SARS-CoV-2 RNA concentrations in wastewater foreshadow dynamics and clinical presentation of new COVID-19 cases

QuaID: Enabling Earlier Detection of Recently Emerged SARS-CoV-2 Variants of Concern in Wastewater

As clinical testing declines, wastewater monitoring can provide crucial surveillance on the emergence of SARS-CoV-2 variants of concern (VoC) in communities. Multiple recent studies support that wastewater-based SARS-CoV-2 detection of circulating VoC can precede clinical cases by up to two weeks. Furthermore, wastewater based epidemiology enables wide population-based screening and study of viral evolutionary dynamics. However, highly sensitive detection of emerging variants remains a complex task due to the pooled nature of environmental samples and genetic material degradation. In this paper we propose quasi-unique mutations for VoC identification, implemented in a novel bioinformatics tool (QuaID) for VoC detection based on quasi-unique mutations. The benefits of QuaID are three-fold: (i) provides up to 3 week earlier VoC detection compared to existing approaches, (ii) enables more sensitive VoC detection, which is shown to be tolerant of >50% mutation drop-out, and (iii) leverages all mutational signatures, including insertions & deletions.

Rapid displacement of SARS-CoV-2 variant Delta by Omicron revealed by allele-specific PCR in wastewater

On November 26, 2021, the B.1.1.529 COVID-19 variant was classified as the Omicron variant of concern (VOC). Reports of higher transmissibility and potential immune evasion triggered flight bans and heightened health control measures across the world to stem its distribution. Wastewater-based surveillance has demonstrated to be a useful complement for clinical community-based tracking of SARS-CoV-2 variants. Using design principles of our previous assays that detect SARS-CoV-2 variants (Alpha and Delta), we developed an allele-specific RT-qPCR assay which simultaneously targets the stretch of mutations from Q493R to Q498R for quantitative detection of the Omicron variant in wastewater. We report their validation against 10-month longitudinal samples from the influent of a wastewater treatment plant in Italy. SARS-CoV-2 RNA concentrations and variant frequencies in wastewater determined using these variant assays agree with clinical cases, revealing rapid displacement of the Delta variant by the Omicron variant within three weeks. These variant trends, when mapped against vaccination rates, support clinical studies that found the rapid emergence of SARS-CoV-2 Omicron variant being associated with an infection advantage over Delta in vaccinated persons. These data reinforce the versatility, utility and accuracy of these open-sourced methods using allele-specific RT-qPCR for tracking the dynamics of variant displacement in communities through wastewater for informed public health responses.

A simple SEIR-V model to estimate COVID-19 prevalence and predict SARS-CoV-2 transmission using wastewater-based surveillance data

Wastewater-based surveillance (WBS) has been widely used as a public health tool to monitor SARS-CoV-2 transmission. However, epidemiological inference from WBS data remains understudied and limits its application. In this study, we have established a quantitative framework to estimate COVID-19 prevalence and predict SARS-CoV-2 transmission through integrating WBS data into an SEIR-V model. We conceptually divide the individual-level viral shedding course into exposed, infectious, and recovery phases as an analogy to the compartments in population-level SEIR model. We demonstrated that the temperature effect on viral losses in the sewer can be straightforwardly incorporated in our framework. Using WBS data from the second wave of the pandemic (Oct 02, 2020 â€" Jan 25, 2021) in the Great Boston area, we showed that the SEIR-V model successfully recapitulates the temporal dynamics of viral load in wastewater and predicts the true number of cases peaked earlier and higher than the number of reported cases by 16 days and 8.6 folds ( R = 0.93), respectively. This work showcases a simple, yet effective method to bridge WBS and quantitative epidemiological modeling to estimate the prevalence and transmission of SARS-CoV-2 in the sewershed, which could facilitate the application of wastewater surveillance of infectious diseases for epidemiological inference and inform public health actions.

Real-time allelic assays of SARS-CoV-2 variants to enhance sewage surveillance

To effectively control the ongoing outbreaks of fast-spreading SARS-CoV-2 variants, there is an urgent need to add rapid variant detection and discrimination methods to the existing sewage surveillance systems established worldwide. We designed eight assays based on allele-specific RT-qPCR for real-time allelic discrimination of eight SARS-CoV-2 variants (Alpha, Beta, Gamma, Delta, Omicron, Lambda, Mu, and Kappa) in sewage. In silico analysis of the designed assays for identifying SARS-CoV-2 variants using more than four million SARS-CoV-2 variant sequences yielded ∼100% specificity and >90% sensitivity. All assays could sensitively discriminate and quantify target variants at levels as low as 10 viral RNA copy/µL with minimal cross-reactivity to the corresponding nontarget genotypes, even for sewage samples containing mixtures of SARS-CoV-2 variants with differential abundances. Integration of this method into the routine sewage surveillance in Hong Kong successfully identified the Beta variant in a community sewage. Complete concordance was observed between the results of viral whole-genome sequencing and those of our novel assays in sewage samples that contained exclusively the Delta variant discharged by a clinically diagnosed COVID-19 patient living in a quarantine hotel. Our assays in this method also provided real-time discrimination of the newly emerging Omicron variant in sewage two days prior to clinical test results in another quarantine hotel in Hong Kong. These novel allelic discrimination assays offer a rapid, sensitive, and specific way for detecting multiple SARS-CoV-2 variants in sewage and can be directly integrated into the existing sewage surveillance systems.

Making waves: Wastewater surveillance of SARS-CoV-2 in an endemic future

Wastewater-based surveillance (WBS) has been widely used as a public health tool to monitor the emergence and spread of SARS-CoV-2 infections in populations during the COVID-19 pandemic. Coincident with the global vaccination efforts, the world is also enduring new waves of SARS-CoV-2 variants. Reinfections and vaccine breakthroughs suggest an endemic future where SARS-CoV-2 continues to persist in the general population. In this treatise, we aim to explore the future roles of wastewater surveillance. Practically, WBS serves as a relatively affordable and non-invasive tool for mass surveillance of SARS-CoV-2 infection while minimizing privacy concerns, attributes that make it extremely suited for its long-term usage. In an endemic future, the utility of WBS will include 1) monitoring the trend of viral loads of targets in wastewater for quantitative estimate of changes in disease incidence; 2) sampling upstream for pinpointing infections in neighborhoods and at the building level; 3) integrating wastewater and clinical surveillance for cost-efficient population surveillance; and 4) genome sequencing wastewater samples to track circulating and emerging variants in the population. We further discuss the challenges and future developments of WBS to reduce inconsistencies in wastewater data worldwide, improve its epidemiological inference, and advance viral tracking and discovery as a preparation for the next viral pandemic.

Monitoring of SARS-CoV-2 in sewersheds with low COVID-19 cases using a passive sampling technique

Monitoring SARS-CoV-2 RNA in sewer systems, upstream of a wastewater treatment plant, is an effective approach for understanding potential COVID-19 transmission in communities with higher spatial resolutions. Passive sampling devices provide a practical solution for frequent sampling within sewer networks where the use of autosamplers is not feasible. Currently, the design of upstream sampling is impeded by limited understanding of the fate of SARS-CoV-2 RNA in sewers and the sensitivity of passive samplers for the number of infected individuals in a catchment. In this study, passive samplers containing electronegative membranes were applied for at least 24-h continuous sampling in sewer systems. When monitoring SARS-CoV-2 along a trunk sewer pipe, we found RNA signals decreased proportionally to increasing dilutions, with non-detects occurring at the end of pipe. The passive sampling membranes were able to detect SARS-CoV-2 shed by >2 COVID-19 infection cases in 10,000 people. Moreover, upstream monitoring in multiple sewersheds using passive samplers identified the emergence of SARS-CoV-2 in wastewater one week ahead of clinical reporting and reflected the spatiotemporal spread of a COVID-19 cluster within a city. This study provides important information to guide the development of wastewater surveillance strategies at catchment and subcatchment levels using different sampling techniques.

