Detection and quantification of sars-cov-2 wastewater treatment plants from different cities in chile, towards to a permanent sentinel surveillance.

Background: The quantification of SARS-CoV-2 in wastewater is a tool that allows determining the trend of viral circulation in a particular geographical area. Aim(s): To quantify the SARS-CoV-2 virus in 15 wastewater treatment plants in different Chilean cities to establish a comparison with the variables of: I) Active cases per 100,000 inhabitants;ii) daily positivity (novel cases);and iii) phases of the lockdown strategy. Method(s): SARS-CoV-2 was concentrated from wastewater samples. To obtain the number of virus genomes per liter, absolute quantification was performed using qRT-PCR. Result(s): Between January and June 2021, 253 samples were processed, all of which were positive for the presence of the virus. Likewise, it will be determined that the rate of active cases per 100,000 inhabitants is the variable that best fits the trends obtained with the quantification of the viral load in wastewater. Conclusion(s): The quantification of SARS- CoV-2 in wastewater as a continuous strategy is an efficient tool to determine the trend of the viral circulation in a delimited geographical area and, combined with genomic surveillance, it can constitute an ideal sentinel surveillance alert on future outbreaks.Copyright © 2022, Sociedad Chilena de Infectologia. All rights reserved.

Not a waste: Wastewater surveillance to enhance public health

Domestic wastewater, when collected and evaluated appropriately, can provide valuable health-related information for a community. As a relatively unbiased and non-invasive approach, wastewater surveillance may complement current practices towards mitigating risks and protecting population health. Spurred by the COVID-19 pandemic, wastewater programs are now widely implemented to monitor viral infection trends in sewersheds and inform public health decision-making. This review summarizes recent developments in wastewater-based epidemiology for detecting and monitoring communicable infectious diseases, dissemination of antimicrobial resistance, and illicit drug consumption. Wastewater surveillance, a quickly advancing Frontier in environmental science, is becoming a new tool to enhance public health, improve disease prevention, and respond to future epidemics and pandemics.

Wastewater early warning system for SARS-CoV-2 outbreaks and variants in a Coruña, Spain.

Wastewater-based epidemiology has been widely used as a cost-effective method for tracking the COVID-19 pandemic at the community level. Here we describe COVIDBENS, a wastewater surveillance program running from June 2020 to March 2022 in the wastewater treatment plant of Bens in A Coruña (Spain). The main goal of this work was to provide an effective early warning tool based in wastewater epidemiology to help in decision-making at both the social and public health levels. RT-qPCR procedures and Illumina sequencing were used to weekly monitor the viral load and to detect SARS-CoV-2 mutations in wastewater, respectively. In addition, own statistical models were applied to estimate the real number of infected people and the frequency of each emerging variant circulating in the community, which considerable improved the surveillance strategy. Our analysis detected 6 viral load waves in A Coruña with concentrations between 103 and 106 SARS-CoV-2 RNA copies/L. Our system was able to anticipate community outbreaks during the pandemic with 8-36 days in advance with respect to clinical reports and, to detect the emergence of new SARS-CoV-2 variants in A Coruña such as Alpha (B.1.1.7), Delta (B.1.617.2), and Omicron (B.1.1.529 and BA.2) in wastewater with 42, 30, and 27 days, respectively, before the health system did. Data generated here helped local authorities and health managers to give a faster and more efficient response to the pandemic situation, and also allowed important industrial companies to adapt their production to each situation. The wastewater-based epidemiology program developed in our metropolitan area of A Coruña (Spain) during the SARS-CoV-2 pandemic served as a powerful early warning system combining statistical models with mutations and viral load monitoring in wastewater over time.

A systematic review on the occurrence, fate, and remediation of SARS-CoV-2 in wastewater.

