Making waves: Wastewater-based epidemiology for COVID-19 - approaches and challenges for surveillance and prediction

Beyond COVID-19: Wastewater-based epidemiology for multipathogen surveillance and normalization strategies

Wastewater-based epidemiology (WBE) is a critical tool for monitoring community health. Although much attention has focused on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a causative agent of coronavirus disease 2019 (COVID-19), other pathogens also pose significant health risks. This study quantified the presence of SARS-CoV-2, influenza A virus (Inf-A), and noroviruses of genogroups I (NoV-GI) and II (NoV-GII) in wastewater samples collected weekly (n = 170) from July 2023 to February 2024 from five wastewater treatment plants (WWTPs) in Yamanashi Prefecture, Japan, by quantitative PCR. Inf-A RNA exhibited localized prevalence with positive ratios of 59 %-82 % in different WWTPs, suggesting regional outbreaks within specific areas. NoV-GI (94 %, 160/170) and NoV-GII (100 %, 170/170) RNA were highly prevalent, with NoV-GII (6.1 ± 0.8 log10 copies/L) consistently exceeding NoV-GI (5.4 ± 0.7 log10 copies/L) RNA concentrations. SARS-CoV-2 RNA was detected in 100 % of the samples, with mean concentrations of 5.3 ± 0.5 log10 copies/L in WWTP E and 5.8 ± 0.4 log10 copies/L each in other WWTPs. Seasonal variability was evident, with higher concentrations of all pathogenic viruses during winter. Non-normalized and normalized virus concentrations by fecal indicator bacteria (Escherichia coli and total coliforms), an indicator virus (pepper mild mottle virus (PMMoV)), and turbidity revealed significant positive associations with the reported disease cases. Inf-A and NoV-GI + GII RNA concentrations showed strong correlations with influenza and acute gastroenteritis cases, particularly when normalized to E. coli (Spearman's ρ = 0.70-0.81) and total coliforms (ρ = 0.70-0.81), respectively. For SARS-CoV-2, non-normalized concentrations showed a correlation of 0.61, decreasing to 0.31 when normalized to PMMoV, suggesting that PMMoV is unsuitable. Turbidity normalization also yielded suboptimal results. This study underscored the importance of selecting suitable normalization parameters tailored to specific pathogens for accurate disease trend monitoring using WBE, demonstrating its utility beyond COVID-19 surveillance.

Assessing infectivity of emerging enveloped viruses in wastewater and sewage sludge: Relevance and procedures

The emergence of SARS-CoV-2 has heightened the need to evaluate the detection of enveloped viruses in the environment, particularly in wastewater, within the context of wastewater-based epidemiology. The studies published over the past 80 years focused primarily on non-enveloped viruses due to their ability to survive longer in environmental matrices such as wastewater or sludge compared to enveloped viruses. However, different enveloped viruses survive in the environment for different lengths of time. Therefore, it is crucial to be prepared to assess the potential infectious risk that may arise from future emerging enveloped viruses. This will require appropriate tools, notably suitable viral concentration methods that do not compromise virus infectivity. This review has a dual purpose: first, to gather all the available literature on the survival of infectious enveloped viruses, specifically at different pH and temperature conditions, and in contact with detergents; second, to select suitable concentration methods for evaluating the infectivity of these viruses in wastewater and sludge. The methodology used in this data collection review followed the systematic approach outlined in the PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analysis) guidelines. Concentration methods cited in the data gathered are more tailored towards detecting the enveloped viruses' genome. There is a lack of suitable methods for detecting infectious enveloped viruses in wastewater and sludge. Ultrafiltration, ultracentrifugation, and polyethylene glycol precipitation methods, under specific/defined conditions, appear to be relevant approaches. Further studies are necessary to validate reliable concentration methods for detecting infectious enveloped viruses. The choice of culture system is also crucial for detection sensitivity. The data also show that the survival of infectious enveloped viruses, though lower than that of non-enveloped ones, may enable environmental transmission. Experimental data on a wide range of enveloped viruses is required due to the variability in virus persistence in the environment.

Investigation of high-risk antibiotic resistance bacteria and their associated antibiotic resistance genes in different agricultural soils with biogas slurry from China

High-risk antibiotic-resistant bacteria (ARB) and their accompanying antibiotic resistance genes (ARGs) seriously threaten public health. As a crucial medium for ARB and ARGs spread, soils with biogas slurry have been widely investigated. However, few studies focused on high-risk multi-drug resistant bacteria (MDRB) and their associated ARGs. This study examined ARB distribution in different agricultural soils with biogas slurry across 12 districts in China. It identified high-risk MDRB in various soil backgrounds, elucidating their resistance and spread mechanism. The findings revealed that diverse cultured ARB were enriched in soils with biogas slurry, especially soil ciprofloxacin ARB, which were enriched (>2.5 times) in 68.4 % of sampling sites. Four high-risk MDRB isolated from Hebei, Zhejiang, Shanxi, and Gansu districts were identified as severe or opportunistic pathogens, which carried abundant mobile genetic elements (MGEs) and 14 known high risk ARGs, including aac(3)-IId, aac(6')-Ib3, aph(6)-Id, aac(6')-Ib3, aadA1, blaOXA-10, blaTEM-1B, dfrA12, dfrA14, cmlA1, sul1, floR, tet(M) and tet(L). The antibiotics accumulation, diverse ARGs and MGEs enrichment, and proliferation of pathogenic bacteria could be potential driving factors of their occurrence and spread. Therefore, the coexistence of the high-risk MDRB and ARGs combined with the associated MGEs in soils with biogas slurry should be further investigated to develop technology and policy for reducing their negative influences on the effectiveness of clinical antibiotics.

