source identification and wastewater-based epidemiology.

Monitoring hand foot and mouth disease using long-term wastewater surveillance in Japan: Quantitative PCR assay development and application

Hand, foot, and mouth disease (HFMD) is a highly contagious disease that primarily affects children under five years of age. It is mainly caused by serotypes of Enterovirus A (EVA): EVA71, Coxsackievirus A types 6 (CVA6), 10 (CVA10), and 16 (CVA16). Despite being highly prevalent in Japan and other countries in the Asia-Pacific region, few studies have investigated HFMD pathogens in wastewater. The present study aimed to develop a highly sensitive and broadly reactive quantitative polymerase chain reaction (qPCR) assay of dominant serotype CVA6, to revise previously developed CVA6, CVA10, and CVA16 assays, and to test these assays in wastewater samples from Yamanashi Prefecture, Japan. The new-CVA6 qPCR assay was developed with maximal nucleotide percent identity among CVA6 isolates from Japan. The new-CVA6 and revised assays were highly sensitive and had the ability to quantify respective positive controls at levels as low as 1 copy/μL. Among the 53 grab influent samples collected between March 2022 and March 2023, EVA71, CVA10, and CVA16 RNA were not detected in any samples, whereas the new-CVA6 assay could detect CVA6 RNA in 38 % (20/53) of samples. CVA6 RNA was detected at a significantly higher concentration in the summer season (3.3 ± 0.8 log10 copies/L; 79 % (11/14)) than in autumn (2.7 ± 0.6 log10 copies/L; 69 % (9/13)). The seasonal trend of CVA6 RNA detection in wastewater aligned with the trend of HFMD case reports in the catchment of the wastewater treatment plant. This is the first study to report the detection and seasonal trends of the EVA serotypes associated with HFMD in wastewater samples in Japan. It provides evidence that wastewater-based epidemiology is applicable even for diseases that are prevalent only in specific population groups.

Wastewater-based epidemiology predicts COVID-19-induced weekly new hospital admissions in over 150 USA counties

Although the coronavirus disease (COVID-19) emergency status is easing, the COVID-19 pandemic continues to affect healthcare systems globally. It is crucial to have a reliable and population-wide prediction tool for estimating COVID-19-induced hospital admissions. We evaluated the feasibility of using wastewater-based epidemiology (WBE) to predict COVID-19-induced weekly new hospitalizations in 159 counties across 45 states in the United States of America (USA), covering a population of nearly 100 million. Using county-level weekly wastewater surveillance data (over 20 months), WBE-based models were established through the random forest algorithm. WBE-based models accurately predicted the county-level weekly new admissions, allowing a preparation window of 1-4 weeks. In real applications, periodically updated WBE-based models showed good accuracy and transferability, with mean absolute error within 4-6 patients/100k population for upcoming weekly new hospitalization numbers. Our study demonstrated the potential of using WBE as an effective method to provide early warnings for healthcare systems.

Wastewater profiling of illicit drugs, an estimation of community consumption: A case study of eThekwini Metropolitan Municipality, South Africa

Estimation of community-wide consumption of illicit drugs through wastewater analysis is a new concept in Africa although widely applied in developed nations as wastewater-based epidemiology (WBE). It is an important tool that can be used in understanding supply and demand patterns of illicit drug use on a local, national, and international scale. Information on illicit drug use is currently limited in Africa, because of a lack of monitoring structures by governments and financial constraints. This study hopes to bridge that gap by contributing to Africa's baseline information on illicit drug use. This study provides the first application of wastewater analysis to quantitatively evaluate daily illicit drug use in the eThekwini Metropolitan Municipality of KwaZulu-Natal Province of South Africa by quantifying the major urinary excreted metabolites, called drug target residues (DTRs), in raw wastewater from four major wastewater treatment plants (WWTPs) sampled for a week. The results showed that cocaine was the dominant illicit drug consumed in the catchment followed by amphetamine, methamphetamine, 3,4-methylenedioxymethamphetamine and 3,4-methylenedioxyamphetamine, with a per capita use of 360–3000 mg day−1 1000 inh−1, 47–800 mg day−1 1000 inh−1, 19–120 mg day −1 1000 inh−1, not detected (ND) to 4.9 mg day−1 1000 inh−1, and ND to 410 g day−1 1000 inh−1 respectively. The weekly usage patterns between the four WWTPs differed probably due to the lifestyle of the populace serving the different WWTPs. These results provide useful data on illicit drug use in eThekwini that can be utilised by public health agencies to implement suitable response strategies.