SARS-CoV-2 RNA in Wastewater Was Highly Correlated With the Number of COVID-19 Cases During the Fourth and Fifth Pandemic Wave in Kobe City, Japan

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause of the current coronavirus disease 2019 (COVID-19) pandemic and associated respiratory infections, has been detected in the feces of patients. Therefore, determining SARS-CoV-2 RNA levels in sewage may help to predict the number of infected people within the area. In this study, we quantified SARS-CoV-2 RNA copy number using reverse transcription quantitative real-time PCR with primers and probes targeting the N gene, which allows the detection of both wild-type and variant strain of SARS-CoV-2 in sewage samples from two wastewater treatment plants (WWTPs) in Kobe City, Japan, during the fourth and fifth pandemic waves of COVID-19 between February 2021 and October 2021. The wastewater samples were concentrated via centrifugation, yielding a pelleted solid fraction and a supernatant, which was subjected to polyethylene glycol (PEG) precipitation. The SARS-CoV-2 RNA was significantly and frequently detected in the solid fraction than in the PEG-precipitated fraction. In addition, the copy number in the solid fraction was highly correlated with the number of COVID-19 cases in the WWTP basin (WWTP-A: r = 0.8205, p < 0.001; WWTP-B: r = 0.8482, p < 0.001). The limit of capturing COVID-19 cases per 100,000 people was 0.75 cases in WWTP-A and 1.20 cases in WWTP-B, respectively. Quantitative studies of RNA in sewage can be useful for administrative purposes related to public health, including issuing warnings and implementing preventive measures within sewage basins.

SARS-CoV-2 Whole-Genome Sequencing Using Oxford Nanopore Technology for Variant Monitoring in Wastewaters

Since the beginning of the Coronavirus Disease-19 (COVID-19) pandemic, multiple Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) mutations have been reported and led to the emergence of variants of concern (VOC) with increased transmissibility, virulence or immune escape. In parallel, the observation of viral fecal shedding led to the quantification of SARS-CoV-2 genomes in wastewater, providing information about the dynamics of SARS-CoV-2 infections within a population including symptomatic and asymptomatic individuals. Here, we aimed to adapt a sequencing technique initially designed for clinical samples to apply it to the challenging and mixed wastewater matrix, and hence identify the circulation of VOC at the community level. Composite raw sewage sampled over 24 h in two wastewater-treatment plants (WWTPs) from a city in western France were collected weekly and SARS-CoV-2 quantified by RT-PCR. Samples collected between October 2020 and May 2021 were submitted to whole-genome sequencing (WGS) using the primers and protocol published by the ARTIC Network and a MinION Mk1C sequencer (Oxford Nanopore Technologies, Oxford, United Kingdom). The protocol was adapted to allow near-full genome coverage from sewage samples, starting from ∼5% to reach ∼90% at depth 30. This enabled us to detect multiple single-nucleotide variant (SNV) and assess the circulation of the SARS-CoV-2 VOC Alpha, Beta, Gamma, and Delta. Retrospective analysis of sewage samples shed light on the emergence of the Alpha VOC with detection of first co-occurring signature mutations in mid-November 2020 to reach predominance of this variant in early February 2021. In parallel, a mutation-specific qRT-PCR assay confirmed the spread of the Alpha VOC but detected it later than WGS. Altogether, these data show that SARS-CoV-2 sequencing in sewage can be used for early detection of an emerging VOC in a population and confirm its ability to track shifts in variant predominance.

Microbiome Analysis for Wastewater Surveillance during COVID-19

Wastewater surveillance (WS), when coupled with advanced molecular techniques, offers near real-time monitoring of community-wide transmission of SARS-CoV-2 and allows assessing and mitigating COVID-19 outbreaks, by evaluating the total microbial assemblage in a community. Composite wastewater samples (24 h) were collected weekly from a manhole between December 2020 and November 2021 in Maryland, USA. RT-qPCR results showed concentrations of SARS-CoV-2 RNA recovered from wastewater samples reflected incidence of COVID-19 cases. When a drastic increase in COVID-19 was detected in February 2021, samples were selected for microbiome analysis (DNA metagenomics, RNA metatranscriptomics, and targeted SARS-CoV-2 sequencing). Targeted SARS-CoV-2 sequencing allowed for detection of important genetic mutations, such as spike: K417N, D614G, P681H, T716I, S982A, and D1118H, commonly associated with increased cell entry and reinfection. Microbiome analysis (DNA and RNA) provided important insight with respect to human health-related factors, including detection of pathogens and their virulence/antibiotic resistance genes. Specific microbial species comprising the wastewater microbiome correlated with incidence of SARS-CoV-2 RNA, suggesting potential association with SARS-CoV-2 infection. Climatic conditions, namely, temperature, were related to incidence of COVID-19 and detection of SARS-CoV-2 in wastewater, having been monitored as part of an environmental risk score assessment carried out in this study. In summary, the wastewater microbiome provides useful public health information, and hence, a valuable tool to proactively detect and characterize pathogenic agents circulating in a community. In effect, metagenomics of wastewater can serve as an early warning system for communicable diseases, by providing a larger source of information for health departments and public officials. IMPORTANCE Traditionally, testing for COVID-19 is done by detecting SARS-CoV-2 in samples collected from nasal swabs and/or saliva. However, SARS-CoV-2 can also be detected in feces of infected individuals. Therefore, wastewater samples can be used to test all individuals of a community contributing to the sewage collection system, i.e., the infrastructure, such as gravity pipes, manholes, tanks, lift stations, control structures, and force mains, that collects used water from residential and commercial sources and conveys the flow to a wastewater treatment plant. Here, we profile community wastewater collected from a manhole, detect presence of SARS-CoV-2, identify genetic mutations of SARS-CoV-2, and perform COVID-19 risk score assessment of the study area. Using metagenomics analysis, we also detect other microorganisms (bacteria, fungi, protists, and viruses) present in the samples. Results show that by analyzing all microorganisms present in wastewater, pathogens circulating in a community can provide an early warning for contagious diseases.

Detection of SARS-CoV-2 RNA throughout wastewater treatment plants and a modeling approach to understand COVID-19 infection dynamics in Winnipeg, Canada

Although numerous studies have detected SARS-CoV-2 RNA in wastewater and attempted to find correlations between the concentration of SARS-CoV-2 RNA and the number of cases, no consensus has been reached on sample collection and processing, and data analysis. Moreover, the fate of SARS-CoV-2 in wastewater treatment plants is another issue, specifically regarding the discharge of the virus into environmental settings and the water cycle. The current study monitored SARS-CoV-2 RNA in influent and effluent wastewater samples with three different concentration methods and sludge samples over six months (July to December 2020) to compare different virus concentration methods, assess the fate of SARS-CoV-2 RNA in wastewater treatment plants, and describe the potential relationship between SARS-CoV-2 RNA concentrations in influent and infection dynamics. Skimmed milk flocculation (SMF) resulted in 15.27 ± 3.32% recovery of an internal positive control, Armored RNA, and a high positivity rate of SARS-CoV-2 RNA in stored wastewater samples compared to ultrafiltration methods employing a prefiltration step to eliminate solids in fresh wastewater samples. Our results suggested that SARS-CoV-2 RNA may predominate in solids, and therefore, concentration methods focusing on both supernatant and solid fractions may result in better recovery. SARS-CoV-2 RNA was detected in influent and primary sludge samples but not in secondary and final effluent samples, indicating a significant reduction during primary and secondary treatments. SARS-CoV-2 RNA was first detected in influent on September 30th, 2020. A decay-rate formula was applied to estimate initial concentrations of late-processed samples with SMF. A model based on shedding rate and new cases was applied to estimate SARS-CoV-2 RNA concentrations and the number of active shedders. Inferred sensitivity of observed and modeled concentrations to the fluctuations in new cases and test-positivity rates indicated a potential contribution of newly infected individuals to SARS-CoV-2 RNA loads in wastewater.