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

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

Monitoring SARS-CoV-2 RNA in wastewater from a shared septic system and sub-sewershed sites to expand COVID-19 disease surveillance

Wastewater-based epidemiology has expanded as a tool for collecting COVID-19 surveillance data, but there is limited information on the feasibility of this form of surveillance within decentralized wastewater systems (e.g., septic systems). This study assessed SARS-CoV-2 RNA concentrations in wastewater samples from a septic system servicing a mobile home park (66 households) and from two pumping stations serving a similarly sized (71 households) and a larger (1,000 households) neighborhood within a nearby sewershed over 35 weeks in 2020. Also, raw wastewater from a hospital in the same sewershed was sampled. The mobile home park samples had the highest detection frequency (39/39 days) and mean concentration of SARS-CoV-2 RNA (2.7 × 107 gene copies/person/day for the N1) among the four sampling sites. N1 gene and N2 gene copies were highly correlated across mobile home park samples (Pearson's r = 0.93, p < 0.0001). In the larger neighborhood, new COVID-19 cases were reported every week during the sampling period; however, we detected SARS-CoV-2 RNA in 12% of the corresponding wastewater samples. The results of this study suggest that sampling from decentralized wastewater infrastructure can be used for continuous monitoring of SARS-CoV-2 infections.

Crykey: Rapid identification of SARS-CoV-2 cryptic mutations in wastewater

Wastewater surveillance for SARS-CoV-2 provides early warnings of emerging variants of concerns and can be used to screen for novel cryptic linked-read mutations, which are co-occurring single nucleotide mutations that are rare, or entirely missing, in existing SARS-CoV-2 databases. While previous approaches have focused on specific regions of the SARS-CoV-2 genome, there is a need for computational tools capable of efficiently tracking cryptic mutations across the entire genome and investigating their potential origin. We present Crykey, a tool for rapidly identifying rare linked-read mutations across the genome of SARS-CoV-2. We evaluated the utility of Crykey on over 3,000 wastewater and over 22,000 clinical samples; our findings are three-fold: i) we identify hundreds of cryptic mutations that cover the entire SARS-CoV-2 genome, ii) we track the presence of these cryptic mutations across multiple wastewater treatment plants and over three years of sampling in Houston, and iii) we find a handful of cryptic mutations in wastewater mirror cryptic mutations in clinical samples and investigate their potential to represent real cryptic lineages. In summary, Crykey enables large-scale detection of cryptic mutations in wastewater that represent potential circulating cryptic lineages, serving as a new computational tool for wastewater surveillance of SARS-CoV-2.

Wastewater sequencing as a powerful tool to reveal SARS-CoV-2 variant introduction and spread in French Guiana, South America

The origin of introduction of a new pathogen in a country, the evolutionary dynamics of an epidemic within a country, and the role of cross-border areas on pathogen dynamics remain complex to disentangle and are often poorly understood. For instance, cross-border areas represent the ideal location for the sharing of viral variants between countries, with international air travel, land travel and waterways playing an important role in the cross-border spread of infectious diseases. Unfortunately, monitoring the point of entry and the evolutionary dynamics of viruses in space and time within local populations remain challenging. Here we tested the efficiency of wastewater-based epidemiology and genotyping in monitoring Covid-19 epidemiology and SARS-CoV-2 variant dynamics in French Guiana, a tropical country located in South America. Our results suggest that wastewater-based epidemiology and genotyping are powerful tools to monitor variant introduction and disease evolution within a tropical country but the inclusion of both clinical and wastewater samples could still improve our understanding of genetic diversity co-circulating. Wastewater sequencing also revealed the cryptic transmission of SARS-CoV-2 variants within the country. Interestingly, we found some amino acid changes specific to the variants co-circulating in French Guiana, suggesting a local evolution of the SARS-CoV-2 variants after their introduction. More importantly, our results showed that the proximity to bordering countries was not the origin of the emergence of the French Guianese B.1.160.25 variant, but rather that this variant emerged from an ancestor B.1.160 variant introduced by European air plane travelers, suggesting thus that air travel remains a significant risk for cross-border spread of infectious diseases. Overall, we suggest that wastewater-based epidemiology and genotyping provides a cost effective and non-invasive approach for pathogen monitoring and an early-warning tool for disease emergence and spread within a tropical country.

SARS-CoV-2 wastewater surveillance in the Czech Republic: Spatial and temporal differences in SARS-CoV-2 RNA concentrations and relationship to clinical data and wastewater parameters

This study presents the results of systematic wastewater monitoring of SARS-CoV-2 RNA and basic wastewater parameters from four different wastewater treatment plants (WWTPs) in the Czech Republic over the 2020-2022 epidemic. Two-step reverse-transcription quantitative PCR targeting genes encoding the N and Nsp12 proteins was employed to detect SARS-CoV-2 RNA loading in 420 wastewater samples. The results obtained were used to evaluate the potential of wastewater analysis for describing the epidemiological situation in cities of different sizes and determining temporal differences based on the prevailing SARS-CoV-2 variant. Strong correlations between the number of active and hospitalised COVID-19 cases in each WWTP catchment area and the concentration of SARS-CoV-2 RNA detected in the wastewater clearly demonstrated the suitability of this wastewater-based epidemiological approach for WWTPs of different sizes and characteristics, despite differences in SARS-CoV-2 variant waves, with some WWTPs showing high predictive potential. This study demonstrated on the data from the Czech Republic that targeted systematic monitoring of wastewater provides sufficiently robust data for surveillance of viral loads in sample populations, and thus contributes to preventing the spread of infection and subsequent introduction of appropriate measures.