The role of wastewater-based epidemiology for SARS-CoV-2 in developing countries: Cumulative evidence from South Africa supports sentinel site surveillance to guide public health decision-making

The uptake of wastewater-based epidemiology (WBE) for SARS-CoV-2 as a complementary tool for monitoring population-level epidemiological features of the COVID-19 pandemic in low-and-middle-income countries (LMICs) is low. We report on the findings from the South African SARS-CoV-2 WBE surveillance network and make recommendations regarding the implementation of WBE in LMICs.Eight laboratories quantified influent wastewater collected from 87 wastewater treatment plants in all nine South African provinces from 01 June 2021 to 31 May 2022 inclusive, during the 3rd and 4th waves of COVID-19. Correlation and regression analyses between wastewater levels of SARS-CoV-2 and district laboratory-confirmed caseloads were conducted. The sensitivity and specificity of novel ‘rules’ based on WBE data to predict an epidemic wave were determined.Amongst 2158 wastewater samples, 543/648 (85 %) samples taken during a wave tested positive for SARS-CoV-2 compared with 842 positive tests from 1512 (55 %) samples taken during the interwave period. Overall, the regression-co-efficient was 0,66 (95 % confidence interval = 0,6-0,72, R2 = 0.59), ranging from 0.14 to 0.87 by testing laboratory. Early warning of the 4th wave of SARS-CoV-2 in Gauteng Province in November–December 2021 was demonstrated. A 50 % increase in log copies of SARS-CoV-2 compared with a rolling mean over the previous five weeks was the most sensitive predictive rule (58 %) to predict a new wave.Our findings support investment in WBE for SARS-CoV-2 surveillance in LMICs as an early warning tool. Standardising test methodology is necessary due to varying correlation strengths across laboratories and redundancy across testing plants. A sentinel site model can be used for surveillance networks without affecting WBE finding for decision-making. Further research is needed to identify optimal test frequency and the need for normalisation to population size to identify predictive and interpretive rules to support early warning and public health action.

An integrated One Health framework for holistic evaluation of risks from antifungal agents in a large-scale multi-city study

A new framework for retrospective mass spectral data mining for antifungal agents (AFs) and Wastewater-Based Epidemiology (WBE) was developed as part of One Health framework to tackle risks from AFs. A large scale, multi-city study was undertaken in South-West England. Key drivers of AFs in the catchment were identified with communal wastewater discharges being the main driver for human AFs (fluconazole, ketoconazole) and agricultural runoff being the main driver for pesticide AFs (prochloraz, prothioconazole and tebuconazole). Average WBE-estimated human used fluconazole and ketoconazole PNDIs (population normalised daily intake) exceeded 300 mg day−1 1000 inh−1 and 2000 mg day−1 1000 inh−1. This is much higher than PNDPs (population normalised daily prescriptions <40 mg day−1 1000 inh−1 and <80 mg day−1 1000 inh−1 for fluconazole and ketoconazole respectively). This was expected due to both prescription and over-the-counter usage, and both oral and topical applications. Pesticide AF, prothioconazole had PNDIs <40,000 mg day−1 1000 inh −1, which gave intake: 0.43, 0.26, 0.07 mg kg−1 in City A, B, and C, likely due to accounting for external/non-human sources. This is higher than the acceptable daily intake (ADI) of 0.01 mg kg−1bw day−1, which warrants further study. Intake per kg of body weight estimated using tebuconazole was 0.86, 1.39, 0.12, 0.13, and 2.7 mg kg−1 in City A-E respectively and is likely due to external/non-human sources. Intake calculated using its metabolite was 0.02 and 0.01 mg kg−1 in City B and C respectively, which aligned with ADI (0.03 mg kg−1bw day−1). The environmental risk assessment of AFs indicated low/medium risk from fluconazole, prochloraz, and tebuconazole, medium risk from epoxiconazole, prothioconazole's metabolite, and tebuconazole, and high risk for prothioconazole in river water. High risk was estimated from fluconazole, epoxiconazole, prothioconazole and its metabolite, tebuconazole, ketoconazole in wastewater samples, which is important during raw sewage discharge events via sewer overflows.

