SARS-CoV-2 concentrations in a wastewater collection system indicated potential COVID-19 hotspots at the zip code level

Comparative assessment of sewer sampling methods for infectious disease surveillance: Insights from transport modeling and simulations of SARS-CoV-2 emissions

Emerging infectious diseases like COVID-19 present significant public health challenges, necessitating effective surveillance methods. Wastewater-based epidemiology (WBE), detecting viral pathogens in wastewater, has emerged as a proactive tool for monitoring infections. This study evaluated various wastewter sampling methods through SARS-CoV-2 transport simulations in an urban sewer network in Sejong City, South Korea, to identify cost-effective strategies for accurate infection monitoring. Using the U.S. EPA's Storm Water Management Model (SWMM) and Markov chain Monte Carlo (MCMC) sampling, we simulated wastewater flow and viral concentrations based on reported COVID-19 case data for the year 2021. In this study, we used reported COVID-19 cases as a hypothetical estimate of the number of infected individuals in the simulation. The SWMM effectively replicated daily and monthly patterns in sewer flow rates. Combining the SWMM with MCMC sampling from the probability distributions of spatio-temporal virus emission patterns, we generated an ensemble time series dataset of hourly virus concentrations based on 200 simulations, forming the basis for evaluating sampling alternatives. Results showed a strong correlation (R2 = 0.81) between daily average virus concentrations and daily infection rates on the fifth day following new infections, consistent with simulated viral emission patterns. Flow-weighted and equally timed sampling methods provided highly reliable infection pattern estimates, suggesting that equally timed sampling is a cost-effective alternative. In contrast, grab sampling performed poorly due to difficulties in capturing peak viral emission periods. We found that a minimum sampling duration of four to six hours was crucial for accurate detection, with performance increasing if the sampling was applied in the morning (R2 ≈ 0.7). Longer durations steadily, but only slightly, improved results. While this simulation-based approach focused on predicting daily virus concentration patterns in wastewater rather than precisely estimating its absolute levels, it provides valuable insights for optimizing WBE in public health surveillance and underscores the need for further validation with real-world data.

Investigating illicit drug hotspots and daily variations using sewer-network wastewater analysis

Previous wastewater-based epidemiology (WBE) research on illicit drug use has predominantly focused on wastewater treatment plant (WWTP) influents, but information on sewer-network wastewater is very limited. This study represents a pioneering small-scale WBE investigation based on the analysis of sewer-network wastewater samples from different sewer manholes in suburban (Tamsui region) and urban areas (Zhongshan and Wanhua regions) and a comparison of the results with those obtained from corresponding WWTP influents. Among sixteen illicit drugs, methamphetamine exhibited the highest concentration in sewer-network wastewater across both areas. Suburban-urban variations were observed, with more types of illicit drugs detected in the suburban area. Back-calculation indicated that methamphetamine and ketamine were the most-consumed illicit drugs in both sewer-network wastewaters and WWTP influents. Similar types of illicit drugs were detected in the sewer-network wastewaters and WWTP influents, indicating the representativeness of WWTP influents in assessing regional illicit drug abuse. Nevertheless, the sewer-network wastewater results offered additional information making it possible to pinpoint potential hotspots of illicit drug and identify peak usage periods throughout the day, in contrast to the WWTP influent results. In the non-suspected suburban area of Tamsui, high potential hotspots of methamphetamine (sampling points 3 and 6) and ketamine (sampling points 1 and 8) were identified. Although the Zhongshan and Wanhua regions were chosen as suspected hotspots of illicit drug abuse, more severe illicit drug use was observed in Wanhua. Moreover, a trend toward higher illicit drug use from early morning to morning was observed. Despite sampling challenges and higher costs, small-scale WBE via sewer-network wastewater analysis provides superior identification of drug abuse hotspots and peak usage periods. Therefore, this study provides valuable insights for law enforcement and can help prevent and combat illicit drug abuse by targeting potential hotspots and understanding daily illicit drug use dynamics.