SARS-CoV-2 wastewater surveillance at two university campuses: lessons learned and insights on intervention strategies for public health guidance

Wastewater surveillance has been a tool for public health officials throughout the COVID-19 pandemic. Universities established pandemic response committees to facilitate safe learning for students, faculty, and staff. These committees met to analyze both wastewater and clinical data to propose mitigation strategies to limit the spread of COVID-19. This paper reviews the initial efforts of utilizing campus data inclusive of wastewater surveillance for SARS-CoV-2 RNA concentrations, clinical case data from university response teams, and mitigation strategies from Grand Valley State University in West Michigan (population 21,648 students) and Oakland University in East Michigan (population 18,552 students) from November 2020 to April 2022. Wastewater positivity rates for both universities ranged from 32.8 to 46.8%. Peak viral signals for both universities directly corresponded to variant points of entry within the campus populations from 2021 to 2022. It was found that the organization of clinical case data and variability of wastewater testing data were large barriers for both universities to effectively understand disease dynamics within the university population. We review the initial efforts of onboarding wastewater surveillance and provide direction for structuring ongoing surveillance workflows and future epidemic response strategies based on those that led to reduced viral signals in campus wastewater.

Significant differences in usage of antibiotics in three Chinese cities measured by wastewater-based epidemiology

Antibiotic use, particularly inappropriate use by irrational prescribing or over-the-counter purchases, is of great concern for China as it facilitates the spread of antibiotic resistances. In this study, we applied wastewater-based epidemiology (WBE) to monitor the total consumption of eight common antibiotics in three cities in northern, eastern and southern China. Wastewater samples were successively collected from 17 wastewater treatment plants including weekdays and weekends spanning four seasons between 2019 and 2021. The concentration of antibiotics and their corresponding metabolites showed a significant correlation, confirming the measured antibiotics were actually consumed. Different seasonal trends in antibiotic use were found among the cities. It was more prevalent in the winter in the northern city Beijing, with the high antibiotic consumption attributed to peak influenza occurrence in the city. This is clear evidence of irrational prescription of antibiotics since it's known that antibiotics do little to treat influenza. In terms of overall consumption, Foshan is significantly lower, thanks to warmer climate and higher use of herbal tea as a prevention measure. WBE estimates of antibiotic consumption were relatively comparable with other data sources, with azithromycin as the top antibiotic measured here. The studied cities had higher WBE estimated antibiotics consumption than results of previous studies in the literature. Monitoring antibiotic use in different areas and periods through WBE in combination with complementary information, can better inform appropriate antibiotic guideline policies in various regions and nations.

Long-term monitoring of SARS-CoV-2 variants in wastewater using a coordinated workflow of droplet digital PCR and nanopore sequencing

Quantitative polymerase chain reaction (PCR) and genome sequencing are important methods for wastewater surveillance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The reverse transcription-droplet digital PCR (RT-ddPCR) is a highly sensitive method for quantifying SARS-CoV-2 RNA in wastewater samples to track the trends of viral activity levels but cannot identify new variants. It also takes time to develop new PCR-based assays targeting variants of interest. Whole genome sequencing (WGS) can be used to monitor known and new SARS-CoV-2 variants, but it is generally not quantitative. Several short-read sequencing techniques can be expensive and might experience delayed turnaround times when outsourced due to inadequate in-house resources. Recently, a portable nanopore sequencing system offers an affordable and real-time method for sequencing SARS-CoV-2 variants in wastewater. This technology has the potential to enable swift response to disease outbreaks without relying on clinical sequencing results. In addressing concerns related to rapid turnaround time and accurate variant analysis, both RT-ddPCR and nanopore sequencing methods were employed to monitor the emergence of SARS-CoV-2 variants in wastewater. This surveillance was conducted at 23 sewer maintenance hole sites and five wastewater treatment plants in Michigan from 2020 to 2022. In 2020, the wastewater samples were dominated by the parental variants (20A, 20C and 20 G), followed by 20I (Alpha, B.1.1.7) in early 2021 and the Delta variant of concern (VOC) in late 2021. For the year 2022, Omicron variants dominated. Nanopore sequencing has the potential to validate suspected variant cases that were initially undetermined by RT-ddPCR assays. The concordance rate between nanopore sequencing and RT-ddPCR assays in identifying SARS-CoV-2 variants to the clade-level was 76.9%. Notably, instances of disagreement between the two methods were most prominent in the identification of the parental and Omicron variants. We also showed that sequencing wastewater samples with SARS-CoV-2 N gene concentrations of >104 GC/100 ml as measured by RT-ddPCR improve genome recovery and coverage depth using MinION device. RT-ddPCR was better at detecting key spike protein mutations A67V, del69-70, K417N, L452R, N501Y, N679K, and R408S (p-value <0.05) as compared to nanopore sequencing. It is suggested that RT-ddPCR and nanopore sequencing should be coordinated in wastewater surveillance where RT-ddPCR can be used as a preliminary quantification method and nanopore sequencing as the confirmatory method for the detection of variants or identification of new variants. The RT-ddPCR and nanopore sequencing methods reported here can be adopted as a reliable in-house analysis of SARS-CoV-2 in wastewater for rapid community level surveillance and public health response.