A new analytical method to determine trace level concentrations of pharmaceuticals in influent wastewater: A tool to monitor human use patterns

The occurrence of pharmaceuticals in influent wastewater samples (IWW) is a recurrent issue. The monitoring of their presence is not only valuable from an environmental point of view, but also as a tool to analyze patterns of human use by the so-called wastewater-based epidemiology. The development of an analytical method based on solid-phase extraction followed by liquid chromatography coupled to tandem mass spectrometry to monitor the occurrence of a group of seventeen pharmaceuticals including the most representative for various therapeutic families in IWW samples is described in this work. The samples were collected during a monitoring week in six wastewater treatment plants located in different cities and towns across Spain. The developed method provides acceptable figures of merit with apparent recoveries in IWW ranging from 42% to 139%, and low matrix effect (in general lower than ± 30%), and method quantification limits (MQL) between 1 ng/L and 24 ng/L for all compounds, except atenolol (58 ng/L). All the studied pharmaceuticals were found in all samples with concentrations ranging from < MQL to 10,393 ng/L, being the highest concentrations for tramadol. The population normalized daily loads revealed that the use of pharmaceuticals follows, in general, a similar pattern in all cities monitored.

Analysis of sampling strategies for pulse loads of SARS-CoV-2: implications for wastewater-based epidemiology.

A faecal transport model was applied to a 11.3 km2 wastewater servicing area in Toronto, Ontario, Canada to explore the role that different wastewater sampling campaigns have on estimating the prevalence of SARS-CoV-2 in a population of 60,000. A stochastic wastewater and water quality model was used to evaluate the effectiveness of 11 sampling campaigns during periods of high and low COVID-19 infection among the population, tested using virtual sampling during dry-weather flow. The virtual sampling campaigns were based on the most common automatic sampler programming capabilities and widely used wastewater-based epidemiology (WBE) sampling campaigns reported in the literature. Sampling campaigns differ in weighting method (time, volume, or flow-weighted sampling), sample count, collection period, or sample time. Results suggest that grab samples should be avoided and/or that sampling campaigns with the greatest sample counts and durations are the most robust at capturing COVID-19 infection among the population. Most surprisingly, changes to the weighting method were negligible indicating that a greater number of samples, and larger sample volumes are preferred. This work suggests that investment in flow monitoring equipment for flow- or volume-weighted sampling will not improve WBE results, and that standard time based sampling is sufficient.

Evaluation of wastewater-based epidemiology of COVID-19 approaches in Singapore's ‘closed-system’ scenario: A long-term country-wide assessment

With the COVID-19 pandemic the use of WBE to track diseases spread has rapidly evolved into a widely applied strategy worldwide. However, many of the current studies lack the necessary systematic approach and supporting quality of epidemiological data to fully evaluate the effectiveness and usefulness of such methods. Use of WBE in a very low disease prevalence setting and for long-term monitoring has yet to be validated and it is critical for its intended use as an early warning system. In this study we seek to evaluate the sensitivity of WBE approaches under low prevalence of disease and ability to provide early warning. Two monitoring scenarios were used: (i) city wide monitoring (population 5,700,000) and (ii) community/localized monitoring (population 24,000 to 240,000). Prediction of active cases by WBE using multiple linear regression shows that a multiplexed qPCR approach with three gene targets has a significant advantage over single-gene monitoring approaches, with R2 = 0.832 (RMSE 0.053) for an analysis using N, ORF1ab and S genes (R2 = 0.677 to 0.793 for single gene strategies). A predicted disease prevalence of 0.001% (1 in 100,000) for a city-wide monitoring was estimated by the multiplexed RT-qPCR approach and was corroborated by epidemiological data evidence in three ‘waves’. Localized monitoring setting shows an estimated detectable disease prevalence of ∼0.002% (1 in 56,000) and is supported by the geospatial distribution of active cases and local population dynamics data. Data analysis also shows that this approach has a limitation in sensitivity, or hit rate, of 62.5 % and an associated high miss rate (false negative rate) of 37.5 % when compared to available epidemiological data. Nevertheless, our study shows that, with enough sampling resolution, WBE at a community level can achieve high precision and accuracies for case detection (96 % and 95 %, respectively) with low false omission rate (4.5 %) even at low disease prevalence levels.