Small-scale wastewater-based epidemiology (WBE) for infectious diseases and antibiotic resistance: A scoping review

Wastewater analysis can serve as a source of public health information. In recent years, wastewater-based epidemiology (WBE) has emerged and proven useful for the detection of infectious diseases. However, insights from the wastewater treatment plant do not allow for the small-scale differentiation within the sewer system that is needed to analyze the target population under study in more detail. Small-scale WBE offers several advantages, but there has been no systematic overview of its application. The aim of this scoping review is to provide a comprehensive overview of the current state of knowledge on small-scale WBE for infectious diseases, including methodological considerations for its application. A systematic database search was conducted, considering only peer-reviewed articles. Data analyses included quantitative summary and qualitative narrative synthesis. Of 2130 articles, we included 278, most of which were published since 2020. The studies analyzed wastewater at the building level (n = 203), especially healthcare (n = 110) and educational facilities (n = 80), and at the neighborhood scale (n = 86). The main analytical parameters were viruses (n = 178), notably SARS-CoV-2 (n = 161), and antibiotic resistance (ABR) biomarkers (n = 99), often analyzed by polymerase chain reaction (PCR), with DNA sequencing techniques being less common. In terms of sampling techniques, active sampling dominated. The frequent lack of detailed information on the specification of selection criteria and the characterization of the small-scale sampling sites was identified as a concern. In conclusion, based on the large number of studies, we identified several methodological considerations and overarching strategic aspects for small-scale WBE. An enabling environment for small-scale WBE requires inter- and transdisciplinary knowledge sharing across countries. Promoting the adoption of small-scale WBE will benefit from a common international conceptualization of the approach, including standardized and internationally accepted terminology. In particular, the development of good WBE practices for different aspects of small-scale WBE is warranted. This includes the establishment of guidelines for a comprehensive characterization of the local sewer system and its sub-sewersheds, and transparent reporting to ensure comparability of small-scale WBE results.

Identifying spatiotemporal trends of SARS-CoV-2 RNA in wastewater: from the perspective of upstream and downstream wastewater-based epidemiology (WBE)

Recently, many efforts have been made to address the rapid spread of newly identified COVID-19 virus variants. Wastewater-based epidemiology (WBE) is considered a potential early warning tool for identifying the rapid spread of this virus. This study investigated the occurrence of SARS-CoV-2 in eight wastewater treatment plants (WWTPs) and their sewerage systems which serve most of the population in Taoyuan City, Taiwan. Across the entire study period, the wastewater viral concentrations were correlated with the number of COVID-19 cases in each WWTP (Spearman's r = 0.23-0.76). In addition, it is confirmed that several treatment technologies could effectively eliminate the virus RNA from WWTP influent (> 90%). On the other hand, further results revealed that an inverse distance weighted (IDW) interpolation and hotspot model combined with the geographic information system (GIS) method could be applied to analyze the spatiotemporal variations of SARS-CoV-2 in wastewater from the sewer system. In addition, socio-economic factors, namely, population density, land use, and income tax were successfully identified as the potential drivers which substantially affected the onset of the COVID-19 outbreak in Taiwan. Finally, the data obtained from this study can provide a powerful tool in public health decision-making not only in response to the current epidemic situation but also to other epidemic issues in the future.

Zooming in to the neighborhood level: A year-long wastewater-based epidemiology monitoring campaign for COVID-19 in small intraurban catchments

