RNA-Seq of Untreated Wastewater to Assess COVID-19 and Emerging and Endemic Viruses For Public Health Surveillance

Background The COVID-19 pandemic showcased the power of genomic sequencing to tackle the emergence and spread of infectious diseases. However, metagenomic sequencing of total microbial RNAs in wastewater has the potential to assess multiple infectious diseases simultaneously and has yet to be explored. Methods A retrospective RNA-Seq epidemiological survey of 140 untreated composite wastewater samples was performed across urban (n=112) and rural (n=28) areas of Nagpur, Central India. Composite wastewater samples were prepared by pooling 422 individual grab samples collected prospectively from sewer lines of urban municipality zones and open drains of rural areas from 3rd February to 3rd April 2021, during the second COVID-19 wave in India. Samples were pre-processed and total RNA was extracted prior to genomic sequencing. Findings This is the first study that has utilised culture and/or probe-independent unbiased RNA-Seq to examine Indian wastewater samples. Our findings reveal the detection of zoonotic viruses including chikungunya, Jingmen tick and rabies viruses, which have not previously been reported in wastewater. SARS-CoV-2 was detectable in 83 locations (59%), with stark abundance variations observed between sampling sites. Hepatitis C virus was the most frequently detected infectious virus, identified in 113 locations and co-occurring 77 times with SARS-CoV-2;and both were more abundantly detected in rural areas than urban zones. Concurrent identification of segmented virus genomic fragments of influenza A virus, norovirus, and rotavirus was observed. Geographical differences were also observed for astrovirus, saffold virus, husavirus, and aichi virus that were more prevalent in urban samples, while the zoonotic viruses chikungunya and rabies, were more abundant in rural environments. Interpretation RNA-Seq can effectively detect multiple infectious diseases simultaneously, facilitating geographical and epidemiological surveys of endemic viruses that could help direct healthcare interventions against emergent and pre-existent infectious diseases as well as cost-effectively and qualitatively characterising the health status of the population over time Funding UK Research and Innovation (UKRI) Global Challenges Research Fund (GCRF) grant number H54810, as supported by Research England.

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.)

Moving forward with COVID-19: Future research prospects of wastewater-based epidemiology methodologies and applications.

Wastewater-based epidemiology (WBE) has been demonstrated for its great potential in tracking of coronavirus disease 2019 (COVID-19) transmission among populations despite some inherent methodological limitations. These include non-optimized sampling approaches and analytical methods; stability of viruses in sewer systems; partitioning/retention in biofilms; and the singular and inaccurate back-calculation step to predict the number of infected individuals in the community. Future research is expected to (1) standardize best practices in wastewater sampling, analysis and data reporting protocols for the sensitive and reproducible detection of viruses in wastewater; (2) understand the in-sewer viral stability and partitioning under the impacts of dynamic wastewater flow, properties, chemicals, biofilms and sediments; and (3) achieve smart wastewater surveillance with artificial intelligence and big data models. Further specific research is essential in the monitoring of other viral pathogens with pandemic potential and subcatchment applications to maximize the benefits of WBE beyond COVID-19.

Correlation between SARS-CoV-2 RNA concentration in wastewater and COVID-19 cases in community: A systematic review and meta-analysis.

Wastewater-based epidemiology (WBE) has been considered as a promising approach for population-wide surveillance of coronavirus disease 2019 (COVID-19). Many studies have successfully quantified severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA concentration in wastewater (CRNA). However, the correlation between the CRNA and the COVID-19 clinically confirmed cases in the corresponding wastewater catchments varies and the impacts of environmental and other factors remain unclear. A systematic review and meta-analysis were conducted to identify the correlation between CRNA and various types of clinically confirmed case numbers, including prevalence and incidence rates. The impacts of environmental factors, WBE sampling design, and epidemiological conditions on the correlation were assessed for the same datasets. The systematic review identified 133 correlation coefficients, ranging from -0.38 to 0.99. The correlation between CRNA and new cases (either daily new, weekly new, or future cases) was stronger than that of active cases and cumulative cases. These correlation coefficients were potentially affected by environmental and epidemiological conditions and WBE sampling design. Larger variations of air temperature and clinical testing coverage, and the increase of catchment size showed strong negative impacts on the correlation between CRNA and COVID-19 case numbers. Interestingly, the sampling technique had negligible impact although increasing the sampling frequency improved the correlation. These findings highlight the importance of viral shedding dynamics, in-sewer decay, WBE sampling design and clinical testing on the accurate back-estimation of COVID-19 case numbers through the WBE approach.

SARS-CoV-2 shedding sources in wastewater and implications for wastewater-based epidemiology.