Can spike fragments of SARS-CoV-2 induce genomic instability and DNA damage in the guppy, Poecilia reticulate? An unexpected effect of the COVID-19 pandemic

The identification of SARS-CoV-2 particles in wastewater and freshwater ecosystems has raised concerns about its possible impacts on non-target aquatic organisms. In this particular, our knowledge of such impacts is still limited, and little attention has been given to this issue. Hence, in our study, we aimed to evaluate the possible induction of mutagenic (via micronucleus test) and genotoxic (via single cell gel electrophoresis assay, comet assay) effects in Poecilia reticulata adults exposed to fragments of the Spike protein of the new coronavirus at the level of 40 μg/L, denominated PSPD-2002. As a result, after 10 days of exposure, we have found that animals exposed to the peptides demonstrated an increase in the frequency of erythrocytic nuclear alteration (ENA) and all parameters assessed in the comet assay (length tail, %DNA in tail and Olive tail moment), suggesting that PSPD-2002 peptides were able to cause genomic instability and erythrocyte DNA damage. Besides, these effects were significantly correlated with the increase in lipid peroxidation processes [inferred by the high levels of malondialdehyde (MDA)] reported in the brain and liver of P. reticulata and with the reduction of the superoxide dismutase (SOD) and catalase (CAT) activity. Thus, our study constitutes a new insight and promising investigation into the toxicity associated with the dispersal of SARS-CoV-2 peptide fragments in freshwater environments.

Early warning of a COVID-19 surge on a university campus based on wastewater surveillance for SARS-CoV-2 at residence halls

As COVID-19 continues to spread globally, monitoring the disease at different scales is critical to support public health decision making. Surveillance for SARS-CoV-2 RNA in wastewater can supplement surveillance based on diagnostic testing. In this paper, we report the results of wastewater-based COVID-19 surveillance on Emory University campus that included routine sampling of sewage from a hospital building, an isolation/quarantine building, and 21 student residence halls between July 13th, 2020 and March 14th, 2021. We examined the sensitivity of wastewater surveillance for detecting COVID-19 cases at building level and the relation between Ct values from RT-qPCR results of wastewater samples and the number of COVID-19 patients residing in the building. Our results show that weekly wastewater surveillance using Moore swab samples was not sensitive enough (6 of 63 times) to reliably detect one or two sporadic cases in a residence building. The Ct values of the wastewater samples over time from the same sampling location reflected the temporal trend in the number of COVID-19 patients in the isolation/quarantine building and hospital (Pearson's r < -0.8), but there is too much uncertainty to directly estimate the number of COVID-19 cases using Ct values. After students returned for the spring 2021 semester, SARS-CoV-2 RNA was detected in the wastewater samples from most of the student residence hall monitoring sites one to two weeks before COVID-19 cases surged on campus. This finding suggests that wastewater-based surveillance can be used to provide early warning of COVID-19 outbreaks at institutions.

Use of sewage surveillance for COVID-19 to guide public health response: A case study in Hong Kong

Sewage surveillance could help develop proactive response to the Coronavirus Disease 2019 (COVID-19) pandemic, but currently there are limited reports about examples in practical exercises. Here, we report a use case of intensified sewage surveillance to initiate public health action to thwart a looming Delta variant outbreak in Hong Kong. On 21 June 2021, albeit under basically contained COVID-19 situation in Hong Kong, routine sewage surveillance identified a high viral load of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in a sewage sample from one site covering over 33,000 population, suggesting infected cases living in the respective sewershed. The use of a newly developed method based on allele-specific real-time quantitative polymerase chain reaction (AS RT-qPCR) served to alert the first documentation of the Delta variant in local community sewage three days before the case was confirmed to be a Delta variant carrier. Intensified sewage surveillance was triggered. Targeted upstream sampling at sub-sewershed areas pinpointed the source of positive viral signal across spatial scales from sewershed to building level, and assisted in determining the specific area for issuing a compulsory testing order for individuals on 23 June 2021. A person who lived in a building with the positive result of sewage testing was confirmed to be infected with COVID-19 on 24 June 2021. Viral genome sequences determined from the sewage sample were compared to those from the clinic specimens of the matched patient, and confirmed that the person was the source of the positive SARS-CoV-2 signal in the sewage sample. This study could help build confidences for public health agencies in using the sewage surveillance in their own communities.

Factors influencing SARS-CoV-2 RNA concentrations in wastewater up to the sampling stage: A systematic review

Wastewater-based surveillance (WBS) for SARS-CoV-2 RNA is a promising complementary approach to monitor community viral circulation. A myriad of factors, however, can influence RNA concentrations in wastewater, impeding its epidemiological value. This article aims to provide an overview and discussion of factors up to the sampling stage that impact SARS-CoV-2 RNA concentration estimates in wastewater. To this end, a systematic review was performed in three databases (MEDLINE, Web of Science and Embase) and two preprint servers (MedRxiv and BioRxiv). Two authors independently screened and selected articles published between January 1, 2019 and May 4, 2021. A total of 22 eligible articles were included in this systematic review. The following factors up to sampling were identified to have an influence on SARS-CoV-2 RNA concentrations in wastewater and its interpretation: (i) shedding-related factors, including faecal shedding parameters (i.e. shedding pattern, recovery, rate, and load distribution), (ii) population size, (iii) in-sewer factors, including solid particles, organic load, travel time, flow rate, wastewater pH and temperature, and (iv) sampling strategy. In conclusion, factors influencing SARS-CoV-2 RNA concentration estimates in wastewater were identified and research gaps were discussed. The identification of these factors supports the need for further research on WBS for COVID-19.

Wastewater surveillance of SARS-CoV-2 mutational profiles at a university and its surrounding community reveals a 20G outbreak on campus

Wastewater surveillance of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been leveraged during the Coronavirus Disease 2019 (COVID-19) pandemic as a public health tool at the community and building level. In this study, we compare the sequence diversity of SARS-CoV-2 amplified from wastewater influent to the Columbia, South Carolina, metropolitan wastewater treatment plant (WWTP) and the University of South Carolina campus during September 2020, which represents the peak of COVID-19 cases at the university during 2020. A total of 92 unique mutations were detected across all WWTP influent and campus samples, with the highest frequency mutations corresponding to the SARS-CoV-2 20C and 20G clades. Signature mutations for the 20G clade dominated SARS-CoV-2 sequences amplified from localized wastewater samples collected at the University of South Carolina, suggesting that the peak in COVID-19 cases during early September 2020 was caused by an outbreak of the 20G lineage. Thirteen mutations were shared between the university building-level wastewater samples and the WWTP influent collected in September 2020, 62% of which were nonsynonymous substitutions. Co-occurrence of mutations was used as a similarity metric to compare wastewater samples. Three pairs of mutations co-occurred in university wastewater and WWTP influent during September 2020. Thirty percent of the detected mutations, including 12 pairs of concurrent mutations, were only detected in university samples. This report affirms the close relationship between the prevalent SARS-CoV-2 genotypes of the student population at a university campus and those of the surrounding community. However, this study also suggests that wastewater surveillance at the building-level at a university offers important insight by capturing sequence diversity that was not apparent in the WWTP influent, thus offering a balance between the community-level wastewater and clinical sequencing.