Monitoring of over-the-counter (OTC) and COVID-19 treatment drugs complement wastewater surveillance of SARS-CoV-2

BACKGROUND

The application of wastewater-based epidemiology to track the outbreak and prevalence of coronavirus disease (COVID-19) in communities has been tested and validated by several researchers across the globe. However, the RNA-based surveillance has its inherent limitations and uncertainties.

OBJECTIVE

This study aims to complement the ongoing wastewater surveillance efforts by analyzing other chemical biomarkers in wastewater to help assess community response (hospitalization and treatment) during the pandemic (2020-2021).

METHODS

Wastewater samples (n = 183) were collected from the largest wastewater treatment facility in Suffolk County, NY, USA and analyzed for COVID-19 treatment drugs (remdesivir, chloroquine, and hydroxychloroquine (HCQ)) and their human metabolites. We additionally monitored 26 pharmaceuticals including common over-the-counter (OTC) drugs. Lastly, we developed a Bayesian model that uses viral RNA, COVID-19 treatment drugs, and pharmaceuticals data to predict the confirmed COVID-19 cases within the catchment area.

RESULTS

The viral RNA levels in wastewater tracked the actual COVID-19 case numbers well as expected. COVID-19 treatment drugs were detected with varying frequency (9-100%) partly due to their instability in wastewater. We observed a significant correlation (R = 0.30, p < 0.01) between the SARS-CoV-2 genes and desethylhydroxychloroquine (DHCQ, metabolite of HCQ). Remdesivir levels peaked immediately after the Emergency Use Authorization approved by the FDA. Although, 13 out of 26 pharmaceuticals assessed were consistently detected (DF = 100%, n = 111), only acetaminophen was significantly correlated with viral loads, especially when the Omicron variant was dominant. The Bayesian models were capable of reproducing the temporal trend of the confirmed cases.

IMPACT

In this study, for the first time, we measured COVID-19 treatment and pharmaceutical drugs and their metabolites in wastewater to complement ongoing COVID-19 viral RNA surveillance efforts. Our results highlighted that, although the COVID-19 treatment drugs were not very stable in wastewater, their detection matched with usage trends in the community. Acetaminophen, an OTC drug, was significantly correlated with viral loads and confirmed cases, especially when the Omicron variant was dominant. A Bayesian model was developed which could predict COVID-19 cases more accurately when incorporating other drugs data along with viral RNA levels in wastewater.

Advances in wastewater analysis revealing the co-circulating viral trends of noroviruses and Omicron subvariants

Co-presence of enveloped and non-enveloped viruses is common both in community circulation and in wastewater. Community surveillance of infections requires robust methods enabling simultaneous quantification of multiple viruses in wastewater. Using enveloped SARS-CoV-2 Omicron subvariants and non-enveloped norovirus (NoV) as examples, this study reports a robust method that integrates electronegative membrane (EM) concentration, viral inactivation, and RNA preservation (VIP) with efficient capture and enrichment of the viral RNA on magnetic (Mag) beads, and direct detection of RNA on the beads. This method provided improved viral recoveries of 80 ± 4 % for SARS-CoV-2 and 72 ± 5 % for Murine NoV. Duplex reverse transcription quantitative polymerase chain reaction (RT-qPCR) assays with newly designed degenerate primer-probe sets offered high PCR efficiencies (90-91 %) for NoV (GI and GII) targets and were able to detect as few as 15 copies of the viral RNA per PCR reaction. This technique, combined with duplex detection of NoV and multiplex detection of Omicron, successfully quantified NoV (GI and GII) and Omicron variants in the same sets of 94 influent wastewater samples collected from two large wastewater systems between July 2022 and June 2023. The wastewater viral RNA results showed temporal changes of both NoV and Omicron variants in the same wastewater systems and revealed an inverse relationship of their emergence. This study demonstrated the importance of a robust analytical platform for simultaneous surveillance of enveloped and non-enveloped viruses in wastewater. The ability to sensitively determine multiple viral pathogens in wastewater will advance applications of wastewater surveillance as a complementary public health tool.

Investigating the Presence of Rotavirus in Wastewater Samples of Bhopal Region, India, by Utilizing Droplet Digital Polymerase Chain Reaction

INTRODUCTION

Rotavirus-induced viral gastroenteritis outbreaks result in over two million hospitalizations globally yearly. Wastewater-based epidemiology (WBE) has emerged as a crucial tool for detecting and monitoring viral outbreaks. The adoption of WBE has been instrumental in the early detection and surveillance of such viral outbreaks, providing a non-invasive method to assess public health.

OBJECTIVE

This study aims to utilize droplet digital polymerase chain reaction (ddPCR) technology to detect and quantify Rotavirus in wastewater samples collected from the Bhopal region of India, thereby contributing to the understanding and management of viral gastroenteritis outbreaks through environmental surveillance.

METHODS

In this study, we used ddPCR to detect and quantify Rotavirus in wastewater samples collected from the Bhopal region of India. We monitored its viral presence in municipal sewage treatment plants bi-weekly using an advanced ddPCR assay. Targeting the rotavirus non-structural protein 3 (NSP-3) region with custom primers and TaqMan probes, we detected virus concentration employing polyethylene glycol (PEG). Following RNA isolation, complementary DNA (cDNA) synthesis, and ddPCR analysis, our novel method eliminated standard curve dependence, propelling virus research and treatment forward.