Wastewater-based epidemiology (WBE) approach for COVID-19 surveillance is largely based on the assumption of SARS-CoV-2 RNA shedding into sewers by infected individuals. Recent studies found that SARS-CoV-2 RNA concentration in wastewater (CRNA) could not be accounted by the fecal shedding alone. This study aimed to determine potential major shedding sources based on literature data of CRNA, along with the COVID-19 prevalence in the catchment area through a systematic literature review. Theoretical CRNA under a certain prevalence was estimated using Monte Carlo simulations, with eight scenarios accommodating feces alone, and both feces and sputum as shedding sources. With feces alone, none of the WBE data was in the confidence interval of theoretical CRNA estimated with the mean feces shedding magnitude and probability, and 63% of CRNA in WBE reports were higher than the maximum theoretical concentration. With both sputum and feces, 91% of the WBE data were below the simulated maximum CRNA in wastewater. The inclusion of sputum as a major shedding source led to more comparable theoretical CRNA to the literature WBE data. Sputum discharging behavior of patients also resulted in great fluctuations of CRNA under a certain prevalence. Thus, sputum is a potential critical shedding source for COVID-19 WBE surveillance.

Artificial neural network-based estimation of COVID-19 case numbers and effective reproduction rate using wastewater-based epidemiology.

As a cost-effective and objective population-wide surveillance tool, wastewater-based epidemiology (WBE) has been widely implemented worldwide to monitor the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA concentration in wastewater. However, viral concentrations or loads in wastewater often correlate poorly with clinical case numbers. To date, there is no reliable method to back-estimate the coronavirus disease 2019 (COVID-19) case numbers from SARS-CoV-2 concentrations in wastewater. This greatly limits WBE in achieving its full potential in monitoring the unfolding pandemic. The exponentially growing SARS-CoV-2 WBE dataset, on the other hand, offers an opportunity to develop data-driven models for the estimation of COVID-19 case numbers (both incidence and prevalence) and transmission dynamics (effective reproduction rate). This study developed artificial neural network (ANN) models by innovatively expanding a conventional WBE dataset to include catchment, weather, clinical testing coverage and vaccination rate. The ANN models were trained and evaluated with a comprehensive state-wide wastewater monitoring dataset from Utah, USA during May 2020 to December 2021. In diverse sewer catchments, ANN models were found to accurately estimate the COVID-19 prevalence and incidence rates, with excellent precision for prevalence rates. Also, an ANN model was developed to estimate the effective reproduction number from both wastewater data and other pertinent factors affecting viral transmission and pandemic dynamics. The established ANN model was successfully validated for its transferability to other states or countries using the WBE dataset from Wisconsin, USA.

Successful application of wastewater-based epidemiology in prediction and monitoring of the second wave of COVID-19 with fragmented sewerage systems-a case study of Jaipur (India).

The present study tracked the city-wide dynamics of severe acute respiratory syndrome-corona virus 2 ribonucleic acids (SARS-CoV-2 RNA) in the wastewater from nine different wastewater treatment plants (WWTPs) in Jaipur during the second wave of COVID-19 out-break in India. A total of 164 samples were collected weekly between February 19th and June 8th, 2021. SARS-CoV-2 was detected in 47.2% (52/110) influent samples and 37% (20/54) effluent samples. The increasing percentage of positive influent samples correlated with the city's increasing active clinical cases during the second wave of COVID-19 in Jaipur. Furthermore, wastewater-based epidemiology (WBE) evidence clearly showed early detection of about 20 days (9/9 samples reported positive on April 20th, 2021) before the maximum cases and maximum deaths reported in the city on May 8th, 2021. The present study further observed the presence of SARS-CoV-2 RNA in treated effluents at the time window of maximum active cases in the city even after tertiary disinfection treatments of ultraviolet (UV) and chlorine (Cl2) disinfection. The average genome concentration in the effluents and removal efficacy of six commonly used treatments, activated sludge process + chlorine disinfection (ASP + Cl2), moving bed biofilm reactor (MBBR) with ultraviolet radiations disinfection (MBBR + UV), MBBR + chlorine (Cl2), sequencing batch reactor (SBR), and SBR + Cl2, were compared with removal efficacy of SBR + Cl2 (81.2%) > MBBR + UV (68.8%) > SBR (57.1%) > ASP (50%) > MBBR + Cl2 (36.4%). The study observed the trends and prevalence of four genes (E, RdRp, N, and ORF1ab gene) based on two different kits and found that prevalence of N > ORF1ab > RdRp > E gene suggested that the effective genome concentration should be calculated based on the presence/absence of multiple genes. Hence, it is imperative to say that using a combination of different detection genes (E, N, RdRp, & ORF1ab genes) increases the sensitivity in WBE.

