Wastewater monitoring outperforms case numbers as a tool to track COVID-19 incidence dynamics when test positivity rates are high

Predicting COVID-19 in Ohio: Insights from wastewater, demographic and socioeconomic data

More than four years into the COVID-19 pandemic, clear patterns have emerged showing that the virus does not affect all populations uniformly. Demographic and socioeconomic disparities play a significant role in the vulnerability to and spread of SARS-CoV-2. Analyzing these disparities can offer insights into the pandemic's dynamics, helping to identify critical factors that need to be addressed in efforts to mitigate the pandemic's impact globally. Wastewater-based surveillance (WBS), a crucial tool for tracking the virus, offers a unique perspective on how socioeconomic and demographic factors might influence infection rates across different communities. However, estimating and predicting the extent of the epidemic from WBS results is still challenging. In our study, we tried to address these challenges by analyzing data from 55 sites in Ohio, USA, with populations ranging from 3300 to 654,817, to better understand the pandemic's dynamics and WBS effectiveness in monitoring COVID-19 spread. Factors such as population size, poverty rate, racial demographics (specifically white and black populations), and median income showed the strongest correlations with both clinical cases and wastewater results, with population size being the most important factor. Moreover, among eight evaluated machine learning models, k-Nearest Neighbors (R2 = 0.873), Random Forest (R2 = 0.862), and XGBoost (R2 = 0.854) were the most effective in predicting clinical cases from WBS data across demographic and socioeconomic categories, while Linear (R2 = 0.578) and Ridge+Linear (R2 = 0.595) were least effective. Thus, these findings highlight the potential of machine learning to predict COVID-19 cases from WBS data across a wide range of demographic and socioeconomic categories.

Relationship between antihistamine residues in wastewater and airborne pollen concentrations: Insights into population-scale pollinosis response

Pollinosis is the most prevalent allergic disorder. Assessing the impact of real-world pollen exposure on symptoms remains challenging due to extensive patient-level efforts required. This study explores the potential of wastewater-based epidemiology (WBE) to investigate the relationship between airborne pollen concentrations and antihistamine residues in wastewater as an indicator of pollinosis symptom treatment at the population-scale. In Zurich (Switzerland), 279 wastewater samples were collected during 2021-2023. Each sample represents a 24-h period with excreta from approximately 471,000 individuals. Eleven antihistamine markers were analyzed in the samples using liquid chromatography high-resolution mass spectrometry. The relationship between antihistamine loads in wastewater and airborne pollen concentrations (47 taxa and miscellaneous pollen) was investigated by determining seasonal components of antihistamine loads and exploring pollen-specific contributions using Non-Negative Least Squares (NNLS) optimization. Four antihistamines were detected in quantifiable amounts in wastewater. The first-generation antihistamine, diphenhydramine, demonstrated rather constant levels. In contrast, the three second-generation antihistamines - bilastine, cetirizine, and fexofenadine - showed pronounced day-to-day variation with strong correlations among each other. For fexofenadine, which was investigated in detail for its correlation with airborne pollen concentrations, approximately 50 % of the annual wastewater loads were associated with acute pollen exposure. Another 20 % related to baseline consumption during the pollen season, while the remaining 30 % seems unrelated to pollen. Birch, grasses, hazel, hornbeam, plane, and plantain explained most of the variance in wastewater loads (R2 = 0.82), with grass pollen alone accounting for a quarter of the annual loads. Increased fexofenadine loads during periods without elevated concentrations of common allergenic pollen suggests the presence of additional triggers for allergy symptoms, potentially yew pollen. Our study demonstrates that WBE can effectively reveal substantial day-to-day variation in antihistamine use related to pollen exposure. Thus, WBE presents an objective and questionnaire-independent method for investigating pollinosis symptom treatment at a population-scale.

Estimating the trend of COVID-19 in Norway by combining multiple surveillance indicators

Estimating the trend of new infections was crucial for monitoring risk and for evaluating strategies and interventions during the COVID-19 pandemic. The pandemic revealed the utility of new data sources and highlighted challenges in interpreting surveillance indicators when changes in disease severity, testing practices or reporting occur. Our study aims to estimate the underlying trend in new COVID-19 infections by combining estimates of growth rates from all available surveillance indicators in Norway. We estimated growth rates by using a negative binomial regression method and aligned the growth rates in time to hospital admissions by maximising correlations. Using a meta-analysis framework, we calculated overall growth rates and reproduction numbers including assessments of the heterogeneity between indicators. We find that the estimated growth rates reached a maximum of 25% per day in March 2020, but afterwards they were between -10% and 10% per day. The correlations between the growth rates estimated from different indicators were between 0.5 and 1.0. Growth rates from indicators based on wastewater, panel and cohort data can give up to 14 days earlier signals of trends compared to hospital admissions, while indicators based on positive lab tests can give signals up to 7 days earlier. Combining estimates of growth rates from multiple surveillance indicators provides a useful description of the COVID-19 pandemic in Norway. This is a powerful technique for a holistic understanding of the trends of new COVID-19 infections and the technique can easily be adapted to new data sources and situations.

Localised wastewater SARS-CoV-2 levels linked to COVID-19 cases: A long-term multisite study in England

Wastewater-based surveillance (WBS) can monitor for the presence of human health pathogens in the population. During COVID-19, WBS was widely used to determine wastewater SARS-CoV-2 RNA concentration (concentrations) providing information on community COVID-19 cases (cases). However, studies examining the relationship between concentrations and cases tend to be localised or focussed on small-scale institutional settings. Few have examined this relationship in multiple settings, over long periods, with large sample numbers, nor attempted to quantify the relationship between concentrations and cases or detail how catchment characteristics affected these. This 18-month study (07/20-12/21) explored the correlation and quantitative relationship between concentrations and cases using censored regression. Our analysis used >94,000 wastewater samples collected from 452 diverse sampling sites (259 Sewage Treatment Works (STW) and 193 Sewer Network Sites (SNS)) covering ~65 % of the English population. Wastewater concentrations were linked to ~6 million diagnostically confirmed COVID-19 cases. High correlation coefficients were found between concentrations and cases (STW: median r = 0.66, IQR: 0.57-0.74; SNS: median r = 0.65, IQR: 0.54-0.74). The quantitative relationship (regression coefficient) between concentrations and cases was variable between catchments. Catchment and sampling characteristics (e.g. size of population and grab vs automated sampling) had significant but small effects on correlation and regression coefficients. During the last six months of the study correlation coefficients reduced and regression coefficients became highly variable between catchments. This coincided with a shift towards younger cases, a highly vaccinated population and rapid emergence of the variant Omicron. The English WBS programme was rapidly introduced at scale during COVID-19. Laboratory methods evolved and study catchments were highly diverse in size and characteristics. Despite this diversity, findings indicate that WBS provides an effective proxy for establishing COVID-19 dynamics across a wide variety of communities. While there is potential for predicting COVID-19 cases from wastewater concentration, this may be more effective at smaller scales.

Temporal, spatial, and methodological considerations in evaluating the viability of measles wastewater surveillance

Measles is a highly transmissible disease of increasing concern due to waning vaccination contributing to a significant rise in measles cases, with 283 reported cases and 16 outbreaks in the U.S. as of November 7, 2024. Early identification of measles cases is thus critical to disease containment and control. Wastewater-based epidemiology (WBE) represents a potential strategy for the efficient identification of measles outbreaks. We investigated the suitability of WBE for measles outbreak identification by using a model-based approach to elucidate the relationship between measles shedding, wastewater concentration, and detectability. The model reveals conditions for effective detection, specifying the optimal timing, location, and methodology needed to achieve a specific probability of detection, including accounting for geographic variability of wastewater generation and measles case rates. Measles RNA shedding, primarily from urine, contributes an average of 8.72 log10 genome copies (GC) daily per infection into sewage. At the average U.S. wastewater treatment plant (WWTP), achieving a 50 % probability of detection requires approximately 78 cases per 100,000 people with a process limit of detection (PLOD) of 3.0 log10 GC/L. At a PLOD of 3.0 log10 GC/L, over half of all WWTPs in the world can detect a single hypothetical measles case at a 10 % probability of detection. However, achieving a 50-90 % detection rate is challenging, especially with a higher PLOD, except in areas with the highest measles cases. Some locations require case levels consistent with a complete lack of vaccination for feasible measles detection in wastewater. Future work exploring measles shedding, variable shedding behavior, and local case rates can enhance model predictions. Overall, this analysis suggests that WBE detection of measles in most locations remains challenging without a significant increase in case rates or technical improvements decreasing the PLOD.