Surveillance of multidrug-resistant tuberculosis in sub-Saharan Africa through wastewater-based epidemiology

The spread of multidrug-resistant tuberculosis (MDR-TB) is a serious public health issue, particularly in developing nations. The current methods of monitoring drug-resistant TB (DR-TB) using clinical diagnoses and hospital records are insufficient due to limited healthcare access and underreporting. This study proposes using Wastewater-Based Epidemiology (WBE) to monitor DR-TB in six African countries (Ghana, Nigeria, Kenya, Uganda, Cameroon, and South Africa) and examines the impact of treated wastewater on the spread of TB drug-resistant genes in the environment. Using droplet-digital polymerase chain reaction (ddPCR), the study evaluated untreated and treated wastewater samples in selected African countries for TB surveillance. There was a statistically significant difference in concentrations of genes conferring resistance to TB drugs in wastewater samples from the selected countries (p-value<0.05); South African samples exhibited the highest concentrations of 4.3(±2,77), 4.8(±2.96), 4.4(±3,10) and 4.7(±3,39) log copies/ml for genes conferring resistance to first-line TB drugs (katG, rpoB, embB and pncA respectively) in untreated wastewater. This may be attributed to the higher prevalence of TB/MDR-TB in SA compared to other African countries. Interestingly, genes conferring resistance to second-line TB drugs such as delamanid (ddn gene) and bedaquiline (atpE gene) were detected in relatively high concentrations (4.8(±3,67 and 3.2(±2,31 log copies/ml for ddn and atpE respectively) in countries, such as Cameroon, where these drugs are not part of the MDR-TB treatment regimens, perhaps due to migration or the unapproved use of these drugs in the country. The gene encoding resistance to streptomycin (rrs gene) was abundant in all countries, perhaps due to the common use of this antibiotic for infections other than TB. These results highlight the need for additional surveillance and monitoring, such as WBE, to gather data at a community level. Combining WBE with the One Health strategy and current TB surveillance systems can help prevent the spread of DR-TB in populations.

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

BackgroundWastewater-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. AimWe aimed to model the relationship between WBE data and incident COVID-19 cases, while adjusting for confounders and autocorrelation. MethodsThis 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. ResultsIn 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. ConclusionThis 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.

Sewage Protein Information Mining: Discovery of Large Biomolecules as Biomarkers of Population and Industrial Activities.

Wastewater-based epidemiology has been revealed as a powerful approach for surveying the health and lifestyle of a population. In this context, proteins have been proposed as potential biomarkers that complement the information provided by currently available methods. However, little is known about the range of molecular species and dynamics of proteins in wastewater and the information hidden in these protein profiles is still to be uncovered. In this study, we investigated the protein composition of wastewater from 10 municipalities in Catalonia with diverse populations and industrial activities at three different times of the year. The soluble fraction of this material was analyzed using liquid chromatography high-resolution tandem mass spectrometry using a shotgun proteomics approach. The complete proteomic profile, distribution among different organisms, and semiquantitative analysis of the main constituents are described. Excreta (urine and feces) from humans, and blood and other residues from livestock were identified as the two main protein sources. Our findings provide new insights into the characterization of wastewater proteomics that allow for the proposal of specific bioindicators for wastewater-based environmental monitoring. This includes human and animal population monitoring, most notably for rodent pest control (immunoglobulins (Igs) and amylases) and livestock processing industry monitoring (albumins).

Challenges detecting SARS-CoV-2 in Costa Rican domestic wastewater and river water

This study presents the development of a SARS-CoV-2 detection method for domestic wastewater and river water in Costa Rica, a middle-income country in Central America. Over a three-year period (November to December 2020, July to November 2021, and June to October 2022), 80 composite wastewater samples (43 influent and 37 effluent) were collected from a Wastewater Treatment Plant (SJ-WWTP) located in San José, Costa Rica. Additionally, 36 river water samples were collected from the Torres River near the SJ-WWTP discharge site. A total of three protocols for SARS-CoV-2 viral concentration and RNA detection and quantification were analyzed. Two protocols using adsorption-elution with PEG precipitation (Protocol A and B, differing in the RNA extraction kit; n = 82) were used on wastewater samples frozen prior to concentration, while wastewater (n = 34) collected in 2022 were immediately concentrated using PEG precipitation. The percent recovery of Bovine coronavirus (BCoV) was highest using the Zymo Environ Water RNA (ZEW) kit with PEG precipitation executed on the same day as collection (mean 6.06 % ± 1.37 %). It was lowest when samples were frozen and thawed, and viruses were concentrated using adsorption-elution and PEG concentration methods using the PureLink™ Viral RNA/DNA Mini (PLV) kit (protocol A; mean 0.48 % ± 0.23 %). Pepper mild mottle virus and Bovine coronavirus were used as process controls to understand the suitability and potential impact of viral recovery on the detection/quantification of SARS-CoV-2 RNA. Overall, SARS-CoV-2 RNA was detected in influent and effluent wastewater samples collected in 2022 but not in earlier years when the method was not optimized. The burden of SARS-CoV-2 at the SJ-WWTP decreased from week 36 to week 43 of 2022, coinciding with a decline in the national COVID-19 prevalence rate. Developing comprehensive nationwide surveillance programs for wastewater-based epidemiology in low-middle-income countries involves significant technical and logistical challenges.