Metrics to relate COVID-19 wastewater data to clinical testing dynamics

Wastewater surveillance has emerged as a useful tool in the public health response to the COVID-19 pandemic. While wastewater surveillance has been applied at various scales to monitor population-level COVID-19 dynamics, there is a need for quantitative metrics to interpret wastewater data in the context of public health trends. 24-hour composite wastewater samples were collected from March 2020 through May 2021 from a Massachusetts wastewater treatment plant and SARS-CoV-2 RNA concentrations were measured using RT-qPCR. The relationship between wastewater copy numbers of SARS-CoV-2 gene fragments and COVID-19 clinical cases and deaths varies over time. We demonstrate the utility of three new metrics to monitor changes in COVID-19 epidemiology: (1) the ratio between wastewater copy numbers of SARS-CoV-2 gene fragments and clinical cases (WC ratio), (2) the time lag between wastewater and clinical reporting, and (3) a transfer function between the wastewater and clinical case curves. The WC ratio increases after key events, providing insight into the balance between disease spread and public health response. Time lag and transfer function analysis showed that wastewater data preceded clinically reported cases in the first wave of the pandemic but did not serve as a leading indicator in the second wave, likely due to increased testing capacity, which allows for more timely case detection and reporting. These three metrics could help further integrate wastewater surveillance into the public health response to the COVID-19 pandemic and future pandemics.

SARS-CoV-2 cycle threshold (Ct) values predict future COVID-19 cases

Aim

Anticipating local surges in COVID-19 cases has predominantly been based on observation of increasing cases. We sought to determine if temporal trends in SARS-CoV-2 Cycle threshold (Ct) values from clinical testing were predictive of future cases.

Methods

Data were collected from a large, safety-net hospital in Los Angeles, California. Ct values for all SARS-CoV-2 detections by the GeneXpert system (Cepheid) between October 2020 to March 2021 were analyzed.

Results

A total of 2,114 SARS-CoV-2-positive samples were included. Cases increased dramatically in December 2020, peaking the first week of January, before returning to pre-surge numbers by mid-February. Ct values fell during this same period, with values in December and January (25.6 ± 7.8 and 27±7.9, respectively) significantly lower than those of the other months (30±9.3 to 37.7 ± 6.3). Average weekly Ct values for all patients negatively correlated with the number of tests run two weeks in the future (r= -0.74, p<0.0001), whereas Ct values for asymptomatic patients negatively correlated most strongly with total number of tests performed one month later (r= -0.88, p<0.0001). Predictive modeling using these Ct values correctly predicted whether cases would increase or decrease 65% of the time for a subsequent surge (May-July 2021).

Conclusions

During the largest COVID-19 surge in Los Angeles to date, we observed significantly lower Ct values (representing higher levels of viral RNA) suggesting that increased transmission of COVID-19 was temporarily associated with higher viral loads. Decreasing Ct values appear to be a leading indicator for predicting future COVID-19 cases, which can facilitate improved hospital-level surge planning.

Unlocking capacities of genomics for the COVID-19 response and future pandemics

During the COVID-19 pandemic, genomics and bioinformatics have emerged as essential public health tools. The genomic data acquired using these methods have supported the global health response, facilitated development of testing methods, and allowed timely tracking of novel SARS-CoV-2 variants. Yet the virtually unlimited potential for rapid generation and analysis of genomic data is also coupled with unique technical, scientific, and organizational challenges. Here, we discuss the application of genomic and computational methods for the efficient data driven COVID-19 response, advantages of democratization of viral sequencing around the world, and challenges associated with viral genome data collection and processing.

Validating and optimizing the method for molecular detection and quantification of SARS-CoV-2 in wastewater

Wastewater surveillance of SARS-CoV-2 has become a promising tool to estimate population-level changes in community infections and the prevalence of COVID-19 disease. Although many studies have reported the detection and quantification of SARS-CoV-2 in wastewater, remarkable variation remains in the methodology. In this study, we validated a molecular testing method by concentrating viruses from wastewater using ultrafiltration and detecting SARS-CoV-2 using one-step RT-qPCR assay. The following parameters were optimized including sample storage condition, wastewater pH, RNA extraction and RT-qPCR assay by quantification of SARS-CoV-2 or spiked human coronavirus strain 229E (hCoV-229E). Wastewater samples stored at 4 °C after collection showed significantly enhanced detection of SARS-CoV-2 with approximately 2-3 PCR-cycle threshold (Ct) values less when compared to samples stored at -20 °C. Pre-adjustment of the wastewater pH to 9.6 to aid virus desorption followed by pH readjustment to neutral after solid removal significantly increased the recovery of spiked hCoV-229E. Of the five commercially available RNA isolation kits evaluated, the MagMAX-96 viral RNA isolation kit showed the best recovery of hCoV-229E (50.1 ± 20.1%). Compared with two-step RT-qPCR, one-step RT-qPCR improved sensitivity for SARS-CoV-2 detection. Salmon DNA was included for monitoring PCR inhibition and pepper mild mottle virus (PMMoV), a fecal indicator indigenous to wastewater, was used to normalize SARS-CoV-2 levels in wastewater. Our method for molecular detection of SARS-CoV-2 in wastewater provides a useful tool for public health surveillance of COVID-19.

Evaluation of multiple analytical methods for SARS-CoV-2 surveillance in wastewater samples

In this study, 14 virus concentration protocols based on centrifugation, filtration, polyethylene glycol (PEG) precipitation and ultrafiltration were tested for their efficacy for the quantification of SARS-CoV-2 in wastewater samples. These protocols were paired with four RNA extraction procedures resulting in a combination of 50 unique approaches. Bovine respiratory syncytial virus (BRSV) was used as a process control and seeded in each wastewater sample subjected to all 50 protocols. The recovery of BRSV obtained through the application of 50 unique approaches ranged from <0.03 to 64.7% (±1.6%). Combination of centrifugation as the solid removal step, ultrafiltration (Amicon-UF-15; 100 kDa cut-off; protocol 9) as the primary virus concentration method, and Zymo Quick-RNA extraction kit provided the highest BRSV recovery (64.7 ± 1.6%). To determine the impact of prolonged storage of large wastewater sample volume (900 mL) at -20 °C on enveloped virus decay, the BRSV seeded wastewaters samples were stored at -20 °C up to 110 days and analyzed using the most efficient concentration (protocol 9) and extraction (Zymo Quick-RNA kit) methods. BRSV RNA followed a first-order decay rate (k = 0.04/h with r² = 0.99) in wastewater. Finally, 21 wastewater influent samples from five wastewater treatment plants (WWTPs) in southern Maryland, USA were analyzed between May to August 2020 to determine SARS-CoV-2 RNA concentrations. SARS-CoV-2 RNA was quantifiable in 17/21 (81%) of the influent wastewater samples with concentration ranging from 1.10 (±0.10) × 10⁴ to 2.38 (±0.16) × 10⁶ gene copies/L. Among the RT-qPCR assays tested, US CDC N1 assay was the most sensitive followed by US CDC N2, E_Sarbeco, and RdRp assays. Data presented in this study may enhance our understanding on the effective concentration and extraction of SARS-CoV-2 from wastewater.

Comprehensive investigation of SARS-CoV-2 fate in wastewater and finding the virus transfer and destruction route through conventional activated sludge and sequencing batch reactor

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA transmission route was thoroughly investigated in the hospital wastewater, sewage collection network, and wastewater treatment plants. Samples were taken on four occasions from December 2020 to April 2021. The performance of two different wastewater treatment processes of sequencing batch reactor (SBR) and conventional activated sludge (CAS) was studied for virus destruction. For this purpose, liquid phase, solid phase and bioaerosol samples were taken from different units of the investigated wastewater treatment plants (WWTPs). The results revealed that all untreated hospital wastewater samples were positive for SARS-CoV-2 RNA. The virus detection frequency increased when the number of hospitalized cases increased. Detection of viral RNA in the wastewater collection system exhibited higher load of virus in the generated wastewater in areas with poor socioeconomic conditions. Virus detection in the emitted bioaerosols in WWTPs showed that bioaerosols released from CAS with surface aeration contains SARS-CoV-2 RNA posing a potential threat to the working staff of the WWTPs. However, no viral RNA was detected in the bioaerosols of the SBR with diffused aeration system. Investigation of SARS-CoV-2 RNA in WWTPs showed high affinity of the virus to be accumulated in biosolids rather than transporting via liquid phase. Following the fate of virus in sludge revealed that it is completely destructed in anaerobic sludge treatment process. Therefore, based on the results of the present study, it can be concluded that receiving water resources could not be contaminated with virus, if the wastewater treatment processes work properly.