Epidemiological model can forecast COVID-19 outbreaks from wastewater-based surveillance in rural communities

Wastewater has emerged as a crucial tool for infectious disease surveillance, offering a valuable means to bridge the equity gap between underserved communities and larger urban municipalities. However, using wastewater surveillance in a predictive manner remains a challenge. In this study, we tested if detecting SARS-CoV-2 in wastewater can forecast outbreaks in rural communities. Under the CDC National Wastewater Surveillance program, we monitored the SARS-CoV-2 in the wastewater of five rural communities and a small city in Idaho (USA). We then used a particle filter method coupled with a stochastic susceptible-exposed-infectious-recovered (SEIR) model to infer active case numbers using quantities of SARS-CoV-2 in wastewater. Our findings revealed that while high daily variations in wastewater viral load made real-time interpretation difficult, the SEIR model successfully factored out this noise, enabling accurate forecasts of the Omicron outbreak in five of the six towns shortly after initial increases in SARS-CoV-2 concentrations were detected in wastewater. The model predicted outbreaks with a lead time of 0 to 11 days (average of 6 days +/- 4) before the surge in reported clinical cases. This study not only underscores the viability of wastewater-based epidemiology (WBE) in rural communities-a demographic often overlooked in WBE research-but also demonstrates the potential of advanced epidemiological modeling to enhance the predictive power of wastewater data. Our work paves the way for more reliable and timely public health guidance, addressing a critical gap in the surveillance of infectious diseases in rural populations.

Wastewater-based surveillance for Hepatitis A virus, Enterovirus, Poliovirus, and SARS-CoV-2 in São Tomé and Príncipe: A pilot study

BACKGROUND

Wastewater-based surveillance is a valuable tool for monitoring pathogen transmission in communities, especially in regions where formal surveillance systems are limited.

AIM

The aim of this study was to implement and evaluate a wastewater-based monitoring system for viral pathogens in São Tomé and Príncipe.

METHODS

A total of 122 water samples were collected bi-weekly from June 2022 to July 2023 at six locations in São Tomé city and analysed for molecular detection of Hepatitis A Virus (HAV), Enterovirus (EV), Poliovirus (PV), SARS-CoV-2, as well as JC-Polyomavirus (JCPyV) and pepper mild mottle virus (PMMoV) as indicators of human contamination. Prevalence was analysed per pathogen and across sampling locations. Results for SARS-CoV-2 were assessed together with notifications from national COVID-19 surveillance. Further, we estimated resources needed to establish a wastewater-based approach to assess community-level transmission of viral pathogens.

RESULTS

All 122 and 117 samples were found positive for PMMoV and JCPyV, respectively, demonstrating a high level of human contamination at all sampling locations. The prevalence of HAV and EV ranged from 0 % to 59 % and 56 % respectively. Consistent with national surveillance data the highest proportion of SARS-CoV-2 positive water samples coincides with the highest number of COVID-19 cases reported during the study, demonstrating the potential of wastewater-based surveillance to identify signals. In addition, for SARS-CoV-2 this approach provided evidence of continuous circulation of the virus in the community, most importantly during weeks when no COVID-19 cases were reported.

CONCLUSION

Our findings provide evidence of high transmission of HAV and EV in communities in São Tomé and continuous circulation of SARS-CoV-2, even in weeks without COVID-19 case notifications. This study demonstrates that monitoring of viral pathogens in humanly impacted open water streams and sewage tanks is a valuable tool to complement clinical surveillance in resource-limited settings.

Wastewater-based epidemiology for COVID-19 surveillance and beyond: A survey

The pandemic of COVID-19 has imposed tremendous pressure on public health systems and social economic ecosystems over the past years. To alleviate its social impact, it is important to proactively track the prevalence of COVID-19 within communities. The traditional way to estimate the disease prevalence is to estimate from reported clinical test data or surveys. However, the coverage of clinical tests is often limited and the tests can be labor-intensive, requires reliable and timely results, and consistent diagnostic and reporting criteria. Recent studies revealed that patients who are diagnosed with COVID-19 often undergo fecal shedding of SARS-CoV-2 virus into wastewater, which makes wastewater-based epidemiology for COVID-19 surveillance a promising approach to complement traditional clinical testing. In this paper, we survey the existing literature regarding wastewater-based epidemiology for COVID-19 surveillance and summarize the current advances in the area. Specifically, we have covered the key aspects of wastewater sampling, sample testing, and presented a comprehensive and organized summary of wastewater data analytical methods. Finally, we provide the open challenges on current wastewater-based COVID-19 surveillance studies, aiming to encourage new ideas to advance the development of effective wastewater-based surveillance systems for general infectious diseases.

Impact of Sample Storage Time and Temperature on the Stability of Respiratory Viruses and Enteric Viruses in Wastewater

Wastewater-based surveillance (WBS) has been widely used to track SARS-CoV-2 as well as many other viruses in communities during the COVID pandemic and post-pandemic. However, it is still not clear how temperature and storage time would influence the stability of viruses in wastewater. In this study, we assessed the stability of SARS-CoV-2, pepper mild mottle virus (PMMoV), influenza viruses A (IAV) and B (IBV), respiratory syncytial virus (RSV), and enteric viruses in raw wastewater stored at room temperature, 4 °C, and -20 °C for 3 and 6 days. SARS-CoV-2, PMMoV, IAV, and enteric viruses were found to be stable up to 6 days after storing at room temperature or 4 °C. SARS-CoV-2 and RSV were more susceptible to freeze-thaw cycles compared to PMMoV and enteric viruses, which were relatively stable for up to 6 days stored at -20 °C. Low detection of IBV in wastewater made it difficult to evaluate the impact. Based on our findings, we conclude that short-term storage or transportation of wastewater samples within 6 days at ambient temperature or 4 °C is acceptable for the majority of these viruses. Freezing samples at -20 °C for even short periods is not recommended for WBS of respiratory viruses. The data obtained from this study can provide guidance for quality assurance purposes from the operational aspects of wastewater surveillance.

Detection of human enteric viral genes in a non-native winter crane fly, Trichocera maculipennis (Diptera) in the sewage treatment facilities at Antarctic stations

BACKGROUND

The Antarctic environment is susceptible to the introduction of non-native species due to its unique ecosystem, which has evolved under geographical isolation and extreme climatic conditions over an extended period. The recent introduction of the non-native winter crane fly, Trichocera maculipennis, to maritime Antarctica may pose a potential threat to the Antarctic ecosystem. In this study, we evaluated the possibility of the mechanical transmission of viruses by T. maculipennis.

METHODS

We assessed the potential for the mechanical transmission of viruses using next-generation sequencing (NGS), quantitative PCR (qPCR), and virus isolation methods from T. maculipennis (Tm)-related samples (Tm body-wash fluid and Tm body-ground samples) collected from habitats and sewage treatment facilities located at three research stations in Antarctica.

RESULTS

Virome analysis detected the genomic fragments of human adenovirus (AdV) and human endogenous retrovirus (HERV) in Tm-related samples. These viruses are commonly found in human feces. In addition, plant viruses, such as pepper mild mottle virus (PMMoV) and cucumber green mottle mosaic virus (CGMMV), both known indicators of enteric viruses, were identified in all Tm-related samples, likely originating from wastewater. However, the low quantities of AdV and HERV genomes detected in Tm-related samples through qPCR, coupled with the non-viability of AdV in virus isolation tests, indicate that T. maculipennis has limited potential for mechanical transmission under the conditions in the studies.

CONCLUSIONS

Our study represents the first evaluation of the potential risk of non-native species serving as vectors for viral pathogens in Antarctica. Although the viruses detected were in relatively low quantities and non-viable, this study highlights the importance of further evaluating the risks associated with non-native species, particularly as the likelihood of their introduction increases to Antarctica due to climate change and increased human activity.

Surveillance of Emerging Rodent-Borne Pathogens in Wastewater in Taiwan: A One Health Approach

Leptospirosis and hantavirus syndrome are two major rodent-borne diseases in Taiwan. Rocahepevirus ratii (RHEV), a virus closely related to hepatitis E virus (HEV, Paslahepevirus balayani), is emerging and has been reported to cause hepatitis in humans. We employed wastewater-based epidemiology to actively monitor rodent-borne pathogens, and the correlations with human cases were evaluated. Wastewater was collected using grab sampling at 11 sites along a sewer system including influents and effluents at a wastewater treatment plant in Tamsui, New Taipei City, Taiwan, monthly during June 2023 to May 2024. The presence of pathogens was examined by reverse transcription-polymerase chain reaction (RT-PCR). The result showed an overall positivity rate of 38.2% (50/131). Leptospira was detected most often (48/131, 36.6%), and RHEV and hantaviruses were found once each during the study period. Sequencing identified Leptospira interrogans close to isolates from rodents and human cases, while sequences of hantavirus and RHEV were most similar to isolates from rodents. No significant correlation was found with human cases or positive samples for rodent DNA. Here, we present an example of a One Health approach applying wastewater to environmental surveillance for the early detection and prevention of emerging diseases.

One Year of Wastewater Surveillance in South Africa Supporting COVID-19 Clinical Findings Across Two Waves of Infection

Wastewater-based epidemiology (WBE) has been an important tool for the detection of COVID-19 outbreaks. The retrospective analysis of COVID-19 data is vital to understand the spread and impact of the virus as well as to inform future planning and response efforts. In this study, we evaluated the SARS-CoV-2 RNA levels in wastewater from 21 wastewater treatment plants (WWTPs) in the City of Cape Town (South Africa) over a period of 12 months and compared the (inactive) SARS-CoV-2 viral RNA in wastewater between wave 2 (November 2020 to January 2021) and wave 3 (June 2021 to September 2021). The SARS-CoV-2 RNA expression was quantified in wastewater using quantitative real-time PCR (qRT-PCR) by targeting the nucleocapsid (N) gene, and the resultant signal was normalized to the WWTP design capacity and catchment size. Our findings show that the maximum SARS-CoV-2 RNA signal was significantly higher in wave 3 than in wave 2 (p < 0.01). The duration of wave 3 (15 weeks) was longer than that of wave 2 (10 weeks), and the wastewater surveillance data supported the clinical findings, as evidenced by the two distinct waves. Furthermore, the data demonstrated the importance of long-term wastewater surveillance as a key indicator of changing trends.