Monitoring SARS-CoV-2 variants in wastewater of Dhaka City, Bangladesh: approach to complement public health surveillance systems

BackgroundWastewater-based epidemiological surveillance has been considered a powerful tool for early detection and monitoring of the dynamics of SARS-CoV-2 and its lineages circulating in a community. This study is aimed to investigate the complexity of SARS-CoV-2 infection dynamics in Dhaka city by examining its genetic variants in wastewater. Also, the study seeks to determine a connection between the SARS-CoV-2 variations detected in clinical testing and those found in wastewater samples.ResultsOut of 504 samples tested in RT-qPCR, 185 (36.7%) tested positive for SARS-CoV-2 viral RNA. The median log10 concentration of SARS-CoV-2 N gene copies/Liter of wastewater (gc/L) was 5.2, and the median log10 concentration of ORF1ab was 4.9. To further reveal the genetic diversity of SARS-CoV-2, ten samples with ORF1ab real-time RT-PCR cycle threshold (Ct) values ranging from 28.78 to 32.13 were subjected to whole genome sequencing using nanopore technology. According to clade classification, sequences from wastewater samples were grouped into 4 clades: 20A, 20B, 21A, 21J, and the Pango lineage, B.1, B.1.1, B.1.1.25, and B.1.617.2, with coverage ranging from 94.2 to 99.8%. Of them, 70% belonged to clade 20B, followed by 10% to clade 20A, 21A, and 21J. Lineage B.1.1.25 was predominant in Bangladesh and phylogenetically related to the sequences from India, the USA, Canada, the UK, and Italy. The Delta variant (B.1.617.2) was first identified in clinical samples at the beginning of May 2021. In contrast, we found that it was circulating in the community and was detected in wastewater in September 2020.ConclusionEnvironmental surveillance is useful for monitoring temporal and spatial trends of existing and emerging infectious diseases and supports evidence-based public health measures. The findings of this study supported the use of wastewater-based epidemiology and provided the baseline data for the dynamics of SARS-CoV-2 variants in the wastewater environment in Dhaka, Bangladesh.

Designing a Wastewater-Based Epidemiology Study at the U.S. Air Force Academy: Using Severe Acute Respiratory Syndrome Coronavirus 2 to Test a Sentinel System forEarly Disease Outbreak Detection.

The presence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) in wastewater has been proposed as a sentinel surveillance epidemiological tool for detection of infectious disease at a community level and as a complementary approach to syndromic surveillance of infectious disease outbreaks. We have designed a study to test the presence and quantity of SARS-CoV2, the virus responsible for COVID19, in the wastewater treatment facility (WWTF) of the U.S. Air Force Academy. Wastewater samples were tested in the laboratory to quantify the amount of SARS-CoV2 RNA using reverse transcription-quantitative polymerase chain reaction. Raw SARS-CoV2 viral titer in wastewater was normalized to the viral titer of a fecal marker, pepper mild mottle virus, to correct for dilutions. Temporal and spatial trends of COVID19 were analyzed. Furthermore, we compared wastewater analysis results against clinical data to assist public health decisions. Preliminary data suggest that wastewater analysis can provide temporal and spatial trends of COVID19. The geographically discrete WWTF at the U.S. Air Force suggests that wastewater testing is a useful approach to developing a comprehensive sentinel surveillance system. Together with ongoing syndromic surveillance data, this proof-of-concept study seeks to determine whether early detection of SARS-CoV2 in a closed system WWTF correlates to changes in community and clinically reported COVID19. The well-documented population served by the geographically discrete WWTF at the U.S. Air Force Academy may serve to better elucidate the adjunctive role of wastewater testing in a comprehensive surveillance system. These results may be of particular interest to the DoD and local commanders given the WWTFs under their immediate control and the information that these studies may provide in support of operational readiness through early detection of disease outbreaks.