Wastewater surveillance to infer COVID-19 transmission: A systematic review

Successful detection of SARS-COV-2 in wastewater suggests the potential utility of wastewater-based epidemiology (WBE) for COVID-19 community surveillance. This systematic review aims to assess the performance of wastewater surveillance as early warning system of COVID-19 community transmission. A systematic search was conducted in PubMed, Medline, Embase and the WBE Consortium Registry according to PRISMA guidelines for relevant articles published until 31st July 2021. Relevant data were extracted and summarized. Quality of each paper was assessed using an assessment tool adapted from Bilotta et al.'s tool for environmental science. Of 763 studies identified, 92 studies distributed across 34 countries were shortlisted for qualitative synthesis. A total of 26,197 samples were collected between January 2020 and May 2021 from various locations serving population ranging from 321 to 11,400,000 inhabitants. Overall sample positivity was moderate at 29.2% in all examined settings with the spike (S) gene having maximum rate of positive detections and nucleocapsid (N) gene being the most targeted. Wastewater signals preceded confirmed cases by up to 63 days, with 13 studies reporting sample positivity before the first cases were detected in the community. At least 50 studies reported an association of viral load with community cases. While wastewater surveillance cannot replace large-scale diagnostic testing, it can complement clinical surveillance by providing early signs of potential transmission for more active public health responses. However, more studies using standardized and validated methods are required along with risk analysis and modelling to understand the dynamics of viral outbreaks.

A nationwide indicator to smooth and normalize heterogeneous SARS-CoV-2 RNA data in wastewater

Since many infected people experience no or few symptoms, the SARS-CoV-2 epidemic is frequently monitored through massive virus testing of the population, an approach that may be biased and may be difficult to sustain in low-income countries. Since SARS-CoV-2 RNA can be detected in stool samples, quantifying SARS-CoV-2 genome by RT-qPCR in wastewater treatment plants (WWTPs) has been carried out as a complementary tool to monitor virus circulation among human populations. However, measuring SARS-CoV-2 viral load in WWTPs can be affected by many experimental and environmental factors. To circumvent these limits, we propose here a novel indicator, the wastewater indicator (WWI), that partly reduces and corrects the noise associated with the SARS-CoV-2 genome quantification in wastewater (average noise reduction of 19%). All data processing results in an average correlation gain of 18% with the incidence rate. The WWI can take into account the censorship linked to the limit of quantification (LOQ), allows the automatic detection of outliers to be integrated into the smoothing algorithm, estimates the average measurement error committed on the samples and proposes a solution for inter-laboratory normalization in the absence of inter-laboratory assays (ILA). This method has been successfully applied in the context of Obépine, a French national network that has been quantifying SARS-CoV-2 genome in a representative sample of French WWTPs since March 5th 2020. By August 26th, 2021, 168 WWTPs were monitored in the French metropolitan and overseas territories of France. We detail the process of elaboration of this indicator, show that it is strongly correlated to the incidence rate and that the optimal time lag between these two signals is only a few days, making our indicator an efficient complement to the incidence rate. This alternative approach may be especially important to evaluate SARS-CoV-2 dynamics in human populations when the testing rate is low.

Optimal selection of monitoring sites in cities for SARS-CoV-2 surveillance in sewage networks

Selecting sampling points to monitor traces of SARS-CoV-2 in sewage at the intra-urban scale is no trivial task given the complexity of the networks and the multiple technical, economic and socio-environmental constraints involved. This paper proposes two algorithms for the automatic selection of sampling locations in sewage networks. The first algorithm, is for the optimal selection of a predefined number of sampling locations ensuring maximum coverage of inhabitants and minimum overlapping amongst selected sites (static approach). The second is for establishing a strategy of iterations of sample&analysis to identify patient zero and hot spots of COVID-19 infected inhabitants in cities (dynamic approach). The algorithms are based on graph-theory and are coupled to a greedy optimization algorithm. The usefulness of the algorithms is illustrated in the case study of Girona (NE Iberian Peninsula, 148,504 inhabitants). The results show that the algorithms are able to automatically propose locations for a given number of stations. In the case of Girona, always covering more than 60% of the manholes and with less than 3% of them overlapping amongst stations. Deploying 5, 6 or 7 stations results in more than 80% coverage in manholes and more than 85% of the inhabitants. For the dynamic sensor placement, we demonstrate that assigning infection probabilities to each manhole as a function of the number of inhabitants connected reduces the number of iterations required to detect the zero patient and the hot spot areas.

COVID-19 wastewater epidemiology: a model to estimate infected populations

BACKGROUND

Wastewater-based epidemiology provides an opportunity for near real-time, cost-effective monitoring of community-level transmission of SARS-CoV-2. Detection of SARS-CoV-2 RNA in wastewater can identify the presence of COVID-19 in the community, but methods for estimating the numbers of infected individuals on the basis of wastewater RNA concentrations are inadequate.

METHODS

This is a wastewater-based epidemiology study using wastewater samples that were collected weekly or twice a week from three sewersheds in South Carolina, USA, between either May 27 or June 16, 2020, and Aug 25, 2020, and tested for SARS-CoV-2 RNA. We developed a susceptible-exposed-infectious-recovered (SEIR) model based on the mass rate of SARS-CoV-2 RNA in the wastewater to predict the number of infected individuals, and have also provided a simplified equation to predict this. Model predictions were compared with the number of confirmed cases identified by the Department of Health and Environmental Control, South Carolina, USA, for the same time period and geographical area.

FINDINGS

We plotted the model predictions for the relationship between mass rate of virus release and numbers of infected individuals, and we validated this prediction on the basis of estimated prevalence from individual testing. A simplified equation to estimate the number of infected individuals fell within the 95% confidence limits of the model. The rate of unreported COVID-19 cases, as estimated by the model, was approximately 11 times that of confirmed cases (ie, ratio of estimated infections for every confirmed case of 10·9, 95% CI 4·2-17·5). This rate aligned well with an independent estimate of 15 infections for every confirmed case in the US state of South Carolina.

INTERPRETATION

The SEIR model provides a robust method to estimate the total number of infected individuals in a sewershed on the basis of the mass rate of RNA copies released per day. This approach overcomes some of the limitations associated with individual testing campaigns and thereby provides an additional tool that can be used to inform policy decisions.

FUNDING

Clemson University, USA.

COVID-19 wastewater epidemiology: a model to estimate infected populations

BACKGROUND

Wastewater-based epidemiology provides an opportunity for near real-time, cost-effective monitoring of community-level transmission of SARS-CoV-2. Detection of SARS-CoV-2 RNA in wastewater can identify the presence of COVID-19 in the community, but methods for estimating the numbers of infected individuals on the basis of wastewater RNA concentrations are inadequate.

METHODS

This is a wastewater-based epidemiology study using wastewater samples that were collected weekly or twice a week from three sewersheds in South Carolina, USA, between either May 27 or June 16, 2020, and Aug 25, 2020, and tested for SARS-CoV-2 RNA. We developed a susceptible-exposed-infectious-recovered (SEIR) model based on the mass rate of SARS-CoV-2 RNA in the wastewater to predict the number of infected individuals, and have also provided a simplified equation to predict this. Model predictions were compared with the number of confirmed cases identified by the Department of Health and Environmental Control, South Carolina, USA, for the same time period and geographical area.