RESULTS

Out of the 42 samples collected, a 16.60% positivity rate was observed, indicating a moderate presence of Rotavirus in Bhopal. The wastewater treatment plants (WWTP) attached to a hospital exhibited a 42.85% positivity rate, indicating the need for targeted monitoring. Leveraging ddPCR, precise quantification of rotavirus concentrations (ranging from 0.75 to 28.9 copies/µL) facilitated understanding and supported effective remediation.

CONCLUSIONS

 This study emphasizes the importance of vigilant wastewater surveillance, especially in WWTPs with higher rotavirus prevalence. The significance of ddPCR in comparison to conventional and real-time PCR lies in its superior sensitivity and specificity in detecting and quantifying positive samples. Furthermore, it can identify positive samples even in the smallest quantities without the need for a standard curve to evaluate. This makes ddPCR a valuable tool for accurate and precise detection and quantification of samples.

Cross-continental comparative experiences of wastewater surveillance and a vision for the 21st century

The COVID-19 pandemic has brought the epidemiological value of monitoring wastewater into sharp focus. The challenges of implementing and optimising wastewater monitoring vary significantly from one region to another, often due to the array of different wastewater systems around the globe, as well as the availability of resources to undertake the required analyses (e.g. laboratory infrastructure and expertise). Here we reflect on the local and shared challenges of implementing a SARS-CoV-2 monitoring programme in two geographically and socio-economically distinct regions, São Paulo state (Brazil) and Wales (UK), focusing on design, laboratory methods and data analysis, and identifying potential guiding principles for wastewater surveillance fit for the 21st century. Our results highlight the historical nature of region-specific challenges to the implementation of wastewater surveillance, including previous experience of using wastewater surveillance, stakeholders involved, and nature of wastewater infrastructure. Building on those challenges, we then highlight what an ideal programme would look like if restrictions such as resource were not a constraint. Finally, we demonstrate the value of bringing multidisciplinary skills and international networks together for effective wastewater surveillance.

Adapted methods for monitoring influenza virus and respiratory syncytial virus in sludge and wastewater

Wastewater-based surveillance constitutes a valuable methodology for the continuous monitoring of viral circulation, with the capacity to function as an early warning system. It holds particular significance in scenarios where respiratory viruses exhibit overlapping clinical presentations, as occurs with SARS-CoV-2, influenza virus (IV), and respiratory syncytial virus (RSV), and allows seasonal virus outbreaks to be distinguished from COVID-19 peaks. Furthermore, sewage sludge, given it harbors concentrated human waste from a large population, serves as a substantial reservoir for pathogen detection. To effectively integrate wastewater-based epidemiology into infectious disease surveillance, the detection methods employed in wastewater samples must be adapted to the distinct characteristics of sludge matrices. In this study, we adapted and applied protocols for the detection of IV and RSV in sewage sludge, comparing their performance with the results obtained in wastewater. To assess the efficiency of these protocols, sludge and wastewater samples were spiked with IV and RSV RNA, either free or incorporated in lentiviral particles. Samples were concentrated using the aluminum hydroxide adsorption-precipitation method before viral RNA extraction. Absolute virus quantification was carried out by RT-qPCR, including an internal control to monitor potential inhibitory factors. Recovery efficiencies for both free IV and RSV RNA were 60 % in sludge, and 75 % and 71 % respectively in wastewater, whereas the values for IV and RSV RNA in lentiviral particles were 16 % and 10 % in sludge and 21 % and 17 % in wastewater respectively. Additionally, the protocol enabled the quantification of naturally occurring IV and RSV in wastewater and sludge samples collected from two wastewater treatment plants during the winter months, thus affirming the efficacy of the employed methodologies.

Amplidiff: an optimized amplicon sequencing approach to estimating lineage abundances in viral metagenomes

BACKGROUND

Metagenomic profiling algorithms commonly rely on genomic differences between lineages, strains, or species to infer the relative abundances of sequences present in a sample. This observation plays an important role in the analysis of diverse microbial communities, where targeted sequencing of 16S and 18S rRNA, both well-known hypervariable genomic regions, have led to insights into microbial diversity and the discovery of novel organisms. However, the variable nature of discriminatory regions can also act as a double-edged sword, as the sought-after variability can make it difficult to design primers for their amplification through PCR. Moreover, the most variable regions are not necessarily the most informative regions for the purpose of differentiation; one should focus on regions that maximize the number of lineages that can be distinguished.

RESULTS

Here we present AmpliDiff, a computational tool that simultaneously finds highly discriminatory genomic regions in viral genomes of a single species, as well as primers allowing for the amplification of these regions. We show that regions and primers found by AmpliDiff can be used to accurately estimate relative abundances of SARS-CoV-2 lineages, for example in wastewater sequencing data. We obtain errors that are comparable with using whole genome information to estimate relative abundances. Furthermore, our results show that AmpliDiff is robust against incomplete input data and that primers designed by AmpliDiff also bind to genomes sampled months after the primers were selected.

CONCLUSIONS

With AmpliDiff we provide an effective, cost-efficient alternative to whole genome sequencing for estimating lineage abundances in viral metagenomes.