The COVID-19 has been declared a pandemic by the World Health Organization. Along with impairing the respiratory system, it also affects the gastrointestinal system. By reviewing experiments on the wastewater analysis for the detection of coronavirus, this study explores the fate, persistence, and various remediation strategies for the virus removal from the wastewater. The results indicated that the virus can be detected in the wastewater samples, feces, and sewage, even before the onset of symptoms. Coronavirus can be a potential panzootic disease, as several mammalian species get infected by the deadly virus. The disinfection strategies used earlier for the treatment of wastewater are not sufficient for the removal of viruses from the wastewater. Therefore, concerted efforts should be made to understand their fate, sources, and occurrence in the environmental matrices. To prevent the spread of the panzootic disease, revised guidelines should be issued for the remediation of the virus. Recent viral remediation methods such as membrane bioreactors and advanced oxidation methods can be used. Therefore, the present review puts a light on the current knowledge on the occurrence of coronaviruses in wastewater, the possible sources, fate, and removal strategies.

Targeted amplicon sequencing of wastewater samples for detecting SARS-CoV-2 variants with high sensitivity and resolution.

Wastewater-based epidemiology (WBE) is a promising approach for monitoring the spread of SARS-CoV-2 within communities. Although qPCR-based WBE is powerful in that it allows quick and highly sensitive detection of this virus, it can provide limited information about which variants are responsible for the overall increase or decrease of this virus in sewage, and this hinders accurate risk assessments. To resolve this problem, we developed a next generation sequencing (NGS)-based method to determine the identity and composition of individual SARS-CoV-2 variants in wastewater samples. Combination and optimization of targeted amplicon-sequencing and nested PCR allowed detection of each variant with sensitivity comparable to that of qPCR. In addition, by targeting the receptor binding domain (RBD) of the S protein, which has mutations informative for variant classification, we could discriminate most variants of concern (VOC) and even sublineages of Omicron (BA.1, BA.2, BA.4/5, BA.2.75, BQ.1.1 and XBB.1). Focusing on a limited domain has a benefit of decreasing the sequencing reads. We applied this method to wastewater samples collected from a wastewater treatment plant in Kyoto city throughout 13 months (from January 2021 to February 2022) and successfully identified lineages of wild-type, alpha, delta, omicron BA.1 and BA.2 as well as their compositions in the samples. The transition of these variants was in good agreement with the epidemic situation reported in Kyoto city during that period based on clinical testing. These data indicate that our NGS-based method is useful for detecting and tracking emerging variants of SARS-CoV-2 in sewage samples. Coupled with the advantages of WBE, this method has the potential to serve as an efficient and low cost means for the community risk assessment of SARS-CoV-2 infection.

20-Month monitoring of SARS-CoV-2 in wastewater of Curitiba, in Southern Brazil.

The COVID-19 pandemic resulted in the collapse of healthcare systems and led to the development and application of several approaches of wastewater-based epidemiology to monitor infected populations. The main objective of this study was to carry out a SARS-CoV-2 wastewater based surveillance in Curitiba, Southern Brazil Sewage samples were collected weekly for 20 months at the entrance of five treatment plants representing the entire city and quantified by qPCR using the N1 marker. The viral loads were correlated with epidemiological data. The correlation by sampling points showed that the relationship between the viral loads and the number of reported cases was best described by a cross-correlation function, indicating a lag between 7 and 14 days amidst the variables, whereas the data for the entire city presented a higher correlation (0.84) with the number of positive tests at lag 0 (sampling day). The results also suggest that the Omicron VOC resulted in higher titers than the Delta VOC. Overall, our results showed that the approach used was robust as an early warning system, even with the use of different epidemiological indicators or changes in the virus variants in circulation. Therefore, it can contribute to public decision-makers and health interventions, especially in vulnerable and low-income regions with limited clinical testing capacity. Looking toward the future, this approach will contribute to a new look at environmental sanitation and should even induce an increase in sewage coverage rates in emerging countries.

Temporal study of wastewater surveillance from September 2020 to March 2021: an estimation of COVID-19 patients in Lahore, Pakistan.