FINDINGS

We plotted the model predictions for the relationship between mass rate of virus release and numbers of infected individuals, and we validated this prediction on the basis of estimated prevalence from individual testing. A simplified equation to estimate the number of infected individuals fell within the 95% confidence limits of the model. The rate of unreported COVID-19 cases, as estimated by the model, was approximately 11 times that of confirmed cases (ie, ratio of estimated infections for every confirmed case of 10·9, 95% CI 4·2-17·5). This rate aligned well with an independent estimate of 15 infections for every confirmed case in the US state of South Carolina.

INTERPRETATION

The SEIR model provides a robust method to estimate the total number of infected individuals in a sewershed on the basis of the mass rate of RNA copies released per day. This approach overcomes some of the limitations associated with individual testing campaigns and thereby provides an additional tool that can be used to inform policy decisions.

FUNDING

Clemson University, USA.

Spatially resolved qualified sewage spot sampling to track SARS-CoV-2 dynamics in Munich - One year of experience

Wastewater-based epidemiology (WBE) is a tool now increasingly proposed to monitor the SARS-CoV-2 burden in populations without the need for individual mass testing. It is especially interesting in metropolitan areas where spread can be very fast, and proper sewage systems are available for sampling with short flow times and thus little decay of the virus. We started in March 2020 to set up a once-a-week qualified spot sampling protocol in six different locations in Munich carefully chosen to contain primarily wastewater of permanent residential areas, rather than industry or hospitals. We used RT-PCR and sequencing to track the spread of SARS-CoV-2 in the Munich population with temporo-spatial resolution. The study became fully operational in mid-April 2020 and has been tracking SARS-CoV-2 RNA load weekly for one year. Sequencing of the isolated viral RNA was performed to obtain information about the presence and abundance of variants of concern in the Munich area over time. We demonstrate that the evolution of SARS-CoV-2 RNA loads (between <7.5 and 3874/ml) in these different areas within Munich correlates well with official seven day incidence notification data (between 0.0 and 327 per 100,000) obtained from the authorities within the respective region. Wastewater viral loads predicted the dynamic of SARS-CoV-2 local incidence about 3 weeks in advance of data based on respiratory swab analyses. Aligning with multiple different point-mutations characteristic for certain variants of concern, we could demonstrate the gradual increase of variant of concern B.1.1.7 in the Munich population beginning in January 2021, weeks before it became apparent in sequencing results of swabs samples taken from patients living in Munich. Overall, the study highlights the potential of WBE to monitor the SARS-CoV-2 pandemic, including the introduction of variants of concern in a local population.

Coding-Complete Genome Sequence of a SARS-CoV-2 Variant Obtained from Raw Sewage at the University of Tennessee-Knoxville Campus

Reported here is a coding-complete genome sequence of a SARS-CoV-2 variant obtained from raw wastewater samples at the University of Tennessee-Knoxville campus. This sequence provides insight into SARS-CoV-2 variants that circulate on large college campuses but remain mostly undetected.

RNA Viromics of Southern California Wastewater and Detection of SARS-CoV-2 Single-Nucleotide Variants

Municipal wastewater provides an integrated sample of a diversity of human-associated microbes across a sewershed, including viruses. Wastewater-based epidemiology (WBE) is a promising strategy to detect pathogens and may serve as an early warning system for disease outbreaks. Notably, WBE has garnered substantial interest during the coronavirus disease 2019 (COVID-19) pandemic to track disease burden through analyses of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA. Throughout the COVID-19 outbreak, tracking SARS-CoV-2 in wastewater has been an important tool for understanding the spread of the virus. Unlike traditional sequencing of SARS-CoV-2 isolated from clinical samples, which adds testing burden to the health care system, in this study, metatranscriptomics was used to sequence virus directly from wastewater. Here, we present a study in which we explored RNA viral diversity through sequencing 94 wastewater influent samples across seven wastewater treatment plants (WTPs), collected from August 2020 to January 2021, representing approximately 16 million people in Southern California. Enriched viral libraries identified a wide diversity of RNA viruses that differed between WTPs and over time, with detected viruses including coronaviruses, influenza A, and noroviruses. Furthermore, single-nucleotide variants (SNVs) of SARS-CoV-2 were identified in wastewater, and we measured proportions of overall virus and SNVs across several months. We detected several SNVs that are markers for clinically important SARS-CoV-2 variants along with SNVs of unknown function, prevalence, or epidemiological consequence. Our study shows the potential of WBE to detect viruses in wastewater and to track the diversity and spread of viral variants in urban and suburban locations, which may aid public health efforts to monitor disease outbreaks. IMPORTANCE Wastewater-based epidemiology (WBE) can detect pathogens across sewersheds, which represents the collective waste of human populations. As there is a wide diversity of RNA viruses in wastewater, monitoring the presence of these viruses is useful for public health, industry, and ecological studies. Specific to public health, WBE has proven valuable during the coronavirus disease 2019 (COVID-19) pandemic to track the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) without adding burden to health care systems. In this study, we used metatranscriptomics and reverse transcription-droplet digital PCR (RT-ddPCR) to assay RNA viruses across Southern California wastewater from August 2020 to January 2021, representing approximately 16 million people from Los Angeles, Orange, and San Diego counties. We found that SARS-CoV-2 quantification in wastewater correlates well with county-wide COVID-19 case data, and that we can detect SARS-CoV-2 single-nucleotide variants through sequencing. Likewise, wastewater treatment plants (WTPs) harbored different viromes, and we detected other human pathogens, such as noroviruses and adenoviruses, furthering our understanding of wastewater viral ecology.

Escalating SARS-CoV-2 circulation in environment and tracking waste management in South Asia

The novel coronavirus disease of 2019 (COVID-19) pandemic has caused an exceptional drift of production, utilization, and disposal of personal protective equipment (PPE) and different microplastic objects for safety against the virus. Hence, we reviewed related literature on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA detected from household, biomedical waste, and sewage to identify possible health risks and status of existing laws, regulations, and policies regarding waste disposal in South Asian (SA) countries. The SARS-CoV-2 RNA was detected in sewage and wastewater samples of Nepal, India, Pakistan, and Bangladesh. Besides, this review reiterates the enormous amounts of PPE and other single-use plastic wastes generated from healthcare facilities and households in the SA region with inappropriate disposal, landfilling, and/or incineration techniques wind-up polluting the environment. Consequently, the Delta variant (B.1.617.2) of SARS-CoV-2 has been detected in sewer treatment plant in India. Moreover, the overuse of non-biodegradable plastics during the pandemic is deteriorating plastic pollution condition and causes a substantial health risk to the terrestrial and aquatic ecosystems. We recommend making necessary adjustments, adopting measures and strategies, and enforcement of the existing biomedical waste management and sanitation-related policy in SA countries. We propose to adopt the knowledge gaps to improve COVID-19-associated waste management and legislation to prevent further environmental pollution. Besides, the citizens should follow proper disposal procedures of COVID-19 waste to control the environmental pollution.

Laboratory-Generated DNA Can Cause Anomalous Pathogen Diagnostic Test Results

The coronavirus disease 2019 (COVID-19) pandemic has brought about the unprecedented expansion of highly sensitive molecular diagnostics as a primary infection control strategy. At the same time, many laboratories have shifted focus to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) research and diagnostic development, leading to large-scale production of SARS-CoV-2 nucleic acids that can interfere with these tests. We have identified multiple instances, in independent laboratories, in which nucleic acids generated in research settings are suspected to have caused researchers to test positive for SARS-CoV-2 in surveillance testing. In some cases, the affected individuals did not work directly with these nucleic acids but were exposed via a contaminated surface or object. Though researchers have long been vigilant of DNA contaminants, the transfer of these contaminants to SARS-CoV-2 testing samples can result in anomalous test results. The impact of these incidents stretches into the public sphere, placing additional burdens on public health resources, placing affected researchers and their contacts in isolation and quarantine, removing them from the testing pool for 3 months, and carrying the potential to trigger shutdowns of classrooms and workplaces. We report our observations as a call for increased stewardship over nucleic acids with the potential to impact both the use and development of diagnostics.