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

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

Wastewater-based epidemiology for COVID-19 using dynamic artificial neural networks

Global efforts in vaccination have led to a decrease in COVID-19 mortality but a high circulation of SARS-CoV-2 is still observed in several countries, resulting in some cases of severe lockdowns. In this sense, wastewater-based epidemiology remains a powerful tool for supporting regional health administrations in assessing risk levels and acting accordingly. In this work, a dynamic artificial neural network (DANN) has been developed for predicting the number of COVID-19 hospitalized patients in hospitals in Valladolid (Spain). This model takes as inputs a wastewater epidemiology indicator for COVID-19 (concentration of RNA from SARS-CoV-2 N1 gene reported from Valladolid Wastewater Treatment Plant), vaccination coverage, and past data of hospitalizations. The model considered both the instantaneous values of these variables and their historical evolution. Two study periods were selected (from May 2021 until September 2022 and from September 2022 to July 2023). During the first period, accurate predictions of hospitalizations (with an overall range between 6 and 171) were favored by the correlation of this indicator with N1 concentrations in wastewater (r = 0.43, p < 0.05), showing accurate forecasting for 1 day ahead and 5 days ahead. The second period's retraining strategy maintained the overall accuracy of the model despite lower hospitalizations. Furthermore, risk levels were assigned to each 1 day ahead prediction during the first and second periods, showing agreement with the level measured and reported by regional health authorities in 95 % and 93 % of cases, respectively. These results evidenced the potential of this novel DANN model for predicting COVID-19 hospitalizations based on SARS-CoV-2 wastewater concentrations at a regional scale. The model architecture herein developed can support regional health authorities in COVID-19 risk management based on wastewater-based epidemiology.

Beyond linear regression: Modeling COVID-19 clinical cases with wastewater surveillance of SARS-CoV-2 for the city of Athens and Ohio University campus

Wastewater-based surveillance has emerged as a detection tool for population-wide infectious diseases, including coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Infected individuals shed the virus, which can be detected in wastewater using molecular techniques such as reverse transcription-digital polymerase chain reaction (RT-dPCR). This study examined the association between the number of clinical cases and the concentration of SARS-CoV-2 in wastewater beyond linear regression and for various normalizations of viral loads. Viral loads were measured in a total of 446 wastewater samples during the period from August 2021 to April 2022. These samples were collected from nine different locations, with 220 samples taken from four specific sites within the city of Athens and 226 samples from five sites within Ohio University. The correlation between COVID-19 cases and wastewater viral concentrations, which was estimated using the Pearson correlation coefficient, was statistically significant and ranged from 0.6 to 0.9. In addition, time-lagged cross correlation was applied to identify the lag time between clinical and wastewater data, estimated 4 to 7 days. While we also explored the effect on the correlation coefficients of various normalizations of viral loads accounting for procedural loss or amount of fecal material and of estimated lag times, these alternative specifications did not change our substantive conclusions. Additionally, several linear and non-linear regression models were applied to predict the COVID-19 cases given wastewater data as input. The non-linear approach was found to yield the highest R-squared and Pearson correlation and lowest Mean Absolute Error values between the predicted and actual number of COVID-19 cases for both aggregated OHIO Campus and city data. Our results provide support for previous studies on correlation and time lag and new evidence that non-linear models, approximated with artificial neural networks, should be implemented for WBS of contagious diseases.

Real-Time Epidemiology and Acute Care Need Monitoring and Forecasting for COVID-19 via Bayesian Sequential Monte Carlo-Leveraged Transmission Models

COVID-19 transmission models have conferred great value in informing public health understanding, planning, and response. However, the pandemic also demonstrated the infeasibility of basing public health decision-making on transmission models with pre-set assumptions. No matter how favourably evidenced when built, a model with fixed assumptions is challenged by numerous factors that are difficult to predict. Ongoing planning associated with rolling back and re-instituting measures, initiating surge planning, and issuing public health advisories can benefit from approaches that allow state estimates for transmission models to be continuously updated in light of unfolding time series. A model being continuously regrounded by empirical data in this way can provide a consistent, integrated depiction of the evolving underlying epidemiology and acute care demand, offer the ability to project forward such a depiction in a fashion suitable for triggering the deployment of acute care surge capacity or public health measures, and support quantitative evaluation of tradeoffs associated with prospective interventions in light of the latest estimates of the underlying epidemiology. We describe here the design, implementation, and multi-year daily use for public health and clinical support decision-making of a particle-filtered COVID-19 compartmental model, which served Canadian federal and provincial governments via regular reporting starting in June 2020. The use of the Bayesian sequential Monte Carlo algorithm of particle filtering allows the model to be regrounded daily and adapt to new trends within daily incoming data-including test volumes and positivity rates, endogenous and travel-related cases, hospital census and admissions flows, daily counts of dose-specific vaccinations administered, measured concentration of SARS-CoV-2 in wastewater, and mortality. Important model outputs include estimates (via sampling) of the count of undiagnosed infectives, the count of individuals at different stages of the natural history of frankly and pauci-symptomatic infection, the current force of infection, effective reproductive number, and current and cumulative infection prevalence. Following a brief description of the model design, we describe how the machine learning algorithm of particle filtering is used to continually reground estimates of the dynamic model state, support a probabilistic model projection of epidemiology and health system capacity utilization and service demand, and probabilistically evaluate tradeoffs between potential intervention scenarios. We further note aspects of model use in practice as an effective reporting tool in a manner that is parameterized by jurisdiction, including the support of a scripting pipeline that permits a fully automated reporting pipeline other than security-restricted new data retrieval, including automated model deployment, data validity checks, and automatic post-scenario scripting and reporting. As demonstrated by this multi-year deployment of the Bayesian machine learning algorithm of particle filtering to provide industrial-strength reporting to inform public health decision-making across Canada, such methods offer strong support for evidence-based public health decision-making informed by ever-current articulated transmission models whose probabilistic state and parameter estimates are continually regrounded by diverse data streams.