The first aim of study was to quantify the viral load in the wastewater samples by RT-qPCR testing in Lahore population to estimate the number of patients affected and predict the next resurgence of COVID-19 wave in the city. The second aim of the study was to determine the hotspot areas of Lahore which remained positive more often for virus with high viral load. In this study, n = 420 sewage samples were collected on an average of two weeks intervals from 30 different sewage water disposal stations (14 sampling events) from Sept 2020 to March 2021. RNA was extracted and quantified by RT-qPCR without concentrating the virus in samples. Number of positive disposal sites (7-93%), viral load from sewage samples (100.296 to 103.034), and estimated patients (660-17,030) ranged from low to high according to the surge and restrain of 2nd and 3rd COVID-19 waves in the country. The viral load and estimated patients were reported high in January 2021 and March 2021 which were similar to the peak of 2nd and 3rd waves in Pakistan. Site 18 (Niaz Baig village DS) showed the highest viral load among all sites. Findings of the present study helped to estimate the number of patients and track the resurgence in COVID-19 waves in Lahore particularly, and in Punjab generally. Furthermore, it emphasizes the role of wastewater-based epidemiology to help policymakers strengthen the quarantine measures along with immunization to overcome enteric viral diseases. Local and national stake holders should work in collaboration to improve the environmental hygiene to control the disease.

Monitoring influenza and respiratory syncytial virus in wastewater. Beyond COVID-19.

Wastewater-based surveillance can be a valuable tool to monitor viral circulation and serve as an early warning system. For respiratory viruses that share similar clinical symptoms, namely SARS-CoV-2, influenza, and respiratory syncytial virus (RSV), identification in wastewater may allow differentiation between seasonal outbreaks and COVID-19 peaks. In this study, to monitor these viruses as well as standard indicators of fecal contamination, a weekly sampling campaign was carried out for 15 months (from September 2021 to November 2022) in two wastewater treatment plants that serve the entire population of Barcelona (Spain). Samples were concentrated by the aluminum hydroxide adsorption-precipitation method and then analyzed by RNA extraction and RT-qPCR. All samples were positive for SARS-CoV-2, while the positivity rates for influenza virus and RSV were significantly lower (10.65 % for influenza A (IAV), 0.82 % for influenza B (IBV), 37.70 % for RSV-A and 34.43 % for RSV-B). Gene copy concentrations of SARS-CoV-2 were often approximately 1 to 2 logarithmic units higher compared to the other respiratory viruses. Clear peaks of IAV H3:N2 in February and March 2022 and RSV in winter 2021 were observed, which matched the chronological incidence of infections recorded in the Catalan Government clinical database. In conclusion, the data obtained from wastewater surveillance provided new information on the abundance of respiratory viruses in the Barcelona area and correlated favorably with clinical data.

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.

Is SARS-CoV-2 a concern in the largest wastewater treatment plant in middle east?

The surveillance of wastewater treatment plant (WWTP) as the end point of SARS-CoV-2 shed from infected people arise a speculation on transmission of this virus of concern from WWTP in epidemic period. To this end, the present study was developed to comprehensively investigate the presence of SARS-CoV-2 in raw wastewater, effluent and air inhaled by workers and employee in the largest WWTP in Tehran for one-year study period. The monthly raw wastewater, effluent and air samples of WWTP were taken and the SARS-CoV-2 RNA were detected using QIAamp Viral RNA Mini Kit and real-time RT-PCR assay. According to results, the speculation on the presence of SARS-CoV-2 was proved in WWTP by detection this virus in raw wastewater. However, no SARS-CoV-2 was found in both effluent and air of WWTP; this presents the low or no infection for workers and employee in WWTP. Furthermore, further research are needed for detection the SARS-CoV-2 in solid and biomass produced from WWTP processes due to flaks formation, followed by sedimentation in order to better understand the wastewater-based epidemiology and preventive measurement for other epidemics probably encountered in the future.

Wastewater-based prediction of COVID-19 cases using a random forest algorithm with strain prevalence data: A case study of five municipalities in Latvia.