Monitoring of SARS-CoV-2 Variants by Wastewater-Based Surveillance as a Sustainable and Pragmatic Approach—A Case Study of Jaipur (India)

Wastewater-based surveillance has been emerging as an efficient and advantageous tool to predict COVID-19 prevalence in the population, much earlier (7–28 days) than reported clinical cases, thus providing sufficient time to organize resources and optimize their use in managing COVID-19. Since the commencement of the COVID-19 pandemic, SARS-CoV-2 genetic lineages have emerged and are circulating all over the world. The assessment of SARS-CoV-2 variants of concern (VOCs) in wastewater has recently been proven to be successful. The present research demonstrates a case study utilizing an established approach to perform monitoring of SARS-CoV-2 variants from 11 distinct wastewater treatment plants across Jaipur (India) during the second peak period of COVID-19 (from 19 February 2021 to 8 June 2021). The sequences obtained were analyzed to detect lineage using the Pangolin tool and SNPs using the mpileup utility of Samtools, which reported high genome coverage. The mutation analyses successfully identified the penetration of the B.1. in the first two weeks of sampling (19–26 February), followed by the B.1.617.2 variant into Jaipur in the first week of March 2021. B.1.617.2 was initially discovered in India in October 2020;however, it was not reported until early April 2021.The present study identified the presence of B.1.617.2 in early March, which correlates well with the clinical patient’s data (290 cases were reported much later by the government on 10 May 2021). The average total genome coverage of the samples is 94.39% when mapped onto the severe acute respiratory syndrome coronavirus 2 isolate Wuhan-Hu-1;a complete genome (NC_045512.2) sequence and SNP analysis showed that 37–51 SNPs were identified in each sample. The current study demonstrates that sewage surveillance for variant characterization is a reliable and practical method for tracking the diversity of SARS-CoV-2 strains in the community that is considerably faster than clinical genomic surveillance. As a result, this method can predict the advent of epidemiologically or clinically important mutations/variants, which can help with public health decision making.

Imprints of Lockdown and Treatment Processes on the Wastewater Surveillance of SARS-CoV-2: A Curious Case of Fourteen Plants in Northern India

The present study investigated the detection of severe acute respiratory syndrome–coronavirus 2 (SARS-CoV-2) genomes at each treatment stage of 14 aerobic wastewater treatment plants (WWTPs) serving the major municipalities in two states of Rajasthan and Uttarakhand in Northern India. The untreated, primary, secondary and tertiary treated wastewater samples were collected over a time frame ranging from under-lockdown to post-lockdown conditions. The results showed that SARS-CoV-2 RNA was detected in 13 out of 40 wastewater samples in Jaipur district, Rajasthan and in 5 out of 14 wastewater samples in the Haridwar District, Uttarakhand with the E gene predominantly observed as compared to the N and RdRp target genes in later time-points of sampling. The Ct values of genes present in wastewater samples were correlated with the incidence of patient and community cases of COVID-19. This study further indicates that the viral RNA could be detected after the primary treatment but was not present in secondary or tertiary treated samples. This study implies that aerobic biological wastewater treatment systems such as moving bed biofilm reactor (MBBR) technology and sequencing batch reactor (SBR) are effective in virus removal from the wastewater. This work might present a new indication that there is little to no risk in relation to SARS-CoV-2 while reusing the treated wastewater for non-potable applications. In contrast, untreated wastewater might present a potential route of viral transmission through WWTPs to sanitation workers and the public. However, there is a need to investigate the survival and infection rates of SARS-CoV-2 in wastewater.

Sewage surveillance for the presence of SARS-CoV-2 genome as a useful wastewater based epidemiology (WBE) tracking tool in India.

The infection with SARS-CoV-2 is reported to be accompanied by the shedding of the virus in fecal samples of infected patients. Earlier reports have suggested that COVID-19 agents can be present in the sewage samples and thus it can be a good indication of the pandemic extent in a community. However, no such studies have been reported in the Indian context. Hence, it becomes absolutely necessary to detect the presence of the SARS-CoV-2 in the wastewater samples from wastewater treatment plants (WWTPs) serving different localities of Jaipur city. Samples from different WWTPs and hospital wastewater samples were collected and wastewater based epidemiology (WBE) studies were carried out using the RT-PCR to confirm the presence of different COVID-19 target genes namely S gene, E gene, ORF1ab gene, RdRp gene and N gene. The results revealed that the untreated wastewater samples showed the presence of SARS-CoV-2 viral genome, which was correlated with the increased number of COVID-19 positive patients from the concerned areas, as reported in the publically available health data. This is the first study that investigated the presence of SARS-CoV-2 viral genome in wastewater, at higher ambient temperature (45 °C), further validating WBE as potential tool in predicting and mitigating outbreaks.