In recent years, wastewater-based epidemiology (WBE) has emerged as a valuable and cost-effective tool for monitoring the prevalence of COVID-19. Large-scale monitoring efforts have been implemented in numerous countries, primarily focusing on sampling at the entrance of wastewater treatment plants (WWTPs) to cover a large population. However, sampling at a finer spatial scale, such as at the neighborhood level (NGBs), pose new challenges, including the absence of composite sampling infrastructure and increased uncertainty due to the dynamics of small catchments. This study aims to investigate the feasibility and accuracy of WBE when deployed at the neighborhood level (sampling in sewers) compared to the city level (sampling at the entrance of a WWTP). To achieve this, we deployed specific WBE sampling stations at the intraurban scale within three NGBs in Barcelona, Spain. The study period covers the 5th and the 6th waves of COVID-19 in Spain, spanning from March 2021 to March 2022, along with the WWTP downstream from the NGBs. The results showed a strong correlation between the dynamics of COVID-19 clinical cases and wastewater SARS-CoV-2 loads at both the NGB and city levels. Notably, during the 5th wave, which was dominated by the Delta SARS-CoV-2 variant, wastewater loads were higher than during the 6th wave (Omicron variant), despite a lower number of clinical cases recorded during the 5th wave. The correlations between wastewater loads and clinical cases at the NGB level were stronger than at the WWTP level. However, the early warning potential varied across neighborhoods and waves, with some cases showing a one-week early warning and others lacking any significant early warning signal. Interestingly, the prevalence of COVID-19 did not exhibit major differences among NGBs with different socioeconomic statuses.

Structured Ethical Review for Wastewater-Based Testing in Support of Public Health

Wastewater-based testing (WBT) for SARS-CoV-2 has rapidly expanded over the past three years due to its ability to provide a comprehensive measurement of disease prevalence independent of clinical testing. The development and simultaneous application of WBT measured biomarkers for research activities and for the pursuit of public health goals, both areas with well-established ethical frameworks. Currently, WBT practitioners do not employ a standardized ethical review process, introducing the potential for adverse outcomes for WBT professionals and community members. To address this deficiency, an interdisciplinary workshop developed a framework for a structured ethical review of WBT. The workshop employed a consensus approach to create this framework as a set of 11 questions derived from primarily public health guidance. This study retrospectively applied these questions to SARS-CoV-2 monitoring programs covering the emergent phase of the pandemic (3/2020-2/2022 (n = 53)). Of note, 43% of answers highlight a lack of reported information to assess. Therefore, a systematic framework would at a minimum structure the communication of ethical considerations for applications of WBT. Consistent application of an ethical review will also assist in developing a practice of updating approaches and techniques to reflect the concerns held by both those practicing and those being monitored by WBT supported programs.

Structured Ethical Review for Wastewater-Based Testing

Wastewater-based testing (WBT) for SARS-CoV-2 has rapidly expanded over the past three years due to its ability to provide a comprehensive measurement of disease prevalence independent of clinical testing. The development and simultaneous application of the field blurred the boundary between measuring biomarkers for research activities and for pursuit of public health goals, both areas with well-established ethical frameworks. Currently, WBT practitioners do not employ a standardized ethical review process (or associated data management safeguards), introducing the potential for adverse outcomes for WBT professionals and community members. To address this deficiency, an interdisciplinary group developed a framework for a structured ethical review of WBT. The workshop employed a consensus approach to create this framework as a set of 11-questions derived from primarily public health guidance because of the common exemption of wastewater samples to human subject research considerations. This study retrospectively applied the set of questions to peer- reviewed published reports on SARS-CoV-2 monitoring campaigns covering the emergent phase of the pandemic from March 2020 to February 2022 (n=53). Overall, 43% of the responses to the questions were unable to be assessed because of lack of reported information. It is therefore hypothesized that a systematic framework would at a minimum improve the communication of key ethical considerations for the application of WBT. Consistent application of a standardized ethical review will also assist in developing an engaged practice of critically applying and updating approaches and techniques to reflect the concerns held by both those practicing and being monitored by WBT supported campaigns.

Abstract Figure

Synopsis

Development of a structured ethical review facilitates retrospective analysis of published studies and drafted scenarios in the context of wastewater-based testing.