Implications of long-term sample storage on the recovery of viruses from wastewater and biobanking

Wastewater-based monitoring has been widely implemented worldwide for the tracking of SARS-CoV-2 outbreaks and other viral diseases. In many surveillance programmes, unprocessed and processed wastewater samples are often frozen and stored for long periods of time in case the identification and tracing of an emerging health threat becomes necessary. However, extensive sample bioarchives may be difficult to maintain due to limitations in ultra-freezer capacity and associated cost. Furthermore, the stability of viruses in such samples has not been systematically investigated and hence the usefulness of bioarchives is unknown. In this study, we assessed the stability of SARS-CoV-2, influenza viruses, noroviruses and the faecal indicator virus, crAssphage, in raw wastewater and purified nucleic aacid extracts stored at -80 °C for 6-24 months. We found that the isolated viral RNA and DNA showed little signs of degradation in storage over 8-24 months, whereas extensive decay viral and loss of qPCR signal was observed during the storage of raw unprocessed wastewater. The most stable viruses were noroviruses and crAssphage, followed by SARS-CoV-2 and influenza A virus. Based on our findings, we conclude that bioarchives comprised of nucleic acid extracts derived from concentrated wastewater samples may be archived long-term, for at least two years, whereas raw wastewater samples may be discarded after one year.

Wastewater-based Epidemiology for COVID-19 Surveillance and Beyond: A Survey

The pandemic of COVID-19 has imposed tremendous pressure on public health systems and social economic ecosystems over the past years. To alleviate its social impact, it is important to proactively track the prevalence of COVID-19 within communities. The traditional way to estimate the disease prevalence is to estimate from reported clinical test data or surveys. However, the coverage of clinical tests is often limited and the tests can be labor-intensive, requires reliable and timely results, and consistent diagnostic and reporting criteria. Recent studies revealed that patients who are diagnosed with COVID-19 often undergo fecal shedding of SARS-CoV-2 virus into wastewater, which makes wastewater-based epidemiology for COVID-19 surveillance a promising approach to complement traditional clinical testing. In this paper, we survey the existing literature regarding wastewater-based epidemiology for COVID-19 surveillance and summarize the current advances in the area. Specifically, we have covered the key aspects of wastewater sampling, sample testing, and presented a comprehensive and organized summary of wastewater data analytical methods. Finally, we provide the open challenges on current wastewater-based COVID-19 surveillance studies, aiming to encourage new ideas to advance the development of effective wastewater-based surveillance systems for general infectious diseases.

Comparison of Three Viral Nucleic Acid Preamplification Pipelines for Sewage Viral Metagenomics

Viral metagenomics is a useful tool for detecting multiple human viruses in urban sewage. However, more refined protocols are required for its effective use in disease surveillance. In this study, we investigated the performance of three different preamplification pipelines (specific to RNA viruses, DNA viruses or both) for viral genome sequencing using spiked-in Phosphate Buffered Saline and sewage samples containing known concentrations of viruses. We found that compared to the pipeline targeting all genome types, the RNA pipeline performed better in detecting RNA viruses in both spiked and unspiked sewage samples, allowing the detection of various mammalian viruses including members from the Reoviridae, Picornaviridae, Astroviridae and Caliciviridae. However, the DNA-specific pipeline did not improve the detection of mammalian DNA viruses. We also measured viral recovery by quantitative reverse transcription polymerase chain reaction and assessed the impact of genetic background (non-viral genetic material) on viral coverage. Our results indicate that viral recoveries were generally lower in sewage (average of 11.0%) and higher in Phosphate Buffered Saline (average of 23.4%) for most viruses. Additionally, spiked-in viruses showed lower genome coverage in sewage, demonstrating the negative effect of genetic background on sequencing. Finally, correlation analysis revealed a relationship between virus concentration and genome normalized reads per million, indicating that viral metagenomic sequencing can be semiquantitative.

Estimating the effective reproduction number of COVID-19 from population-wide wastewater data: An application in Kagawa, Japan

Although epidemiological surveillance of COVID-19 has been gradually downgraded globally, the transmission of COVID-19 continues. It is critical to quantify the transmission dynamics of COVID-19 using multiple datasets including wastewater virus concentration data. Herein, we propose a comprehensive method for estimating the effective reproduction number using wastewater data. The wastewater virus concentration data, which were collected twice a week, were analyzed using daily COVID-19 incidence data obtained from Takamatsu, Japan between January 2022 and September 2022. We estimated the shedding load distribution (SLD) as a function of time since the date of infection, using a model employing the delay distribution, which is assumed to follow a gamma distribution, multiplied by a scaling factor. We also examined models that accounted for the temporal smoothness of viral load measurement data. The model that smoothed temporal patterns of viral load was the best fit model (WAIC = 2795.8), which yielded a mean estimated distribution of SLD of 3.46 days (95% CrI: 3.01-3.95 days). Using this SLD, we reconstructed the daily incidence, which enabled computation of the effective reproduction number. Using the best fit posterior draws of parameters directly, or as a prior distribution for subsequent analyses, we first used a model that assumed temporal smoothness of viral load concentrations in wastewater, as well as infection counts by date of infection. In the subsequent approach, we examined models that also incorporated weekly reported case counts as a proxy for weekly incidence reporting. Both approaches enabled estimations of the epidemic curve as well as the effective reproduction number from twice-weekly wastewater viral load data. Adding weekly case count data reduced the uncertainty of the effective reproduction number. We conclude that wastewater data are still a valuable source of information for inferring the transmission dynamics of COVID-19, and that inferential performance is enhanced when those data are combined with weekly incidence data.

The different adsorption-degradation behaviors of SARS-CoV-2 by bioactive chemicals in wastewater: The suppression kinetics and their implications for wastewater-based epidemiology

Wastewater-Based Epidemiology (WBE) is widely used to monitor the progression of SARS-CoV-2 pandemic. While there is a clear correlation between the number of COVID patients in a sewershed and the viral load in the wastewater, there is notable variability across different treatment plants. In particular, some facilities consistently exhibit higher viral content per diagnosed patient, implying a potential underestimation of the number of COVID patients, while others show a low viral load per diagnosed case, indicating potential attenuation of genetic material from the sewershed. In this study, we investigated the impact of nonylphenol ethoxylate (NPHE), linear alkylbenzene sulfonic acid (LABS), bisoctyl dimethyl ammonium chloride (BDAC), and didecyldimethylammonium chloride (DDAC), the surfactants that have been commonly used as detergents, emulsifiers, wetting agents on the stability of SARS-CoV-2 in wastewater. The results showed multiple and dynamic mechanisms, including degradation and desorption, can occur simultaneously during the interaction between SARS-CoV-2 and different chemicals depending on the physicochemical properties of each chemical. Through the elucidation of the dynamic interactions, the findings from this study could help the state health organizations and scientific community to optimize the SARS-CoV-2 wastewater-based epidemiology strategies.

Global WWTP Microbiome-based Integrative Information Platform: From experience to intelligence

Domestic and industrial wastewater treatment plants (WWTPs) are facing formidable challenges in effectively eliminating emerging pollutants and conventional nutrients. In microbiome engineering, two approaches have been developed: a top-down method focusing on domesticating seed microbiomes into engineered ones, and a bottom-up strategy that synthesizes engineered microbiomes from microbial isolates. However, these approaches face substantial hurdles that limit their real-world applicability in wastewater treatment engineering. Addressing this gap, we propose the creation of a Global WWTP Microbiome-based Integrative Information Platform, inspired by the untapped microbiome and engineering data from WWTPs and advancements in artificial intelligence (AI). This open platform integrates microbiome and engineering information globally and utilizes AI-driven tools for identifying seed microbiomes for new plants, providing technical upgrades for existing facilities, and deploying microbiomes for accidental pollution remediation. Beyond its practical applications, this platform has significant scientific and social value, supporting multidisciplinary research, documenting microbial evolution, advancing Wastewater-Based Epidemiology, and enhancing global resource sharing. Overall, the platform is expected to enhance WWTPs' performance in pollution control, safeguarding a harmonious and healthy future for human society and the natural environment.