Wastewater-Based Epidemiology to Describe the Evolution of SARS-CoV-2 in the South-East of Spain, and Application of Phylogenetic Analysis and a Machine Learning Approach.

The COVID-19 pandemic has posed a significant global threat, leading to several initiatives for its control and management. One such initiative involves wastewater-based epidemiology, which has gained attention for its potential to provide early warning of virus outbreaks and real-time information on its spread. In this study, wastewater samples from two wastewater treatment plants (WWTPs) located in the southeast of Spain (region of Murcia), namely Murcia, and Cartagena, were analyzed using RT-qPCR and high-throughput sequencing techniques to describe the evolution of SARS-CoV-2 in the South-East of Spain. Additionally, phylogenetic analysis and machine learning approaches were applied to develop a pre-screening tool for the identification of differences among the variant composition of different wastewater samples. The results confirmed that the levels of SARS-CoV-2 in these wastewater samples changed concerning the number of SARS-CoV-2 cases detected in the population, and variant occurrences were in line with clinical reported data. The sequence analyses helped to describe how the different SARS-CoV-2 variants have been replaced over time. Additionally, the phylogenetic analysis showed that samples obtained at close sampling times exhibited a higher similarity than those obtained more distantly in time. A second analysis using a machine learning approach based on the mutations found in the SARS-CoV-2 spike protein was also conducted. Hierarchical clustering (HC) was used as an efficient unsupervised approach for data analysis. Results indicated that samples obtained in October 2022 in Murcia and Cartagena were significantly different, which corresponded well with the different virus variants circulating in the two locations. The proposed methods in this study are adequate for comparing consensus sequence types of the SARS-CoV-2 sequences as a preliminary evaluation of potential changes in the variants that are circulating in a given population at a specific time point.

Long-term SARS-CoV-2 surveillance in wastewater and estimation of COVID-19 cases: An application of wastewater-based epidemiology

The role of wastewater-based epidemiology (WBE), a powerful tool to complement clinical surveillance, has increased as many grassroots-level facilities, such as municipalities and cities, are actively involved in wastewater monitoring, and the clinical testing of coronavirus disease 2019 (COVID-19) is downscaled widely. This study aimed to conduct long-term wastewater surveillance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Yamanashi Prefecture, Japan, using one-step reverse transcription-quantitative polymerase chain reaction (RT-qPCR) assay and estimate COVID-19 cases using a cubic regression model that is simple to implement. Influent wastewater samples (n = 132) from a wastewater treatment plant were collected normally once weekly between September 2020 and January 2022 and twice weekly between February and August 2022. Viruses in wastewater samples (40 mL) were concentrated by the polyethylene glycol precipitation method, followed by RNA extraction and RT-qPCR. The K-6-fold cross-validation method was used to select the appropriate data type (SARS-CoV-2 RNA concentration and COVID-19 cases) suitable for the final model run. SARS-CoV-2 RNA was successfully detected in 67 % (88 of 132) of the samples tested during the whole surveillance period, 37 % (24 of 65) and 96 % (64 of 67) of the samples collected before and during 2022, respectively, with concentrations ranging from 3.5 to 6.3 log10 copies/L. This study applied a nonnormalized SARS-CoV-2 RNA concentration and nonstandardized data for running the final 14-day (1 to 14 days) offset models to estimate weekly average COVID-19 cases. Comparing the parameters used for a model evaluation, the best model showed that COVID-19 cases lagged 3 days behind the SARS-CoV-2 RNA concentration in wastewater samples during the Omicron variant phase (year 2022). Finally, 3- and 7-day offset models successfully predicted the trend of COVID-19 cases from September 2022 until February 2023, indicating the applicability of WBE as an early warning tool.