Urine biomarkers for Alzheimer's disease: A new opportunity for wastewater-based epidemiology?

While Alzheimer's disease (AD) diagnosis, management, and care have become priorities for healthcare providers and researcher's worldwide due to rapid population aging, epidemiologic surveillance efforts are currently limited by costly, invasive diagnostic procedures, particularly in low to middle income countries (LMIC). In recent years, wastewater-based epidemiology (WBE) has emerged as a promising tool for public health assessment through detection and quantification of specific biomarkers in wastewater, but applications for non-infectious diseases such as AD remain limited. This early review seeks to summarize AD-related biomarkers and urine and other peripheral biofluids and discuss their potential integration to WBE platforms to guide the first prospective efforts in the field. Promising results have been reported in clinical settings, indicating the potential of amyloid β, tau, neural thread protein, long non-coding RNAs, oxidative stress markers and other dysregulated metabolites for AD diagnosis, but questions regarding their concentration and stability in wastewater and the correlation between clinical levels and sewage circulation must be addressed in future studies before comprehensive WBE systems can be developed.

An interpretative review of the wastewater-based surveillance of the SARS-CoV-2: where do we stand on its presence and concern?

Wastewater-based epidemiology (WBE) has been used for monitoring infectious diseases like polio, hepatitis, etc. since the 1940s. It is also being used for tracking the SARS-CoV-2 at the population level. This article aims to compile and assess the information for the qualitative and quantitative detection of the SARS-CoV-2 in wastewater. Based on the globally published studies, we highlight the importance of monitoring SARS-CoV-2 presence/detection in the wastewater and concurrently emphasize the development of early surveillance techniques. SARS-CoV-2 RNA sheds in the human feces, saliva, sputum and mucus that ultimately reaches to the wastewater and brings viral RNA into it. For the detection of the virus in the wastewater, different detection techniques have been optimized and are in use. These are based on serological, biosensor, targeted PCR, and next generation sequencing for whole genome sequencing or targeted amplicon sequencing. The presence of the SARS-CoV-2 RNA in wastewater could be used as a potential tool for early detection and devising the strategies for eradication of the virus before it is spread in the community. Additionally, with the right and timely understanding of viral behavior in the environment, an accurate and instructive model that leverages WBE-derived data may be created. This might help with the creation of technological tools and doable plans of action to lessen the negative effects of current viral epidemics or future potential outbreaks on public health and the economy. Further work toward whether presence of viral load correlates with its ability to induce infection, still needs evidence. The current increasing incidences of JN.1 variant is a case in point for continued early detection and surveillance, including wastewater.

Changes to Public Health Surveillance Methods Due to the COVID-19 Pandemic: Scoping Review

BACKGROUND

Public health surveillance plays a vital role in informing public health decision-making. The onset of the COVID-19 pandemic in early 2020 caused a widespread shift in public health priorities. Global efforts focused on COVID-19 monitoring and contact tracing. Existing public health programs were interrupted due to physical distancing measures and reallocation of resources. The onset of the COVID-19 pandemic intersected with advancements in technologies that have the potential to support public health surveillance efforts.

OBJECTIVE

This scoping review aims to explore emergent public health surveillance methods during the early COVID-19 pandemic to characterize the impact of the pandemic on surveillance methods.

METHODS

A scoping search was conducted in multiple databases and by scanning key government and public health organization websites from March 2020 to January 2022. Published papers and gray literature that described the application of new or revised approaches to public health surveillance were included. Papers that discussed the implications of novel public health surveillance approaches from ethical, legal, security, and equity perspectives were also included. The surveillance subject, method, location, and setting were extracted from each paper to identify trends in surveillance practices. Two public health epidemiologists were invited to provide their perspectives as peer reviewers.

RESULTS

Of the 14,238 unique papers, a total of 241 papers describing novel surveillance methods and changes to surveillance methods are included. Eighty papers were review papers and 161 were single studies. Overall, the literature heavily featured papers detailing surveillance of COVID-19 transmission (n=187). Surveillance of other infectious diseases was also described, including other pathogens (n=12). Other public health topics included vaccines (n=9), mental health (n=11), substance use (n=4), healthy nutrition (n=1), maternal and child health (n=3), antimicrobial resistance (n=2), and misinformation (n=6). The literature was dominated by applications of digital surveillance, for example, by using big data through mobility tracking and infodemiology (n=163). Wastewater surveillance was also heavily represented (n=48). Other papers described adaptations to programs or methods that existed prior to the COVID-19 pandemic (n=9). The scoping search also found 109 papers that discuss the ethical, legal, security, and equity implications of emerging surveillance methods. The peer reviewer public health epidemiologists noted that additional changes likely exist, beyond what has been reported and available for evidence syntheses.

CONCLUSIONS

The COVID-19 pandemic accelerated advancements in surveillance and the adoption of new technologies, especially for digital and wastewater surveillance methods. Given the investments in these systems, further applications for public health surveillance are likely. The literature for surveillance methods was dominated by surveillance of infectious diseases, particularly COVID-19. A substantial amount of literature on the ethical, legal, security, and equity implications of these emerging surveillance methods also points to a need for cautious consideration of potential harm.