A scoping review of global SARS-CoV-2 wastewater-based epidemiology in light of COVID-19 pandemic

Recently, wastewater-based epidemiology (WBE) research has experienced a strong impetus during the Coronavirus disease 2019 (COVID-19) pandemic. However, a few technical issues related to surveillance strategies, such as standardized procedures ranging from sampling to testing protocols, need to be resolved in preparation for future infectious disease outbreaks. This review highlights the study characteristics, potential use of WBE and overview of methods, as well as methods utilized to detect severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) including its variant in wastewater. A literature search was performed electronically in PubMed and Scopus according to PRISMA guidelines for relevant peer-reviewed articles published between January 2020 and March 2022. The search identified 588 articles, out of which 221 fulfilled the necessary criteria and are discussed in this review. Most global WBE studies were conducted in North America (n = 75, 34 %), followed by Europe (n = 68, 30.8 %), and Asia (n = 43, 19.5 %). The review also showed that most of the application of WBE observed were to correlate SARS-CoV-2 ribonucleic acid (RNA) trends in sewage with epidemiological data (n = 90, 40.7 %). The techniques that were often used globally for sample collection, concentration, preferred matrix recovery control and various sample types were also discussed. Overall, this review provided a framework for researchers specializing in WBE to apply strategic approaches to their research questions in achieving better functional insights. In addition, areas that needed more in-depth analysis, data collection, and ideas for new initiatives were identified.

Persistence of human respiratory viral RNA in wastewater-settled solids

Wastewater-based epidemiology has emerged as a valuable tool for monitoring respiratory viral diseases within communities by analyzing concentrations of viral nucleic-acids in wastewater. However, little is known about the fate of respiratory virus nucleic-acids in wastewater. Two important fate processes that may modulate their concentrations in wastewater as they move from household drains to the point of collection include sorption or partitioning to wastewater solids and degradation. This study investigated the decay kinetics of genomic nucleic-acids of seven human respiratory viruses, including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), respiratory syncytial virus (RSV), human coronavirus (HCoV)-OC43, HCoV-229E, HCoV-NL63, human rhinovirus (HRV), and influenza A virus (IAV), as well as pepper mild mottle virus (PMMoV) in wastewater solids. Viruses (except for PMMoV) were spiked into wastewater solids and their concentrations were followed for 50 days at three different temperatures (4°C, 22°C, and 37°C). Viral genomic RNA decayed following first-order kinetics with decay rate constants k from 0 to 0.219 per day. Decay rate constants k were not different from 0 for all targets in solids incubated at 4°C; k values were largest at 37°C and at this temperature, k values were similar across nucleic-acid targets. Regardless of temperature, there was limited viral RNA decay, with an estimated 0% to 20% reduction, over the typical residence times of sewage in the piped systems between input and collection point (<1 day). The k values reported herein can be used directly in fate and transport models to inform the interpretation of measurements made during wastewater surveillance.IMPORTANCEUnderstanding whether or not the RNA targets quantified for wastewater-based epidemiology (WBE) efforts decay during transport between drains and the point of sample collection is critical for data interpretation. Here we show limited decay of viral RNA targets typically measured for respiratory disease WBE.

Evaluating acute gastroenteritis-causing pathogen reduction in wastewater and the applicability of river water for wastewater-based epidemiology in the Kathmandu Valley, Nepal

Rapid urbanization and population growth without the implementation of proper waste management are capable of contaminating water sources, which can lead to acute gastroenteritis. This study examined the detection and reduction of five gastroenteritis-causing enteropathogens, Salmonella, Campylobacter coli, Campylobacter jejuni, Clostridium perfringens, and genogroup IV norovirus, and one respiratory pathogen, influenza A virus, in two municipal wastewater treatment plants (WWTP) using an oxidation ditch system (WWTP A; n = 20) and a stabilization pond system (WWTP B; n = 18) in the Kathmandu Valley, Nepal, collected between August 2017 and August 2019. All enteropathogens were detected in wastewater via quantitative PCR. The concentrations of the pathogens ranged from 5.7 to 7.9 log10 copies/L in WWTP A and from 4.9 to 8.1 log10 copies/L in WWTP B. The log10 reduction values of the pathogens ranged from 0.3 to 1.0 in WWTP A and from -0.1 to 0.2 in WWTP B. The association between the pathogen concentrations and the number of clinical cases in the corresponding week could not be evaluated; however, the consistent detection of pathogens in the wastewater despite low number of case reports suggested the use of wastewater-based epidemiology (WBE) for early warning of acute gastroenteritis (AGE) in the Kathmandu Valley. The pathogens were also detected in river water at approximately 7.0 log10 copies/L and exhibited no significant difference in concentration compared to wastewater, suggesting the applicability of river water for WBE of AGE. Insufficient treatment of all pathogens in the wastewater was observed, suggesting the need for full rehabilitation of the treatment plants. However, the influent may be utilized for early detection of AGE-causing pathogens in the city, whereas the river water may serve as an alternative in areas without connection to the WWTPs.

Influences of 23 different equations used to calculate gene copies of SARS-CoV-2 during wastewater-based epidemiology

Following the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in late 2019, the use of wastewater-based surveillance (WBS) has increased dramatically along with associated infrastructure globally. However, due to the global nature of its application, and various workflow adaptations (e.g., sample collection, water concentration, RNA extraction kits), numerous methods for back-calculation of gene copies per volume (gc/L) of sewage have also emerged. Many studies have considered the comparability of processing methods (e.g., water concentration, RNA extraction); however, for equations used to calculate gene copies in a wastewater sample and subsequent influences on monitoring viral trends in a community and its association with epidemiological data, less is known. Due to limited information on how many formulas exist for the calculation of SARS-CoV-2 gene copies in wastewater, we initially attempted to quantify how many equations existed in the referred literature. We identified 23 unique equations, which were subsequently applied to an existing wastewater dataset. We observed a range of gene copies based on use of different equations, along with variability of AUC curve values, and results from correlation and regression analyses. Though a number of individual laboratories appear to have independently converged on a similar formula for back-calculation of viral load in wastewater, and share similar relationships with epidemiological data, differential influences of various equations were observed for variation in PCR volumes, RNA extraction volumes, or PCR assay parameters. Such observations highlight challenges when performing comparisons among WBS studies when numerous methodologies and back-calculation methods exist. To facilitate reproducibility among studies, the different gc/L equations were packaged as an R Shiny app, which provides end users the ability to investigate variability within their datasets and support comparisons among studies.

Critical Needs for Integrated Surveillance: Wastewater-Based and Clinical Epidemiology in Evolving Scenarios with Lessons Learned from SARS-CoV-2

During the COVID-19 pandemic, wastewater-based epidemiology (WBE) and clinical surveillance have been used as tools for analyzing the circulation of SARS-CoV-2 in the community, but both approaches can be strongly influenced by some sources of variability. From the challenging perspective of integrating environmental and clinical data, we performed a correlation analysis between SARS-CoV-2 concentrations in raw sewage and incident COVID-19 cases in areas served by medium-size wastewater treatment plants (WWTPs) from 2021 to 2023. To this aim, both datasets were adjusted for several sources of variability: WBE data were adjusted for factors including the analytical protocol, sewage flow, and population size, while clinical data adjustments considered the demographic composition of the served population. Then, we addressed the impact on the correlation of differences among sewerage networks and variations in the frequency and type of swab tests due to changes in political and regulatory scenarios. Wastewater and clinical data were significantly correlated when restrictive containment measures and limited movements were in effect (ρ = 0.50) and when COVID-19 cases were confirmed exclusively through molecular testing (ρ = 0.49). Moreover, a positive (although weak) correlation arose for WWTPs located in densely populated areas (ρ = 0.37) and with shorter sewerage lengths (ρ = 0.28). This study provides methodological approaches for interpreting WBE and clinical surveillance data, which could also be useful for other infections. Data adjustments and evaluation of possible sources of bias need to be carefully considered from the perspective of integrated environmental and clinical surveillance of infections.

Epidemiological model can forecast COVID-19 outbreaks from wastewater-based surveillance in rural communities

Wastewater can play a vital role in infectious disease surveillance, especially in underserved communities where it can reduce the equity gap to larger municipalities. However, using wastewater surveillance in a predictive manner remains a challenge. We tested if detecting SARS-CoV-2 in wastewater can predict outbreaks in rural communities. Under the CDC National Wastewater Surveillance program, we monitored several rural communities in Idaho (USA). While high daily variations in wastewater viral load made real-time interpretation difficult, a SEIR model could factor out the data noise and forecast the start of the Omicron outbreak in five of the six cities that were sampled soon after SARS-CoV-2 quantities increased in wastewater. For one city, the model could predict an outbreak 11 days before reported clinical cases began to increase. An epidemiological modeling approach can transform how epidemiologists use wastewater data to provide public health guidance on infectious diseases in rural communities.

COVID-19 hospitalizations and deaths predicted by SARS-CoV-2 levels in Boise, Idaho wastewater

The viral load of COVID-19 in untreated wastewater from Idaho's capital city Boise, ID (Ada County) has been used to predict changes in hospital admissions (statewide in Idaho) and deaths (Ada County) using distributed fixed lag modeling and artificial neural networks (ANN). The wastewater viral counts were used to determine the lag time between peaks in wastewater viral counts and COVID-19 hospitalizations as well as deaths (14 and 23 days, respectively). Quantitative measurement of SARS-CoV-2 viral RNA counts in the untreated wastewater was determined three times a week using RT-qPCR over a span of 13 months. To mitigate the effects of PCR inhibitors in wastewater, a series of dilution tests were conducted, and the 1/4 dilution was used to generate the most successful model. Wastewater SARS-CoV-2 viral RNA counts and hospitalization from June 7, 2021 to December 29, 2021 were used as training data to predict hospitalizations; and wastewater SARS-CoV-2 viral RNA counts and deaths from June 7, 2021 to December 20, 2021 were used as training data to predict deaths. These training data were used to make predictive ANN models for future hospitalizations and deaths. To the best of our knowledge, this is the first report of prediction of deaths from COVID-19 based on wastewater SARS-CoV-2 viral RNA counts using machine learning-based multilayered ANN. The applied modeling demonstrates that wastewater surveillance data can be combined with hospitalizations and death data to generate machine learning-based ANN models that predict future COVID-19 hospital admissions and deaths, providing an early warning for medical response teams and healthcare policymakers.