Surveillance of SARS-CoV-2 in sewage from buildings housing residents with different vulnerability levels

During the last three years, various restrictions have been set up to limit the transmission of the Coronavirus Disease (COVID-19). While these rules apply at a large scale (e.g., country-wide level) human-to-human transmission of the virus that causes COVID-19, the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), occurs at a small scale. Different preventive policies and testing protocols were implemented in buildings where COVID-19 poses a threat (e.g., elderly residences) or constitutes a disruptive force (e.g., schools). In this study, we sampled sewage from different buildings (a school, a university campus, a university residence, and an elderly residence) that host residents of different levels of vulnerability. Our main goal was to assess the agreement between the SARS-CoV-2 concentration in wastewater and the policies applied in these buildings. All buildings were sampled using passive samplers while 24 h composite samples were also collected from the elderly residence. Results showed that passive samplers performed comparably well to composite samples while being cost-effective to keep track of COVID-19 prevalence. In the elderly residence, the comparison of sampling protocols (passive vs. active) combined with the strict clinical testing allowed us to compare the sensitivities of the two methods. Active sampling was more sensitive than passive sampling, as the former was able to detect a COVID-19 prevalence of 0.4 %, compared to a prevalence of 2.2 % for passive sampling. The number of COVID-19-positive individuals was tracked clinically in all the monitored buildings. More frequent detection of SARS-CoV-2 in wastewater was observed in residential buildings than in non-residential buildings using passive samplers. In all buildings, sewage surveillance can be used to complement COVID-19 clinical testing regimes, as the detection of SARS-CoV-2 in wastewater remained positive even when no COVID-19-positive individuals were reported. Passive sampling is useful for building managers to adapt their COVID-19 mitigation policies.

Contribution of wastewater-based epidemiology to SARS-CoV-2 screening in Brazil and the United States

Wastewater-based epidemiology (WBE) is a valuable tool for investigating the existence, prevalence, and spread of pathogens, such as SARS-CoV-2, in a given population. WBE, proposed as part of the SARS-CoV-2 surveillance strategy for monitoring virus circulation, may complement clinical data and contribute to reducing the spread of the disease through early detection. In developing countries such as Brazil, where clinical data are scarce, information obtained from wastewater monitoring can be crucial for designing public health interventions. In the United States, the country with the largest number of confirmed SARS-CoV-2 cases worldwide, WBE programs have begun to be carried out to investigate correlations with coronavirus disease 2019 (COVID-19) clinical data and support health agencies in decision-making to prevent the spread of the disease. This systematic review aimed to assess the contribution of WBE to SARS-CoV-2 screening in Brazil and the United States and compare studies conducted in a developed and developing country. Studies in Brazil and the United States showed WBE to be an important epidemiological surveillance strategy in the context of the COVID-19 pandemic. WBE approaches are useful for early detection of COVID-19 outbreaks, estimation of clinical cases, and assessment of the effectiveness of vaccination program.

When case reporting becomes untenable: Can sewer networks tell us where COVID-19 transmission occurs?

Monitoring SARS-CoV-2 in wastewater is a valuable approach to track COVID-19 transmission. Designing wastewater surveillance (WWS) with representative sampling sites and quantifiable results requires knowledge of the sewerage system and virus fate and transport. We developed a multi-level WWS system to track COVID-19 in Atlanta using an adaptive nested sampling strategy. From March 2021 to April 2022, 868 wastewater samples were collected from influent lines to wastewater treatment facilities and upstream community manholes. Variations in SARS-CoV-2 concentrations in influent line samples preceded similar variations in numbers of reported COVID-19 cases in the corresponding catchment areas. Community sites under nested sampling represented mutually-exclusive catchment areas. Community sites with high SARS-CoV-2 detection rates in wastewater covered high COVID-19 incidence areas, and adaptive sampling enabled identification and tracing of COVID-19 hotspots. This study demonstrates how a well-designed WWS provides actionable information including early warning of surges in cases and identification of disease hotspots.

Optimization of sewage sampling for wastewater-based epidemiology through stochastic modeling

The proliferation of the SARS-CoV-2 global pandemic has brought to attention the need for epidemiological tools that can detect diseases in specific geographical areas through non-contact means. Such methods may protect those potentially infected by facilitating early quarantine policies to prevent the spread of the disease. Sampling of municipal wastewater has been studied as a plausible solution to detect pathogen spread, even from asymptomatic patients. However, many challenges exist in wastewater-based epidemiology such as identifying a representative sample for a population, determining the appropriate sample size, and establishing the right time and place for samples. In this work, a new approach to address these questions is assessed using stochastic modeling to represent wastewater sampling given a particular community of interest. Using estimates for various process parameters, inferences on the population infected are generated with Monte Carlo simulation output. A case study at the University of Oklahoma is examined to calibrate and evaluate the model output. Finally, extensions are provided for more efficient wastewater sampling campaigns in the future. This research provides greater insight into the effects of viral load, the percentage of the population infected, and sampling time on mean SARS-CoV-2 concentration through simulation. In doing so, an earlier warning of infection for a given population may be obtained and aid in reducing the spread of viruses.