Wastewater-based epidemiology (WBE) is a rapid and cost-effective method that can detect SARS-CoV-2 genomic components in wastewater and can provide an early warning for possible COVID-19 outbreaks up to one or two weeks in advance. However, the quantitative relationship between the intensity of the epidemic and the possible progression of the pandemic is still unclear, necessitating further research. This study investigates the use of WBE to rapidly monitor the SARS-CoV-2 virus from five municipal wastewater treatment plants in Latvia and forecast cumulative COVID-19 cases two weeks in advance. For this purpose, a real-time quantitative PCR approach was used to monitor the SARS-CoV-2 nucleocapsid 1 (N1), nucleocapsid 2 (N2), and E genes in municipal wastewater. The RNA signals in the wastewater were compared to the reported COVID-19 cases, and the strain prevalence data of the SARS-CoV-2 virus were identified by targeted sequencing of receptor binding domain (RBD) and furin cleavage site (FCS) regions employing next-generation sequencing technology. The model methodology for a linear model and a random forest was designed and carried out to ascertain the correlation between the cumulative cases, strain prevalence data, and RNA concentration in the wastewater to predict the COVID-19 outbreak and its scale. Additionally, the factors that impact the model prediction accuracy for COVID-19 were investigated and compared between linear and random forest models. The results of cross-validated model metrics showed that the random forest model is more effective in predicting the cumulative COVID-19 cases two weeks in advance when strain prevalence data are included. The results from this research help inform WBE and public health recommendations by providing valuable insights into the impact of environmental exposures on health outcomes.

Robust smoothing of left-censored time series data with a dynamic linear model to infer SARS-CoV-2 RNA concentrations in wastewater

Wastewater sampling for the detection and monitoring of SARS-CoV-2 has been developed and applied at an unprecedented pace, however uncertainty remains when interpreting the measured viral RNA signals and their spatiotemporal variation. The proliferation of measurements that are below a quantifiable threshold, usually during non-endemic periods, poses a further challenge to interpretation and time-series analysis of the data. Inspired by research in the use of a custom Kalman smoother model to estimate the true level of SARS-CoV-2 RNA concentrations in wastewater, we propose an alternative left-censored dynamic linear model. Cross-validation of both models alongside a simple moving average, using data from 286 sewage treatment works across England, allows for a comprehensive validation of the proposed approach. The presented dynamic linear model is more parsimonious, has a faster computational time and is represented by a more flexible modelling framework than the equivalent Kalman smoother. Furthermore we show how the use of wastewater data, transformed by such models, correlates more closely with regional case rate positivity as published by the Office for National Statistics (ONS) Coronavirus (COVID-19) Infection Survey. The modelled output is more robust and is therefore capable of better complementing traditional surveillance than untransformed data or a simple moving average, providing additional confidence and utility for public health decision making. © 2023, American Institute of Mathematical Sciences. All rights reserved.

The fate of SARS-CoV-2 viral RNA in coastal New England wastewater treatment plants

Municipal sewage carries SARS-CoV-2 viruses shed in the human stool by infected individuals to wastewater treatment plants (WWTPs). It is well-established that increasing prevalence of COVID-19 in a community increases the viral load in its WWTPs. Despite the fact that wastewater treatment facilities serve a critical role in protecting downstream human and environmental health through removal or inactivation of the virus, little is known about the fate of the virus along the treatment train. To assess the efficacy of differing WWTP size and treatment processes in viral RNA removal we quantified two SARS-CoV-2 nucleocapsid (N) biomarkers (N1 and N2) in both liquid and solids phases for multiple treatment train locations from seven coastal New England WWTPs. SARS-CoV-2 biomarkers were commonly detected in the influent, primary treated, and sludge samples (returned activated sludge, waste activated sludge, and digested sludge), and not detected after secondary clarification processes or disinfection. Solid fractions had 470 to 3,700-fold higher concentrations of viral biomarkers than liquid fractions, suggesting considerably higher affinity of the virus for the solid phase. Our findings indicate that a variety of wastewater treatment designs are efficient at achieving high removal of SARS CoV-2 from effluent;however, quantifiable viral RNA was commonly detected in wastewater solids at various points in the facility. This study supports the important role municipal wastewater treatment facilities serve in reducing the discharge of SARS-CoV-2 viral fragments to the environment and highlights the need to better understand the fate of this virus in wastewater solids.