Influenza transmission dynamics quantified from RNA in wastewater in Switzerland

INTRODUCTION

Influenza infections are challenging to monitor at the population level due to many mild and asymptomatic cases and similar symptoms to other common circulating respiratory diseases, including COVID-19. Methods for tracking cases outside of typical reporting infrastructure could improve monitoring of influenza transmission dynamics. Influenza shedding into wastewater represents a promising source of information where quantification is unbiased by testing or treatment-seeking behaviours.

METHODS

We quantified influenza A and B virus loads from influent at Switzerland's three largest wastewater treatment plants, serving about 14% of the Swiss population (1.2 million individuals). We estimated trends in infection incidence and the effective reproductive number (Re) in these catchments during a 2021/22 epidemic and compared our estimates to typical influenza surveillance data.

RESULTS

Wastewater data captured the same overall trends in infection incidence as laboratory-confirmed case data at the catchment level. However, the wastewater data were more sensitive in capturing a transient peak in incidence in December 2021 than the case data. The Re estimated from the wastewater data was roughly at or below the epidemic threshold of 1 during work-from-home measures in December 2021 but increased to at or above the epidemic threshold in two of the three catchments after the relaxation of these measures. The third catchment yielded qualitatively the same results but with wider confidence intervals. The confirmed case data at the catchment level yielded comparatively less precise R_e estimates before and during the work-from-home period, with confidence intervals that included one before and during the work-from-home period.

DISCUSSION

Overall, we show that influenza RNA in wastewater can help monitor nationwide influenza transmission dynamics. Based on this research, we developed an online dashboard for ongoing wastewater-based influenza surveillance in Switzerland.

A comprehensive, open-source data model for wastewater-based epidemiology

The recent SARS-COV-2 pandemic has sparked the adoption of wastewater-based epidemiology (WBE) as a low-cost way to monitor the health of populations. In parallel, the pandemic has encouraged researchers to openly share their data to serve the public better and accelerate science. However, environmental surveillance data are highly dependent on context and are difficult to interpret meaningfully across sites. This paper presents the second iteration of the Public Health Environmental Surveillance Open Data Model (PHES-ODM), an open-source dictionary and set of data tools to enhance the interoperability of environmental surveillance data and enable the storage of contextual (meta)data. The data model describes how to store environmental surveillance program data, metadata about measurements taken on various specimens (water, air, surfaces, sites, populations) and data about measurement protocols. The model provides software tools that support the collection and use of PHES-ODM formatted data, including performing PCR calculations and data validation, recording data into input templates, generating wide tables for analysis, and producing SQL database definitions. Fully open-source and already adopted by institutions in Canada, the European Union, and other countries, the PHES-ODM provides a path forward for creating robust, interoperable, open datasets for environmental public health surveillance for SARS-CoV-2 and beyond.

Wastewater surveillance provides 10-days forecasting of COVID-19 hospitalizations superior to cases and test positivity: A prediction study

Background

The public health response to COVID-19 has shifted to reducing deaths and hospitalizations to prevent overwhelming health systems. The amount of SARS-CoV-2 RNA fragments in wastewater are known to correlate with clinical data including cases and hospital admissions for COVID-19. We developed and tested a predictive model for incident COVID-19 hospital admissions in New York State using wastewater data.

Methods

Using county-level COVID-19 hospital admissions and wastewater surveillance covering 13.8 million people across 56 counties, we fit a generalized linear mixed model predicting new hospital admissions from wastewater concentrations of SARS-CoV-2 RNA from April 29, 2020 to June 30, 2022. We included covariates such as COVID-19 vaccine coverage in the county, comorbidities, demographic variables, and holiday gatherings.

Findings

Wastewater concentrations of SARS-CoV-2 RNA correlated with new hospital admissions per 100,000 up to ten days prior to admission. Models that included wastewater had higher predictive power than models that included clinical cases only, increasing the accuracy of the model by 15%. Predicted hospital admissions correlated highly with observed admissions (r = 0.77) with an average difference of 0.013 hospitalizations per 100,000 (95% CI = [0.002, 0.025]).

Interpretation

Using wastewater to predict future hospital admissions from COVID-19 is accurate and effective with superior results to using case data alone. The lead time of ten days could alert the public to take precautions and improve resource allocation for seasonal surges.

Ohio Coronavirus Wastewater Monitoring Network: Implementation of Statewide Monitoring for Protecting Public Health

CONTEXT

Prior to the COVID-19 pandemic, wastewater influent monitoring for tracking disease burden in sewered communities was not performed in Ohio, and this field was only on the periphery of the state academic research community.

PROGRAM

Because of the urgency of the pandemic and extensive state-level support for this new technology to detect levels of community infection to aid in public health response, the Ohio Water Resources Center established relationships and support of various stakeholders. This enabled Ohio to develop a statewide wastewater SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) monitoring network in 2 months starting in July 2020.

IMPLEMENTATION

The current Ohio Coronavirus Wastewater Monitoring Network (OCWMN) monitors more than 70 unique locations twice per week, and publicly available data are updated weekly on the public dashboard.

EVALUATION

This article describes the process and decisions that were made during network initiation, the network progression, and data applications, which can inform ongoing and future pandemic response and wastewater monitoring.

DISCUSSION

Overall, the OCWMN established wastewater monitoring infrastructure and provided a useful tool for public health professionals responding to the pandemic.

Influence of storage conditions and multiple freeze-thaw cycles on N1 SARS-CoV-2, PMMoV, and BCoV signal

Wastewater-based epidemiology/wastewater-based surveillance (WBE/WBS) continues to serve as an effective means of monitoring various diseases, including COVID-19 and the emergence of SARS-CoV-2 variants, at the population level. As the use of WBE expands, storage conditions of wastewater samples will play a critical role in ensuring the accuracy and reproducibility of results. In this study, the impacts of water concentration buffer (WCB), storage temperature, and freeze-thaw cycles on the detection of SARS-CoV-2 and other WBE-related gene targets were examined. Freeze-thawing of concentrated samples did not significantly affect (p > 0.05) crossing/cycle threshold (Ct) value for any of the gene targets studied (SARS-CoV-2 N1, PMMoV, and BCoV). However, use of WCB during concentration resulted in a significant (p < 0.05) decrease in Ct for all targets, and storage at -80 °C (in contrast to -20 °C) appeared preferable for wastewater storage signal stability based on decreased Ct values, although this was only significantly different (p < 0.05) for the BCoV target. Interestingly, when Ct values were converted to gene copies per influent sample, no significant differences (p > 0.05) were observed in any of the targets examined. Stability of RNA targets in concentrated wastewater against freeze-thaw degradation supports archiving of concentrated samples for use in retrospective examination of COVID-19 trends and tracing SARS-CoV-2 variants and potentially other viruses, and provides a starting point for establishing a consistent procedure for specimen collection and storage for the WBE/WBS community.

Bayesian sequential approach to monitor COVID-19 variants through test positivity rate from wastewater

Deployment of clinical testing on a massive scale was an essential control measure for curtailing the burden of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections and the magnitude of the COVID-19 (coronavirus disease 2019) pandemic during its waves. As the pandemic progressed, new preventive and surveillance mechanisms emerged. Implementation of vaccine programs, wastewater (WW) surveillance, and at-home COVID-19 antigen tests reduced the demand for mass SARS-CoV-2 testing. Unfortunately, reductions in testing and test reporting rates also reduced the availability of public health data to support decision-making. This paper proposes a sequential Bayesian approach to estimate the COVID-19 test positivity rate (TPR) using SARS-CoV-2 RNA concentrations measured in WW through an adaptive scheme incorporating changes in virus dynamics. The proposed modeling framework was applied to WW surveillance data from two WW treatment plants in California; the City of Davis and the University of California, Davis campus. TPR estimates are used to compute thresholds for WW data using the Centers for Disease Control and Prevention thresholds for low (<5% TPR), moderate (5%-8% TPR), substantial (8%-10% TPR), and high (>10% TPR) transmission. The effective reproductive number estimates are calculated using TPR estimates from the WW data. This approach provides insights into the dynamics of the virus evolution and an analytical framework that combines different data sources to continue monitoring COVID-19 trends. These results can provide public health guidance to reduce the burden of future outbreaks as new variants continue to emerge. IMPORTANCE We propose a statistical model to correlate WW with TPR to monitor COVID-19 trends and to help overcome the limitations of relying only on clinical case detection. We pose an adaptive scheme to model the nonautonomous nature of the prolonged COVID-19 pandemic. The TPR is modeled through a Bayesian sequential approach with a beta regression model using SARS-CoV-2 RNA concentrations measured in WW as a covariable. The resulting model allows us to compute TPR based on WW measurements and incorporates changes in viral transmission dynamics through an adaptive scheme.