A time series based machine learning strategy for wastewater-based forecasting and nowcasting of COVID-19 dynamics

Monitoring COVID-19 infection cases has been a singular focus of many policy makers and communities. However, direct monitoring through testing has become more onerous for a number of reasons, such as costs, delays, and personal choices. Wastewater-based epidemiology (WBE) has emerged as a viable tool for monitoring disease prevalence and dynamics to supplement direct monitoring. The objective of this study is to intelligently incorporate WBE information to nowcast and forecast new weekly COVID-19 cases and to assess the efficacy of such WBE information for these tasks in an interpretable manner. The methodology consists of a time-series based machine learning (TSML) strategy that can extract deeper knowledge and insights from temporal structured WBE data in the presence of other relevant temporal variables, such as minimum ambient temperature and water temperature, to boost the capability for predicting new weekly COVID-19 case numbers. The results confirm that feature engineering and machine learning can be utilized to enhance the performance and interpretability of WBE for COVID-19 monitoring, along with identifying the different recommended features to be applied for short-term and long-term nowcasting and short-term and long-term forecasting. The conclusion of this research is that the proposed time-series ML methodology performs as well, and sometimes better, than simple predictions that assume available and accurate COVID-19 case numbers from extensive monitoring and testing. Overall, this paper provides an insight into the prospects of machine learning based WBE to the researchers and decision-makers as well as public health practitioners for predicting and preparing the next wave of COVID-19 or the next pandemic.

Festivals following the easing of COVID-19 restrictions: Prevalence of new psychoactive substances and illicit drugs

The market for illicit drugs and new psychoactive substances (NPS) has grown significantly and people attending festivals have been identified as being at high risk (high extent and frequency of substance use). Traditional public health surveillance data sources have limitations (high costs, long implementation times, and ethical issues) and wastewater-based epidemiology (WBE) can cost-effectively support surveillance efforts. Influent wastewater samples were analyzed for NPS and illicit drug consumption collected during New Year period (from 29-Dec-2021 to 4-Jan-2022) and a summer Festival (from 29-June-2022 to 12-July-2022) in a large city in Spain. Samples were analyzed for phenethylamines, cathinones, opioids, benzodiazepines, plant-based NPS, dissociatives, and the illicit drugs methamphetamine, MDA, MDMA, ketamine, heroin, cocaine, and pseudoephedrine by liquid chromatography mass spectrometry. High consumption rates of specific NPS and established illicit drugs were identified at the peak of each event. Furthermore, a dynamic change in NPS use (presence and absence of substances) was detected over a period of six months. Eleven NPS, including synthetic cathinones, benzodiazepines, plant-based NPS and dissociatives, and seven illicit drugs were found across both the New Year and summer Festival. Statistically significant differences (p < 0.05) were seen for 3-MMC (New Year vs summer Festival), eutylone (New Year vs summer Festival), cocaine (summer Festival vs normal week and summer Festival vs New Year), MDMA (New Year vs normal week and summer Festival vs normal week), heroin (summer Festival vs New Year) and pseudoephedrine (summer Festival vs New Year). This WBE study assessed the prevalence of NPS and illicit drugs at festivals following the reduction of the COVID-19 pandemic restrictions highlighting the high use of specific substances at the peak of each event. This approach identified in a cost-effective and timely manner without any ethical issues the most used drugs and changes in use patterns and, thus, can complement public health information.

Investigation of New Psychoactive Substances (NPS), Other Illicit Drugs, and Drug-Related Compounds in a Taiwanese Wastewater Sample Using High-Resolution Mass-Spectrometry-Based Targeted and Suspect Screening

The proliferation of new psychoactive substances (NPSs) in recent years has posed a significant challenge to public health. Traditional monitoring methods have proven insufficient in tracking these constantly evolving substances, leading to the development of alternative approaches such as wastewater-based epidemiology (WBE). The present study aims to utilize high-resolution mass spectrometry (HRMS)-based targeted and suspect screening to profile NPS, other illicit drugs, and drug-related compounds in a Taiwanese wastewater sample. For the targeted analysis, 8 out 18 standards of illicit drugs have been identified. The suspect screening approach based on approximately 3600 substances in the SWGDRUG library can further identify 92 compounds, including opiate analgesics, synthetic cathinones, phenylalkylamines derivatives, phenethylamine derivatives, tryptamine derivatives, steroids, and ephedrine-related compounds. Additionally, the presence of 5-methoxy-2-aminoindane (MEAI) in the wastewater indicates that drug dealers have recently sold this potential NPS to evade drug regulations. This study firstly reports the HRMS-based comprehensive profile of NPS, other illicit drugs, and drug-related compounds in Taiwan, which could be applied as biomarkers for estimating the consumption of drugs.