An innovative approach for predicting pandemic hotspots in complex wastewater networks using graph theory coupled with fuzzy logic.

Early prediction of COVID-19 infected communities (potential hotspots) is essential to limit the spread of virus. Diagnostic testing has limitations in big populations because it cannot deliver information at a fast enough rate to stop the spread in its early phases. Wastewater based epidemiology (WBE) experiments showed promising results for brisk detection of 'SARS CoV-2' RNA in urban wastewater. However, a systematic and targeted approach to track COVID-19 virus in the complex wastewater networks at a community level is lacking. This research combines graph network (GN) theory with fuzzy logic to determine the chances of a specific community being a COVID-19 hotspot in a wastewater network. To detect 'SARS-CoV-2' RNA, GN divides wastewater network into communities and fuzzy logic-based inference system is used to identify targeted communities. For the propose of tracking, 4000 sample cases from Minnesota (USA) were tested based on various contributing factors. With a probability score of greater than 0.8, 42% of cases were likely to be designated as COVID-19 hotspots based on multiple demographic characteristics. The research enhances the conventional WBE approach through two novel aspects, viz. (1) by integrating graph theory with fuzzy logic for quick prediction of potential hotspot along with its likelihood percentage in a wastewater network, and (2) incorporating the uncertainty associated with COVID-19 contributing factors using fuzzy membership functions. The targeted approach allows for rapid testing and implementation of vaccination campaigns in potential hotspots. Consequently, governmental bodies can be well prepared to check future pandemics and variant spreading in a more planned manner. Supplementary Information: The online version contains supplementary material available at 10.1007/s00477-023-02468-3.

SARS-CoV-2 WASTEWATER SURVEILLANCE IN A US JAIL: CORRELATION, FEASIBILITY, NEXT STEPS

Background: Jails house vulnerable persons. Crowded conditions, restricted access to medical care, and limited resources facilitate infectious disease outbreaks, particularly for airborne, highly transmissible diseases like COVID-19 (C19). Wastewater-Based Surveillance (WBS) is a low-cost, highly sensitive, non-invasive method that can provide an early warning of C19 surges in communities. We examined the value of SARS-CoV-2 WBS for a mega-jail. Method(s): 28-week study period: 10/20/21- 5/5/22. Wastewater samples were collected x 25 weeks;SARS-CoV-2 RNA was measured using RT-qPCR. We sampled one manhole serving multiple housing units. C19 rapid test data on jail entrants were summarized daily by the jail;16 mass PCR screenings using selfcollected nasal swabs were conducted by the study team. Individual diagnostic tests were collated and analyzed on a weekly basis. Data were summarized by % of the tested jailed individuals found infected. The Spearman correlation coefficient between weekly SARS-CoV-2 RNA in wastewater and % of positive (pos) C19 diagnostic tests were calculated;we also used linear regression to assess the predictability between paired Ct values and weekly % of pos diagnostic tests. Result(s): Weekly WBS coupled with periodic mass testing of jailed individuals was feasible. The efficiency of gathering individual nasal swabs increased to 3 tests collected per minute through a CQI process. PCR signal strength for SARSCoV- 2 RNA in jail wastewater correlated with the % of jail residents tested who had C19. The mean RT-qPCR Cycle threshold (Ct) value was 35.2. Overall, 3.4% of nasal swabs were pos. A strong inverse correlation was observed between % nasal swab pos and WBS Ct value (Figure.) The Spearman correlation coefficient was r= 0.628;linear regression likewise showed a similar correlation. Conclusion(s): Weekly WBS results for C19 correlated with the proportion of C19 individual test results. WBS proved to be a practical strategy to surveil for C19 in this jail setting. We are developing means to identify exact source, by housing unit, of wastewater with positive signal. Future studies will explore WBS for Mpox and HIV in correctional facilities. HIV RNA can be found in wastewater specimens;whether WBS for HIV in congregate facilities is feasible remains an open question.