Analysis of SARS-CoV-2 in wastewater for prevalence estimation and investigating clinical diagnostic test biases

Here we analyze SARS-CoV-2 genome copies in Catalonia's wastewater during the Omicron peak and develop a mathematical model to estimate the number of infections and the temporal relationship between reported and unreported cases. 1-liter samples from 16 wastewater treatment plants were collected and used in a compartmental epidemiological model. The average correlation between genome copies and reported cases was 0.85, with an average delay of 8.8 days. The model estimated that 53% of the population was infected, compared to the 19% reported cases. The under-reporting was highest in November and December 2021. The maximum genome copies shed in feces by an infected individual was estimated to range from 1.4×108 gc/g to 4.4×108 gc/g. Our framework demonstrates the potential of wastewater data as a leading indicator for daily new infections, particularly in contexts with low detection rates. It also serves as a complementary tool for prevalence estimation and offers a general approach for integrating wastewater data into compartmental models.

Global trends in the research and development of medical/pharmaceutical wastewater treatment over the half-century

The COVID-19 pandemic has severely impacted public health and the worldwide economy. The overstretched operation of health systems around the world is accompanied by potential and ongoing environmental threats. At present, comprehensive scientific assessments of research on temporal changes in medical/pharmaceutical wastewater (MPWW), as well as estimations of researcher networks and scientific productivity are lacking. Therefore, we conducted a thorough literature study, using bibliometrics to reproduce research on medical wastewater over nearly half a century. Our primary goal is systematically to map the evolution of keyword clusters over time, and to obtain the structure and credibility of clusters. Our secondary objective was to measure research network performance (country, institution, and author) using CiteSpace and VOSviewer. We extracted 2306 papers published between 1981 and 2022. The co-cited reference network identified 16 clusters with well-structured networks (Q = 0.7716, S = 0.896). The main trends were as follows: 1) Early MPWW research prioritized sources of wastewater, and this cluster was considered to be the mainstream research frontier and direction, representing an important source and priority research area. 2) Mid-term research focused on characteristic contaminants and detection technologies. Particularly during 2000-2010, a period of rapid developments in global medical systems, pharmaceutical compounds (PhCs) in MPWW were recognized as a major threat to human health and the environment. 3) Recent research has focused on novel degradation technologies for PhC-containing MPWW, with high scores for research on biological methods. Wastewater-based epidemiology has emerged as being consistent with or predictive of the number of confirmed COVID-19 cases. Therefore, the application of MPWW in COVID-19 tracing will be of great interest to environmentalists. These results could guide the future direction of funding agencies and research groups.

M. AH, Fernandez S, Huang AT, Vatanaprasan P, Jay DJ, Saethang T, Luk-in S, Storer RJ, Ounjai P, Devanga Ragupathi NK, Kanthawee P, Sano D, Furukawa T, Sei K, Leelahavanichkul A, Kanjanabuch T, Hirankarn N, Higgins PG, Kicic A, Singer AC, Chatsuwan T, Trowsdale S, Abe S, McLellan AD, Ishikawa H. COVID-19 monitoring with sparse sampling of sewered and non-sewered wastewater in urban and rural communities

Equitable SARS-CoV-2 surveillance in low-resource communities lacking centralized sewers is critical as wastewater-based epidemiology (WBE) progresses. However, large-scale studies on SARS-CoV-2 detection in wastewater from low-and middle-income countries is limited because of economic and technical reasons. In this study, wastewater samples were collected twice a month from 186 urban and rural subdistricts in nine provinces of Thailand mostly having decentralized and non-sewered sanitation infrastructure and analyzed for SARS-CoV-2 RNA variants using allele-specific RT-qPCR. Wastewater SARS-CoV-2 RNA concentration was used to estimate the real-time incidence and time-varying effective reproduction number (Re). Results showed an increase in SARS-CoV-2 RNA concentrations in wastewater from urban and rural areas 14-20 days earlier than infected individuals were officially reported. It also showed that community/food markets were "hot spots" for infected people. This approach offers an opportunity for early detection of transmission surges, allowing preparedness and potentially mitigating significant outbreaks at both spatial and temporal scales.

Capturing the SARS-CoV-2 infection pyramid within the municipality of Rotterdam using longitudinal sewage surveillance

Despite high vaccination rates in the Netherlands, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to circulate. Longitudinal sewage surveillance was implemented along with the notification of cases as two parts of the surveillance pyramid to validate the use of sewage for surveillance, as an early warning tool, and to measure the effect of interventions. Sewage samples were collected from nine neighborhoods between September 2020 and November 2021. Comparative analysis and modeling were performed to understand the correlation between wastewater and case trends. Using high resolution sampling, normalization of wastewater SARS-CoV-2 concentrations, and 'normalization' of reported positive tests for testing delay and intensity, the incidence of reported positive tests could be modeled based on sewage data, and trends in both surveillance systems coincided. The high collinearity implied that high levels of viral shedding around the onset of disease largely determined SARS-CoV-2 levels in wastewater, and that the observed relationship was independent of variants of concern and vaccination levels. Sewage surveillance alongside a large-scale testing effort where 58 % of a municipality was tested, indicated a five-fold difference in the number of SARS-CoV-2-positive individuals and reported cases through standard testing. Where trends in reported positive cases were biased due to testing delay and testing behavior, wastewater surveillance can objectively display SARS-CoV-2 dynamics for both small and large locations and is sensitive enough to measure small variations in the number of infected individuals within or between neighborhoods. With the transition to a post-acute phase of the pandemic, sewage surveillance can help to keep track of re-emergence, but continued validation studies are needed to assess the predictive value of sewage surveillance with new variants. Our findings and model aid in interpreting SARS-CoV-2 surveillance data for public health decision-making and show its potential as one of the pillars of future surveillance of (re)emerging viruses.

Recent progress on wastewater-based epidemiology for COVID-19 surveillance: A systematic review of analytical procedures and epidemiological modeling

On March 11, 2020, the World Health Organization declared the coronavirus disease 2019 (COVID-19), whose causative agent is the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), a pandemic. This virus is predominantly transmitted via respiratory droplets and shed via sputum, saliva, urine, and stool. Wastewater-based epidemiology (WBE) has been able to monitor the circulation of viral pathogens in the population. This tool demands both in-lab and computational work to be meaningful for, among other purposes, the prediction of outbreaks. In this context, we present a systematic review that organizes and discusses laboratory procedures for SARS-CoV-2 RNA quantification from a wastewater matrix, along with modeling techniques applied to the development of WBE for COVID-19 surveillance. The goal of this review is to present the current panorama of WBE operational aspects as well as to identify current challenges related to it. Our review was conducted in a reproducible manner by following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for systematic reviews. We identified a lack of standardization in wastewater analytical procedures. Regardless, the reverse transcription-quantitative polymerase chain reaction (RT-qPCR) approach was the most reported technique employed to detect and quantify viral RNA in wastewater samples. As a more convenient sample matrix, we suggest the solid portion of wastewater to be considered in future investigations due to its higher viral load compared to the liquid fraction. Regarding the epidemiological modeling, the data-driven approach was consistently used for the prediction of variables associated with outbreaks. Future efforts should also be directed toward the development of rapid, more economical, portable, and accurate detection devices.

Epidemic modelling of monitoring public behavior using surveys during pandemic-induced lockdowns

BACKGROUND

Implementing a lockdown for disease mitigation is a balancing act: Non-pharmaceutical interventions can reduce disease transmission significantly, but interventions also have considerable societal costs. Therefore, decision-makers need near real-time information to calibrate the level of restrictions.

METHODS

We fielded daily surveys in Denmark during the second wave of the COVID-19 pandemic to monitor public response to the announced lockdown. A key question asked respondents to state their number of close contacts within the past 24 hours. Here, we establish a link between survey data, mobility data, and hospitalizations via epidemic modelling of a short time-interval around Denmark's December 2020 lockdown. Using Bayesian analysis, we then evaluate the usefulness of survey responses as a tool to monitor the effects of lockdown and then compare the predictive performance to that of mobility data.

RESULTS

We find that, unlike mobility, self-reported contacts decreased significantly in all regions before the nation-wide implementation of non-pharmaceutical interventions and improved predicting future hospitalizations compared to mobility data. A detailed analysis of contact types indicates that contact with friends and strangers outperforms contact with colleagues and family members (outside the household) on the same prediction task.

CONCLUSIONS

Representative surveys thus qualify as a reliable, non-privacy invasive monitoring tool to track the implementation of non-pharmaceutical interventions and study potential transmission paths.