IMPORTANCE To meet the challenges imposed by the COVID-19 pandemic, research laboratories shifted their focus and clinical diagnostic laboratories developed and utilized new assays. Nucleic acid-based testing became widespread and, for the first time, was used as a prophylactic measure. We report 15 cases of researchers at two institutes testing positive for SARS-CoV-2 on routine surveillance tests, in the absence of any symptoms or transmission. These researchers were likely contaminated with nonhazardous nucleic acids generated in the laboratory in the course of developing new SARS-CoV-2 diagnostics. These contaminating nucleic acids were persistent and widespread throughout the laboratory. We report these findings as a cautionary tale to those working with nucleic acids used in diagnostic testing and as a call for careful stewardship of diagnostically relevant molecules. Our conclusions are especially relevant as at-home COVID-19 testing gains traction in the marketplace and these amplicons may impact on the general public.

Age-dependent association between SARS-CoV-2 cases reported by passive surveillance and viral load in wastewater

The actual number of individuals infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is difficult to estimate using a case-reporting system (i.e., passive surveillance) alone because of asymptomatic infection. While wastewater-based epidemiology has been implemented as an alternative/additional monitoring tool to reduce reporting bias, the relationship between passive and wastewater surveillance data has not yet been explicitly examined. As there is strong age dependency in the symptomatic ratio of SARS-CoV-2 infections, here, we aimed to estimate i) an age-dependent association between the number of reported cases and viral load in wastewater and ii) the time lag between these time series. The viral load in wastewater was modeled as a combination of contributions from virus shedding by different age groups, incorporating the delay, and fitted with daily case count data collected from the Massachusetts Department of Public Health and SARS-CoV-2 RNA concentration in wastewater recorded by the Massachusetts Water Resources Authority. The estimated lag between the time series of viral loads in wastewater and of reported cases was 10.8 (95% confidence interval: 10.2-11.6) and 8.8 (8.4-9.1) days for the northern and southern areas of the wastewater treatment plant, respectively. The estimated contribution rate of a reported case to the viral load in wastewater in the 0-19 yr age group was 0.38 (0.35-0.41) and 0.40 (0.37-0.43) for the northern and southern areas, and that in the 80+ yr age group was 0.67 (0.65-0.69) and 0.51 (0.49-0.52) for the northern and southern areas, respectively. The estimated lag between these time series suggested the predictability of reported cases 10 days later using viral loads in wastewater. The contribution of a reported case in passive surveillance to the viral load in wastewater differed by age, suggesting a large variation in viral shedding kinetics among age groups.

The first case study of wastewater-based epidemiology of COVID-19 in Hong Kong

Early detection and surveillance of severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) virus are key pre-requisites for the effective control of coronavirus disease (COVID-19). So far, sewage testing has been increasingly employed as an alternative surveillance tool for this disease. However, sampling site characteristics impact the testing results and should be addressed in the early use stage of this emerging tool. In this study, we implemented the sewage testing for SARS-CoV-2 virus across sampling sites with different sewage system characteristics. We first validated a testing method using "positive" samples from a hospital treating COVID-19 patients. This method was used to test 107 sewage samples collected during the third wave of the COVID-19 outbreak in Hong Kong (from June 8 to September 29, 2020), covering sampling sites associated with a COVID-19 hospital, public housing estates, and conventional sewage treatment facilities. The highest viral titer of 1975 copy/mL in sewage was observed in a sample collected from the isolation ward of the COVID-19 hospital. Sewage sampling at individual buildings detected the virus 2 days before the first cases were identified. Sequencing of the detected viral fragment confirmed an identical nucleotide sequence to that of the SARS-CoV-2 isolated from human samples. The virus was also detected in sewage treatment facilities, which serve populations of approximately 40,000 to more than one million people.

Toxicological insights of Spike fragments SARS-CoV-2 by exposure environment: A threat to aquatic health?

The Spike protein (S protein) is a critical component in the infection of the new coronavirus (SARS-CoV-2). The objective of this work was to evaluate whether peptides from S protein could cause negative impact in the aquatic animals. The aquatic toxicity of SARS-CoV-2 Spike protein peptides derivatives has been evaluated in tadpoles (n = 50 tadpoles/5 replicates of 10 animals) from species Physalaemus cuvieri (Leptodactylidae). After synthesis, purification, and characterization of peptides (PSDP2001, PSDP2002, PSDP2003) an aquatic contamination has been simulated with these peptides during 24 h of exposure in two concentrations (100 and 500 ng/mL). The control group ("C") was composed of tadpoles kept in polyethylene containers containing de-chlorinated water. Oxidative stress, antioxidant biomarkers and AChE activity were assessed. In both concentrations, PSPD2002 and PSPD2003 increased catalase and superoxide dismutase antioxidants enzymes activities, as well as oxidative stress (nitrite levels, hydrogen peroxide and reactive oxygen species). All three peptides also increased acetylcholinesterase activity in the highest concentration. These peptides showed molecular interactions in silico with acetylcholinesterase and antioxidant enzymes. Aquatic particle contamination of SARS-CoV-2 has cholinesterasic effect in P. cuvieri tadpoles. These findings indicate that the COVID-19 can constitute environmental impact or biological damage potential.

A year into the COVID-19 pandemic: Rethinking of wastewater monitoring as a preemptive approach

Under the current pandemic situation caused by the novel coronavirus SARS-CoV-2, wastewater monitoring has been increasingly investigated as a surveillance tool for community-wide disease prevalence. After a year into the pandemic, this review critically discusses the real progress made in the detection of SARS-CoV-2 using wastewater monitoring. The limitations and the key challenges faced in improving the detection methods are highlighted. As per the literature, the complex nature of the wastewater matrix poses problems in processing the samples and achieving high sensitivity at low loads of viral RNA using the current detection methods. Furthermore, in the absence of a gold standard analytical method for wastewater, the validation of the generated data for use in wastewater-based epidemiological modeling of the disease becomes practically difficult. However, research is advancing in adopting clinical methods to the wastewater by using appropriate processing controls, and recovery methods. Besides, the technological advances made by the industry including the development of PCR kits with improved detection limits, easy-to-use viral RNA concentration methods, ability to detect the coronavirus variants, and artificial intelligence and advanced data modeling for continuous and remote monitoring greatly help to debottleneck some of these problems. Currently, these technologies are limited to healthcare systems, however, their use for wastewater monitoring is expected to provide opportunities for wide-scale applications of wastewater-based epidemiology (WBE). Moreover, the data from wastewater monitoring act as the initial checkpoint for human health even before the appearance of symptoms, hence WBE needs more attention to manage current and future infectious transmissions.

Detection and Stability of SARS-CoV-2 Fragments in Wastewater: Impact of Storage Temperature

SARS-CoV-2 wastewater epidemiology suffers from uncertainties concerning sample storage. We show the effect of the storage of wastewater on the detectable SARS-CoV-2 load. Storage at 4 °C for up to 9 days had no significant effect, while storage at -20 °C led to a significant reduction in gene copy numbers.

Estimating the minimum number of SARS-CoV-2 infected cases needed to detect viral RNA in wastewater: To what extent of the outbreak can surveillance of wastewater tell us?