Wastewater surveillance could serve as a pandemic early warning system for COVID‐19 and beyond

Wastewater‐based surveillance can be used as an early warning system to identify COVID‐19 outbreaks because the viral load can be observed in sewage before it is clinically verified. Wastewater surveillance of SARS‐CoV‐2 can trace the transmission dynamics of infection in communities when using the scale of a wastewater diversion and treatment system. Using this early detection method can help protect human health and mitigate socio‐economic losses. It can help quantify the epidemiological data of a given population in real‐time and circumvent the need for other epidemiological indicators. There are challenges in using this technique in areas with underdeveloped sewerage infrastructure. It is especially the case in developing nations where uniform protocols for viral detection are lacking, and wastewater is heterogeneous because of environmental and operational conditions. This article explains the need for and importance of wastewater‐based surveillance for SARS‐CoV‐2. It lays out the most recent methodological approaches for detecting SARS‐CoV‐2 in municipal wastewater and outlines the main challenges associated with wastewater‐based epidemiology (WBE). The article includes a case study of surveillance work across India to demonstrate how a developing nation manages research and locational challenges. The socio‐economic, ethical, and policy dimensions of WBE for SARS‐CoV‐2 are also discussed.This article is categorized under: Engineering Water > Water, Health, and Sanitation Engineering Water > Sustainable Engineering of Water Engineering Water > Methods [ FROM AUTHOR] Copyright of WIRES Water is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

ASSOCIATION BETWEEN OBSERVED MASKING AND SARS-CoV-2 PRESENCE IN SCHOOL WASTEWATER

Background: The SARS-CoV-2 virus is airborne and highly transmissible. Masking is an important strategy for source control and personal protection. The American Academy of Pediatrics recommends masking as part of a comprehensive strategy to reduce the spread of COVID-19 and respiratory diseases in school settings, however the effectiveness of school masking policies has been heavily debated. Previous studies of masking effectiveness have been limited by the use of self-reported masking behavior, policies as a proxy for masking behaviors, and/or case surveillance data that are biased by access to testing Methods: The Safer at School Early Alert (SASEA) project provided daily wastewater SARS-CoV-2 surveillance for elementary schools serving historically marginalized communities in San Diego County. We previously found that daily wastewater surveillance can identify 95% of PCR-detectable COVID-19 cases on campus. Between March 2 and May 27, 2022, we randomly selected 10 schools from the SASEA project for bi-weekly systematic observations of masking behaviors of students, staff, and parents. Each school was observed by 4 trained observers from the time school let out until all individuals had left. Observers counted the total number of adults and children and whether they were fully masked (nose and mouth covered), partially masked, or unmasked. We built a logistic regression model to measure the association between positive wastewater signals in the 5 days following an observation event (outcome) and the percentage of individuals who were observed fully masked vs partially or unmasked (primary predictor). Result(s): We conducted 60 observation events over 6 weeks, during which positive wastewater signals- suggesting the presence of at least one COVID-19 case on campus-occurred on 9 days. On average, 38.6% of individuals were observed fully masked. After adjusting for intra-site correlation, observation week, current case rate per 100,000 in the school ZIP code and vaccination rate in the school ZIP code, we found that the odds of a positive wastewater signal in the 5 days after observation decreased by 47% (aOR 0.53, 95% CI: 0.28 - 0.99) for each 10% increase in the proportion of fully masked individuals. Conclusion(s): Masking is an effective strategy to prevent the spread of COVID-19 in school settings. Even a relatively small increase in the proportion of individuals masking can potentially lead to a significant difference in epidemic spread.