Application of neighborhood-scale wastewater-based epidemiology in low COVID-19 incidence situations

Wastewater-based epidemiology (WBE), an emerging approach for community-wide COVID-19 surveillance, was primarily characterized at large sewersheds such as wastewater treatment plants serving a large population. Although informed public health measures can be better implemented for a small population, WBE for neighborhood-scale sewersheds is less studied and not fully understood. This study applied WBE to seven neighborhood-scale sewersheds (average population of 1471) from January to November 2021. Community testing data showed an average of 0.004 % incidence rate in these sewersheds (97 % of monitoring periods reported two or fewer daily infections). In 92 % of sewage samples, SARS-CoV-2 N gene fragments were below the limit of quantification. We statistically determined 10-2.6 as the threshold of the SARS-CoV-2 N gene concentration normalized to pepper mild mottle virus (N/PMMOV) to alert high COVID-19 incidence rate in the studied sewershed. This threshold of N/PMMOV identified neighborhood-scale outbreaks (COVID-19 incidence rate higher than 0.2 %) with 82 % sensitivity and 51 % specificity. Importantly, neighborhood-scale WBE can discern local outbreaks that would not otherwise be identified by city-scale WBE. Our findings suggest that neighborhood-scale WBE is an effective community-wide disease surveillance tool when COVID-19 incidence is maintained at a low level.

Optimal allocation and operation of sewer monitoring sites for wastewater-based disease surveillance: A methodological proposal

Wastewater-based epidemiology (WBE) is drawing increasing attention as a promising tool for an early warning of emerging infectious diseases such as COVID-19. This study demonstrated the utility of a spatial bisection method (SBM) and a global optimization algorithm (i.e., genetic algorithm, GA), to support better designing and operating a WBE program for disease surveillance and source identification. The performances of SBM and GA were compared in determining the optimal locations of sewer monitoring manholes to minimize the difference among the effective spatial monitoring scales of the selected manholes. While GA was more flexible in determining the spatial resolution of the monitoring areas, SBM allows stepwise selection of optimal sampling manholes with equiareal subcatchments and lowers computational cost. Upon detecting disease outbreaks at a regular sewer monitoring site, additional manholes within the catchment can be selected and monitored to identify source areas with a required spatial resolution. SBM offered an efficient method for rapidly searching for the optimal locations of additional sampling manholes to identify the source areas. This study provides strategic and technical elements of WBE including sampling site selection with required spatial resolution and a source identification method.

Wastewater-based epidemiology (WBE) for SARS-CoV-2 - A review focussing on the significance of the sewer network using a Dublin city catchment case study

Wastewater-based epidemiology (WBE) has been employed by many countries globally since the beginning of the COVID-19 pandemic in order to assess the benefits of this surveillance tool in the context of informing public health measures. WBE has been successfully employed to detect SARS-CoV-2 at wastewater treatment plants for community-wide surveillance, as well as in smaller catchments and institutions for targeted surveillance of COVID-19. In addition, WBE has been successfully used to detect new variants, identify areas of high infection levels, as well as to detect new infection outbreaks. However, due to to the large number of inherent uncertainties in the WBE process, including the inherent intricacies of the sewer network, decay of the virus en route to a monitoring point, levels of recovery from sampling and quantification methods, levels of faecal shedding among the infected population, as well as population normalisation methods, the usefulness of wastewater samples as a means of accurately quantifying SARS-CoV-2 infection levels among a population remains less clear. The current WBE programmes in place globally will help to identify new areas of research aimed at reducing the levels of uncertainty in the WBE process, thus improving WBE as a public health monitoring tool for future pandemics. In the meantime, such programmes can provide valuable comparisons to clinical testing data and other public health metrics, as well being an effective early warning tool for new variants and new infection outbreaks. This review includes a case study of sampled wastewater from the sewer network in Dublin, Ireland, during a peak infection period of COVID-19 in the city, which evaluates the different uncertainties in the WBE process.