There is increasing interest in wastewater-based epidemiology (WBE) of SARS-CoV-2 RNA to serve as an early warning system for a community. Despite successful detection of SARS-CoV-2 RNA in wastewaters sampled from multiple locations, there is still no clear idea on the minimal number of cases in a community that are associated with a positive detection of the virus in wastewater. To address this knowledge gap, we sampled wastewaters from a septic tank (n = 57) and biological activated sludge tank (n = 52) located on-site of a hospital. The hospital is providing treatment for SARS-CoV-2 infected patients, with the number of hospitalized patients per day known. It was observed that depending on which nucleocapsid gene is targeted by means of RT-qPCR, a range of 253-409 positive cases out of 10,000 persons are required prior to detecting RNA SARS-CoV-2 in wastewater. There was a weak correlation between N1 and N2 gene abundances in wastewater with the number of hospitalized cases. This correlation was however not observed for N3 gene. The frequency of detecting N1 and N2 gene in wastewater was also higher than that for N3 gene. Furthermore, nucleocapsid genes of SARS-CoV-2 were detected at lower frequency in the partially treated wastewater than in the septic tank. In particular, N1 gene abundance was associated with water quality parameters such as total organic carbon and pH. In instances of positive detection, the average abundance of N1 and N3 genes in the activated sludge tank were reduced by 50 and 70% of the levels detected in septic tank, suggesting degradation of the SARS-CoV-2 gene fragments already occurring in the early stages of the wastewater treatment process.

Estimating the minimum number of SARS-CoV-2 infected cases needed to detect viral RNA in wastewater: To what extent of the outbreak can surveillance of wastewater tell us?

There is increasing interest in wastewater-based epidemiology (WBE) of SARS-CoV-2 RNA to serve as an early warning system for a community. Despite successful detection of SARS-CoV-2 RNA in wastewaters sampled from multiple locations, there is still no clear idea on the minimal number of cases in a community that are associated with a positive detection of the virus in wastewater. To address this knowledge gap, we sampled wastewaters from a septic tank (n = 57) and biological activated sludge tank (n = 52) located on-site of a hospital. The hospital is providing treatment for SARS-CoV-2 infected patients, with the number of hospitalized patients per day known. It was observed that depending on which nucleocapsid gene is targeted by means of RT-qPCR, a range of 253-409 positive cases out of 10,000 persons are required prior to detecting RNA SARS-CoV-2 in wastewater. There was a weak correlation between N1 and N2 gene abundances in wastewater with the number of hospitalized cases. This correlation was however not observed for N3 gene. The frequency of detecting N1 and N2 gene in wastewater was also higher than that for N3 gene. Furthermore, nucleocapsid genes of SARS-CoV-2 were detected at lower frequency in the partially treated wastewater than in the septic tank. In particular, N1 gene abundance was associated with water quality parameters such as total organic carbon and pH. In instances of positive detection, the average abundance of N1 and N3 genes in the activated sludge tank were reduced by 50 and 70% of the levels detected in septic tank, suggesting degradation of the SARS-CoV-2 gene fragments already occurring in the early stages of the wastewater treatment process.

Pit latrines may be a potential risk in rural China and low-income countries when dealing with COVID-19

According to the latest reports, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which caused coronavirus disease 2019 (COVID-19), was successfully isolated from the excreta (stool and urine) of COVID-19 patients, suggesting SARS-CoV-2 could be transmitted through excreta contaminated water. As pit latrines and the use of untreated excreta as fertilizer were common in rural China, we surveyed 27 villages of Jiangxi and Hubei provinces and found that pit latrines could be a potential source of SARS-CoV-2 water pollution. Recently, bats have been widely recognized as the source of SARS-CoV-2. There were many possible intermediate hosts of SARS-CoV-2, including pangolin, snake, bird and fish, but which one was still not clear exactly. Here, we proposed a hypothesis to illustrate the mechanism that SARS-CoV-2 might spread from the excreta of infected humans in pit latrines to potential animal hosts, thus becoming a sustainable source of infection in rural China. Therefore, we believe that abolishing pit latrines and banning the use of untreated excreta as fertilizer can improve the local living environment and effectively prevent COVID-19 and other potential waterborne diseases that could emanate from the excreta of infected persons. Although this study focused on rural areas in China, the results could also be applied to low-income countries, especially in Africa.

Wastewater surveillance for SARS-CoV-2 on college campuses: Initial efforts, lessons learned and research needs

Background

Wastewater surveillance for SARS-CoV-2 is an emerging approach to help identify the risk of a COVID-19 outbreak. This tool can contribute to public health surveillance at both community (wastewater treatment system) and institutional (e.g., colleges, prisons, nursing homes) scales.

Objectives

This research aims to understand the successes, challenges, and lessons learned from initial wastewater surveillance efforts at colleges and university systems to inform future research, development and implementation.

Methods

This paper presents the experiences of 25 college and university systems in the United States that monitored campus wastewater for SARS-CoV-2 during the fall 2020 academic period. We describe the broad range of approaches, findings, resource needs, and lessons learned from these initial efforts. These institutions range in size, social and political geographies, and include both public and private institutions.

Discussion

Our analysis suggests that wastewater monitoring at colleges requires consideration of information needs, local sewage infrastructure, resources for sampling and analysis, college and community dynamics, approaches to interpretation and communication of results, and follow-up actions. Most colleges reported that a learning process of experimentation, evaluation, and adaptation was key to progress. This process requires ongoing collaboration among diverse stakeholders including decision-makers, researchers, faculty, facilities staff, students, and community members.

Predicting daily COVID-19 case rates from SARS-CoV-2 RNA concentrations across a diversity of wastewater catchments

We assessed the relationship between municipality COVID-19 case rates and SARS-CoV-2 concentrations in the primary sludge of corresponding wastewater treatment facilities. Over 1700 daily primary sludge samples were collected from six wastewater treatment facilities with catchments serving 18 cities and towns in the State of Connecticut, USA. Samples were analyzed for SARS-CoV-2 RNA concentrations during a 10 month time period that overlapped with October 2020 and winter/spring 2021 COVID-19 outbreaks in each municipality. We fit lagged regression models to estimate reported case rates in the six municipalities from SARS-CoV-2 RNA concentrations collected daily from corresponding wastewater treatment facilities. Results demonstrate the ability of SARS-CoV-2 RNA concentrations in primary sludge to estimate COVID-19 reported case rates across treatment facilities and wastewater catchments, with coverage probabilities ranging from 0.94 to 0.96. Lags of 0 to 1 days resulted in the greatest predictive power for the model. Leave-one-out cross validation suggests that the model can be broadly applied to wastewater catchments that range in more than one order of magnitude in population served. The close relationship between case rates and SARS-CoV-2 concentrations demonstrates the utility of using primary sludge samples for monitoring COVID-19 outbreak dynamics. Estimating case rates from wastewater data can be useful in locations with limited testing availability, testing disparities, or delays in individual COVID-19 testing programs.

Wastewater network infrastructure in public health: Applications and learnings from the COVID-19 pandemic

Accurate estimates of COVID-19 burden of infections in communities can inform public health strategy for the current pandemic. Wastewater based epidemiology (WBE) leverages sewer infrastructure to provide insights on rates of infection by measuring viral concentrations in wastewater. By accessing the sewer network at various junctures, important insights regarding COVID-19 disease activity can be gained. The analysis of sewage at the wastewater treatment plant level enables population-level surveillance of disease trends and virus mutations. At the neighborhood level, WBE can be used to describe trends in infection rates in the community thereby facilitating local efforts at targeted disease mitigation. Finally, at the building level, WBE can suggest the presence of infections and prompt individual testing. In this critical review, we describe the types of data that can be obtained through varying levels of WBE analysis, concrete plans for implementation, and public health actions that can be taken based on WBE surveillance data of infectious diseases, using recent and successful applications of WBE during the COVID-19 pandemic for illustration.