Wastewater-based modeling, reconstruction, and prediction for COVID-19 outbreaks in Hungary caused by highly immune evasive variants

(MOTIVATION)

Wastewater-based epidemiology (WBE) has emerged as a promising approach for monitoring the COVID-19 pandemic, since the measurement process is cost-effective and is exposed to fewer potential errors compared to other indicators like hospitalization data or the number of detected cases. Consequently, WBE was gradually becoming a key tool for epidemic surveillance and often the most reliable data source, as the intensity of clinical testing for COVID-19 drastically decreased by the third year of the pandemic. Recent results suggests that the model-based fusion of wastewater measurements with clinical data and other indicators is essential in future epidemic surveillance.

(METHOD)

In this work, we developed a wastewater-based compartmental epidemic model with a two-phase vaccination dynamics and immune evasion. We proposed a multi-step optimization-based data assimilation method for epidemic state reconstruction, parameter estimation, and prediction. The computations make use of the measured viral load in wastewater, the available clinical data (hospital occupancy, delivered vaccine doses, and deaths), the stringency index of the official social distancing rules, and other measures. The current state assessment and the estimation of the current transmission rate and immunity loss allow a plausible prediction of the future progression of the pandemic.

(RESULTS)

Qualitative and quantitative evaluations revealed that the contribution of wastewater data in our computational epidemiological framework makes predictions more reliable. Predictions suggest that at least half of the Hungarian population has lost immunity during the epidemic outbreak caused by the BA.1 and BA.2 subvariants of Omicron in the first half of 2022. We obtained a similar result for the outbreaks caused by the subvariant BA.5 in the second half of 2022.

(APPLICABILITY)

The proposed approach has been used to support COVID management in Hungary and could be customized for other countries as well.

Exploration on wastewater-based epidemiology of SARS-CoV-2: Mimic relative quantification with endogenous biomarkers as internal reference

Wastewater-based epidemiology has become a powerful surveillance tool for monitoring the pandemic of COVID-19. Although it is promising to quantitatively correlate the SARS-CoV-2 RNA concentration in wastewater with the incidence of community infection, there is still no consensus on whether the viral nucleic acid concentration in sewage should be normalized against the abundance of endogenous biomarkers and which biomarker should be used as a reference for the normalization. Here, several candidate endogenous reference biomarkers for normalization of SARS-CoV-2 signal in municipal sewage were evaluated. The human fecal indicator virus (crAssphage) is a promising candidate of endogenous reference biomarker for data normalization of both DNA and RNA viruses for its intrinsic viral nature and high and stable content in sewage. Without constructing standard curves, the relative quantification of sewage viral nucleic acid against the abundance of the reference biomarker can be used to correlate with community COVID-19 incidence, which was proved via mimic experiments by spiking pseudovirus of different concentrations in sewage samples. Dilution of pseudovirus-seeded wastewater did not affect the relative abundance of viral nucleic acid, demonstrating that relative quantification can overcome the sewage dilution effects caused by the greywater input, precipitation and/or groundwater infiltration. The process of concentration, recovery and detection of the endogenous biomarker was consistent with that of SARS-CoV-2 RNA. Thus, it is necessary to co-quantify the endogenous biomarker because it can be not only an internal reference for data normalization, but also a process control.

Integrating Water Purification with Electrochemical Aptamer Sensing for Detecting SARS-CoV-2 in Wastewater

Wastewater analysis of pathogens, particularly SARS-CoV-2, is instrumental in tracking and monitoring infectious diseases in a population. This method can be used to generate early warnings regarding the onset of an infectious disease and predict the associated infection trends. Currently, wastewater analysis of SARS-CoV-2 is almost exclusively performed using polymerase chain reaction for the amplification-based detection of viral RNA at centralized laboratories. Despite the development of several biosensing technologies offering point-of-care solutions for analyzing SARS-CoV-2 in clinical samples, these remain elusive for wastewater analysis due to the low levels of the virus and the interference caused by the wastewater matrix. Herein, we integrate an aptamer-based electrochemical chip with a filtration, purification, and extraction (FPE) system for developing an alternate in-field solution for wastewater analysis. The sensing chip employs a dimeric aptamer, which is universally applicable to the wild-type, alpha, delta, and omicron variants of SARS-CoV-2. We demonstrate that the aptamer is stable in the wastewater matrix (diluted to 50%) and its binding affinity is not significantly impacted. The sensing chip demonstrates a limit of detection of 1000 copies/L (1 copy/mL), enabled by the amplification provided by the FPE system. This allows the integrated system to detect trace amounts of the virus in native wastewater and categorize the amount of contamination into trace (<10 copies/mL), medium (10-1000 copies/mL), or high (>1000 copies/mL) levels, providing a viable wastewater analysis solution for in-field use.

Wastewater monitoring of SARS-CoV-2 RNA at K-12 schools: comparison to pooled clinical testing data

Background

Wastewater measurements of SARS-CoV-2 RNA have been extensively used to supplement clinical data on COVID-19. Most examples in the literature that describe wastewater monitoring for SARS-CoV-2 RNA use samples from wastewater treatment plants and individual buildings that serve as the primary residence of community members. However, wastewater surveillance can be an attractive supplement to clinical testing in K-12 schools where individuals only spend a portion of their time but interact with others in close proximity, increasing risk of potential transmission of disease.

Methods

Wastewater samples were collected from two K-12 schools in California and divided into solid and liquid fractions to be processed for detection of SARS-CoV-2. The resulting detection rate in each wastewater fraction was compared to each other and the detection rate in pooled clinical specimens.

Results

Most wastewater samples were positive for SARS-CoV-2 RNA when clinical testing was positive (75% for solid samples and 100% for liquid samples). Wastewater samples continued to test positive for SARS-CoV-2 RNA when clinical testing was negative or in absence of clinical testing (83% for both solid and liquid samples), indicating presence of infected individuals in the schools. Wastewater solids had a higher concentration of SARS-CoV-2 than wastewater liquids on an equivalent mass basis by three orders of magnitude.

Early detection of local SARS-CoV-2 outbreaks by wastewater surveillance: a feasibility study

Wastewater surveillance and quantitative analysis of SARS-CoV-2 RNA are increasingly used to monitor the spread of COVID-19 in the community. We studied the feasibility of applying the surveillance data for early detection of local outbreaks. A Monte Carlo simulation model was constructed, applying data on reported variation in RNA gene copy concentration in faeces and faecal masses shed. It showed that, even with a constant number of SARS-CoV-2 RNA shedders, the variation in concentrations found in wastewater samples will be large, and that it will be challenging to translate viral concentrations into incidence estimates, especially when the number of shedders is low. Potential signals for early detection of hypothetical outbreaks were analysed for their performance in terms of sensitivity and specificity of the signals. The results suggest that a sudden increase in incidence is not easily identified on the basis of wastewater surveillance data, especially in small sampling areas and in low-incidence situations. However, with a high number of shedders and when combining data from multiple consecutive tests, the performance of wastewater sampling is expected to improve considerably. The developed modelling approach can increase our understanding of the results from wastewater surveillance of SARS-CoV-2.

Regional and temporal differences in the relation between SARS-CoV-2 biomarkers in wastewater and estimated infection prevalence - Insights from long-term surveillance

Wastewater-based epidemiology provides a conceptual framework for the evaluation of the prevalence of public health related biomarkers. In the context of the Coronavirus disease-2019, wastewater monitoring emerged as a complementary tool for epidemic management. In this study, we evaluated data from six wastewater treatment plants in the region of Saxony, Germany. The study period lasted from February to December 2021 and covered the third and fourth regional epidemic waves. We collected 1065 daily composite samples and analyzed SARS-CoV-2 RNA concentrations using reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Regression models quantify the relation between RNA concentrations and disease prevalence. We demonstrated that the relation is site and time specific. Median loads per diagnosed case differed by a factor of 3-4 among sites during both waves and were on average 45 % higher during the third wave. In most cases, log-log-transformed data achieved better regression performance than non-transformed data and local calibration outperformed global models for all sites. The inclusion of lag/lead time, discharge and detection probability improved model performance in all cases significantly, but the importance of these components was also site and time specific. In all cases, models with lag/lead time and log-log-transformed data obtained satisfactory goodness-of-fit with adjusted coefficients of determination higher than 0.5. Back-estimation of testing efficiency from wastewater data confirmed state-wide prevalence estimation from individual testing statistics, but revealed pronounced differences throughout the epidemic waves and among the different sites.

COPMAN: A novel high-throughput and highly sensitive method to detect viral nucleic acids including SARS-CoV-2 RNA in wastewater

During the coronavirus disease 2019 (COVID-19) pandemic, wastewater-based epidemiology (WBE) attracted attention as an objective and comprehensive indicator of community infection that does not require individual inspection. Although several severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) detection methods from wastewater have been developed, there are obstacles to their social implementation. In this study, we developed the COPMAN (Coagulation and Proteolysis method using Magnetic beads for detection of Nucleic acids in wastewater), an automatable method that can concentrate and detect multiple types of viruses from a limited volume (∼10 mL) of wastewater. The COPMAN consists of a high basicity polyaluminum chloride (PAC) coagulation process, magnetic bead-based RNA purification, and RT-preamplification, followed by qPCR. A series of enzymes exhibiting a high tolerance to PCR inhibitors derived from wastewater was identified and employed in the molecular detection steps in the COPMAN. We compared the detectability of viral RNA from 10-mL samples of virus-spiked (heat-inactivated SARS-CoV-2 and intact RSV) or unspiked wastewater by the COPMAN and other methods (PEG-qPCR, UF-qPCR, and EPISENS-S). The COPMAN was the most efficient for detecting spiked viruses from wastewater, detecting the highest level of pepper mild mottle virus (PMMoV), a typical intrinsic virus in human stool, from wastewater samples. The COPMAN also successfully detected indigenous SARS-CoV-2 RNA from 12 samples of wastewater at concentrations of 2.2 × 10⁴ to 5.4 × 10⁵ copies/L, during initial stages of an infection wave in the right and the left bank of the Sagami River in Japan (0.65 to 11.45 daily reported cases per 100,000 people). These results indicate that the COPMAN is suitable for detection of multiple pathogens from small volume of wastewater in automated stations.