Longitudinal SARS-CoV-2 RNA wastewater monitoring across a range of scales correlates with total and regional COVID-19 burden in a well-defined urban population

Wastewater-based epidemiology (WBE) is an emerging surveillance tool that has been used to monitor the ongoing COVID-19 pandemic by tracking SARS-CoV-2 RNA shed into wastewater. WBE was performed to monitor the occurrence and spread of SARS-CoV-2 from three wastewater treatment plants (WWTP) and six neighborhoods in the city of Calgary, Canada (population 1.44 million). A total of 222 WWTP and 192 neighborhood samples were collected from June 2020 to May 2021, encompassing the end of the first-wave (June 2020), the second-wave (November end to December 2020) and the third-wave of the COVID-19 pandemic (mid-April to May 2021). Flow-weighted 24-hour composite samples were processed to extract RNA that was then analyzed for two SARS-CoV-2-specific regions of the nucleocapsid gene, N1 and N2, using reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Using this approach SARS-CoV-2 RNA was detected in 98.06% (406/414) of wastewater samples. SARS-CoV-2 RNA abundance was compared to clinically diagnosed COVID-19 cases organized by the three-digit postal code of affected individuals' primary residences, enabling correlation analysis at neighborhood, WWTP and city-wide scales. Strong correlations were observed between N1 & N2 gene signals in wastewater and new daily cases for WWTPs and neighborhoods. Similarly, when flow rates at Calgary's three WWTPs were used to normalize observed concentrations of SARS-CoV-2 RNA and combine them into a city-wide signal, this was strongly correlated with regionally diagnosed COVID-19 cases and clinical test percent positivity rate. Linked census data demonstrated disproportionate SARS-CoV-2 in wastewater from areas of the city with lower socioeconomic status and more racialized communities. WBE across a range of urban scales was demonstrated to be an effective mechanism of COVID-19 surveillance.

Higher incidence of novel coronavirus (COVID-19) cases in areas with combined sewer systems, heavy precipitation, and high percentages of impervious surfaces

Combined sewer systems (CSS) are water management systems that collect and transport stormwater and sewer water in the same pipes. During large storm events, stormwater runoff may exceed the capacity of the system and lead to combined sewer overflows (CSOs), where untreated sewer and stormwater are released into the environment. Though current literature reveals inconclusive evidence regarding the infectivity of SARS-CoV-2 in wastewater, detection of infectious SARS-CoV-2 in urine and feces of COVID-19 patients led to concerns that areas contaminated by CSOs may be a reservoir of SARS-CoV-2 and may result in illness after the ingestion and/or inhalation of contaminated splashes, droplets, or aerosols. We investigated the association between COVID-19 incidence and CSSs and whether this association differed by precipitation and percent impervious surfaces as a proxy for possible CSOs. We fitted a quasi-Poisson regression model to estimate the change in percentage of incidence rate of COVID-19 cases in counties with a CSS compared to those without, adjusting for potential confounders (i.e., state, population density, date of first documented COVID-19 case, social vulnerability, and percent vaccinated) and including interaction variables between CSS, precipitation, and impervious surfaces. Our findings suggest that heavy precipitation in combination with high percentages of imperviousness is associated with higher incidences of COVID-19 cases in counties with a CSS compared to in counties without (p-value = 2.5e-9). For example, CSS-counties with precipitation of 10 in/month may observe a higher incidence in COVID-19 cases compared to non-CSS counties if their impervious surfaces exceed 33.5% [95%CI: 23.0%, 60.0%]. We theorize that more COVID-19 cases may be seen in counties with a CSS, heavy precipitation, and high percentages of impervious surfaces because of the possible increase in frequency and severity of CSOs. The results suggest links between climate change, urbanization, and COVID-19.