Bayesian sequential approach to monitor COVID-19 variants through positivity rate from wastewater

Trends in COVID-19 infection have changed throughout the pandemic due to myriad factors, including changes in transmission driven by social behavior, vaccine development and uptake, mutations in the virus genome, and public health policies. Mass testing was an essential control measure for curtailing the burden of COVID-19 and monitoring the magnitude of the pandemic during its multiple phases. However, as the pandemic progressed, new preventive and surveillance mechanisms emerged. Implementing vaccine programs, wastewater (WW) surveillance, and at-home COVID-19 tests reduced the demand for mass severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testing. This paper proposes a sequential Bayesian approach to estimate the COVID-19 positivity rate (PR) using SARS-CoV-2 RNA concentrations measured in WW through an adaptive scheme incorporating changes in virus dynamics. PR estimates are used to compute thresholds for WW data using the CDC thresholds for low, substantial, and high transmission. The effective reproductive number estimates are calculated using PR estimates from the WW data. This approach provides insights into the dynamics of the virus evolution and an analytical framework that combines different data sources to continue monitoring the COVID-19 trends. These results can provide public health guidance to reduce the burden of future outbreaks as new variants continue to emerge. The proposed modeling framework was applied to the City of Davis and the campus of the University of California Davis.

Citywide wastewater SARS-CoV-2 levels strongly correlated with multiple disease surveillance indicators and outcomes over three COVID-19 waves

Public health surveillance systems for COVID-19 are multifaceted and include multiple indicators reflective of different aspects of the burden and spread of the disease in a community. With the emergence of wastewater disease surveillance as a powerful tool to track infection dynamics of SARS-CoV-2, there is a need to integrate and validate wastewater information with existing disease surveillance systems and demonstrate how it can be used as a routine surveillance tool. A first step toward integration is showing how it relates to other disease surveillance indicators and outcomes, such as case positivity rates, syndromic surveillance data, and hospital bed use rates. Here, we present an 86-week long surveillance study that covers three major COVID-19 surges. City-wide SARS-CoV-2 RNA viral loads in wastewater were measured across 39 wastewater treatment plants and compared to other disease metrics for the city of Houston, TX. We show that wastewater levels are strongly correlated with positivity rate, syndromic surveillance rates of COVID-19 visits, and COVID-19-related general bed use rates at hospitals. We show that the relative timing of wastewater relative to each indicator shifted across the pandemic, likely due to a multitude of factors including testing availability, health-seeking behavior, and changes in viral variants. Next, we show that individual WWTPs led city-wide changes in SARS-CoV-2 viral loads, indicating a distributed monitoring system could be used to enhance the early-warning capability of a wastewater monitoring system. Finally, we describe how the results were used in real-time to inform public health response and resource allocation.

Quantifying the relationship between sub-population wastewater samples and community-wide SARS-CoV-2 seroprevalence

Robust epidemiological models relating wastewater to community disease prevalence are lacking. Assessments of SARS-CoV-2 infection rates have relied primarily on convenience sampling, which does not provide reliable estimates of community disease prevalence due to inherent biases. This study conducted serial stratified randomized samplings to estimate the prevalence of SARS-CoV-2 antibodies in 3717 participants, and obtained weekly samples of community wastewater for SARS-CoV-2 concentrations in Jefferson County, KY (USA) from August 2020 to February 2021. Using an expanded Susceptible-Infected-Recovered model, the longitudinal estimates of the disease prevalence were obtained and compared with the wastewater concentrations using regression analysis. The model analysis revealed significant temporal differences in epidemic peaks. The results showed that in some areas, the average incidence rate, based on serological sampling, was 50 % higher than the health department rate, which was based on convenience sampling. The model-estimated average prevalence rates correlated well with the wastewater (correlation = 0.63, CI (0.31,0.83)). In the regression analysis, a one copy per ml-unit increase in weekly average wastewater concentration of SARS-CoV-2 corresponded to an average increase of 1-1.3 cases of SARS-CoV-2 infection per 100,000 residents. The analysis indicates that wastewater may provide robust estimates of community spread of infection, in line with the modeled prevalence estimates obtained from stratified randomized sampling, and is therefore superior to publicly available health data.

Wastewater Reveals the Spatiotemporal Spread of SARS-CoV-2 in the Canton of Ticino (Switzerland) during the Onset of the COVID-19 Pandemic

Wastewater-based epidemiology (WBE) has emerged as an effective tool for monitoring SARS-CoV-2 dynamics during the COVID-19 pandemic. Here, we add a spatial component to WBE and use it to investigate SARS-CoV-2 spread in the canton of Ticino during the onset of the pandemic in Switzerland (end of February 2020 to beginning of March 2020). Ticino is located at the border to Northern Italy, where a large COVID-19 outbreak occurred in February 2020. Not surprisingly, Ticino was the site of the first clinically confirmed COVID-19 case in Switzerland. We retrospectively analyzed daily influent samples from nine wastewater treatment plants in Ticino that jointly cover an area of 20 km × 60 km and 351,000 people (>99% of the population). Our result is a fine-grained view of the spatiotemporal evolution of the COVID-19 pandemic in this canton. The wastewater analysis revealed that by February 29, 2020, SARS-CoV-2 had already spread to all catchments. At the same time, only four individual cases had been clinically confirmed across the region served by the treatment plants investigated. Our results demonstrate that WBE could serve as a versatile tool to monitor the introduction and spread of an infectious agent on a regional scale. To fully exploit its utility, WBE should be implemented in real time and become an integral part of future disease surveillance efforts.

Diurnal Variability of SARS-CoV-2 RNA Concentrations in Hourly Grab Samples of Wastewater Influent during Low COVID-19 Incidence

Wastewater-based epidemiology (WBE) has been widely deployed during the COVID-19 pandemic, but with limited evaluation of the utility of discrete sampling for large sewersheds and low COVID-19 incidence. In this study, SARS-CoV-2 RNA was measured in 72 consecutive hourly influent grab samples collected at a wastewater treatment plant serving nearly 500 000 residents when incidence was low (approximately 20 cases per 100 000). We characterized diurnal variability and relationships between SARS-CoV-2 RNA detection and physicochemical covariates [flow rate, total ammonia nitrogen (TAN), and total solids (TS)]. The highest detection rate observed was 82% during the first peak flow, which occurred in the early afternoon (14:00). Higher detection rates were also observed when sampling above median TAN concentrations (71%; p < 0.01; median = 40.26 mg of NH4/L). SARS-CoV-2 RNA concentrations were weakly correlated with flow rate (Kendall's τ = 0.16; p < 0.01), TAN (τ = 0.19; p < 0.05), and TS (τ = 0.18; p < 0.01), suggesting generally low RNA sewer discharges as expected at low incidence. Our results elucidated sensible adjustments to maximize detection rates, including using multiple gene targets, collecting duplicate samples, and sampling during higher flow and TAN discharges. Optimizing the lower-incidence bounds of WBE can help assess its suitability for verifying COVID-19 reemergence or eradication.

Comparative Analysis of RNA-Extraction Approaches and Associated Influences on RT-qPCR of the SARS-CoV-2 RNA in a University Residence Hall and Quarantine Location

Wastewater-based epidemiology (WBE) provides an early warning and trend analysis approach for determining the presence of COVID-19 in a community and complements clinical testing in assessing the population level, even as viral loads fluctuate. Here, we evaluate combinations of two wastewater concentration methods (i.e., ultrafiltration and composite supernatant-solid), four pre-RNA extraction modifications, and three nucleic acid extraction kits using two different wastewater sampling locations. These consisted of a quarantine facility containing clinically confirmed COVID-19-positive inhabitants and a university residence hall. Of the combinations examined, composite supernatant-solid with pre-RNA extraction consisting of water concentration and RNA/DNA shield performed the best in terms of speed and sensitivity. Further, of the three nucleic acid extraction kits examined, the most variability was associated with the Qiagen kit. Focusing on the quarantine facility, viral concentrations measured in wastewater were generally significantly related to positive clinical cases, with the relationship dependent on method, modification, kit, target, and normalization, although results were variable-dependent on individual time points (Kendall's Tau-b (τ) = 0.17 to 0.6) or cumulatively (Kendall's Tau-b (τ) = -0.048 to 1). These observations can support laboratories establishing protocols to perform wastewater surveillance and monitoring efforts for COVID-19.