Detection and diversity of gastrointestinal viruses in wastewater from Caracas, Venezuela, 2021-2022

Gastrointestinal viruses (GIV) are an important cause of childhood morbidity and mortality, particularly in developing countries. Their epidemiological impact in Venezuela during the COVID-19 pandemic remains unclear. GIV can also be detected in domestic sewage. Ninety-one wastewater samples from urban areas of Caracas collected over 12 months and concentrated by polyethylene-glycol-precipitation, were analyzed by multiplex reverse-transcription-PCR for rotavirus/calicivirus/astrovirus and enterovirus/klassevirus/cosavirus, and monoplex-PCR for adenovirus and Aichi virus. The overall frequency of virus detection was 46.2%, fluctuating over months, and peaking in the rainy season. Adenoviruses circulated throughout the year, especially type F41, and predominated (52.7%) over caliciviruses (29.1%) that peaked in the rainy months, rotaviruses (9.1%), cosaviruses (5.5%), astroviruses and enteroviruses (1.8%). Aichi-virus and klassevirus were absent. Rotavirus G9/G12, and P[4]/P[8]/P[14] predominated. The occurrence of GIV in wastewater reflects transmission within the population of Caracas and the persistence of a potential public health risk that needs to be adequately monitored.

SARS-CoV-2 Detection and Genome Sequencing in Urban Wastewaters

Due to the excretion of SARS-CoV-2 in faeces, the use of wastewater-based epidemiology (WBE) is a useful tool for virus surveillance in large populations. The analysis of this virus includes a concentration step prior to virus detection by RT-qPCR. In addition, the use of massive sequencing allows the detection of specific mutations of clinical importance, as well as the detection of the introduction of new lineages in a specific population. In this chapter, we describe the analysis of SARS-CoV-2 in urban wastewater by the concentration of the samples by precipitation with aluminum chloride, the detection, and quantification of SARS-CoV-2 RNA by RT-qPCR and the genomic sequencing using two different sequencing platforms.

Tracking the effects of the COVID-19 pandemic on viral gastroenteritis through wastewater-based retrospective analyses

The COVID-19 pandemic possibly disrupted the circulation and seasonality of gastroenteritis viruses (e.g., Norovirus (NoV), Sapovirus (SaV), group A rotavirus (ARoV), and Aichivirus (AiV)). Despite the growing application of wastewater-based epidemiology (WBE), there remains a lack of sufficient investigations into the actual impact of the COVID-19 pandemic on the prevalence of gastroenteritis viruses. In this study, we measured NoV GI and GII, SaV, ARoV, and AiV RNA concentrations in 296 influent wastewater samples collected from three wastewater treatment plants (WWTPs) in Sapporo, Japan between October 28, 2018 and January 12, 2023 using the highly sensitive EPISENS™ method. The detection ratios of SaV and ARoV after May 2020 (SaV: 49.8 % (134/269), ARoV: 57.4 % (151/263)) were significantly lower than those before April 2020 (SaV: 93.9 % (31/33), ARoV: 97.0 % (32/33); SaV: p < 3.5×10-7, ARoV: p < 1.5×10-6). Furthermore, despite comparable detection ratios before (88.5 %, 23/26) and during (66.7 %, 80/120) the COVID-19 pandemic (p = 0.032), the concentrations of NoV GII revealed a significant decrease after the onset of the pandemic (p < 1.5×10-7, Cliff's delta = 0.72). NoV GI RNA were sporadically detected (24.7 %, 8/33) before April 2020 and after May 2020 (6.5 %, 17/263), whereas AiV was consistently (100 %, 33/33) detected from wastewater throughout the study period (95.8 %, 252/263). The WBE results demonstrated the significant influence of COVID-19 countermeasures on the circulation of gastroenteritis viruses, with variations observed in the magnitude of their impact across different types of viruses. These epidemiological findings highlight that the hygiene practices implemented to prevent COVID-19 infections may also be effective for controlling the prevalence of gastroenteritis viruses, providing invaluable insights for public health units and the development of effective disease management guidelines.

Early warning of statewide COVID-19 Omicron wave by sentineled urbanized sewer network monitoring using digital PCR in a province capital city, of Gujarat, India

Wastewater-based epidemiology (WBE) has been implemented globally. However, there remains confusion about the number and frequency of samples to be collected, as well as which types of treatment systems can provide reliable specific details about the virus prevalence in specific areas or communities, enabling prompt management and intervention measures. More research is necessary to fully comprehend the possibility of deploying sentinel locations in sewer networks in larger geographic areas. The present study introduces the first report on wastewater-based surveillance in Gandhinagar City using digital PCR (d-PCR) as a SARS-Cov-2 quantification tool, which describes the viral load from five pumping stations in Gandhinagar from October 2021 to March 2022. Raw wastewater samples (n = 119) were received and analyzed weekly to detect SARS-CoV-2 RNA, 109 of which were positive for N1 or N2 genes. The monthly variation analysis in viral genome copies depicted the highest concentrations in January 2022 and February 2022 (p < 0.05; Wilcoxon signed rank test) coincided with the Omicron wave, which contributed mainly from Vavol and Jaspur pumping stations. Cross-correlation analysis indicated that WBE from five stations in Gandhinagar, i.e., capital city sewer networks, provided two-week lead times to the citywide and statewide active cases (time-series cross-correlation function [CCF]; 0.666 and 0.648, respectively), mainly from individual contributions of the urbanized Kudasan and Vavol stations (CCF; 0.729 and 0.647, respectively). These findings suggest that sewer pumping stations in urbanized neighborhoods can be used as sentinel sites for statewide clinical surveillance and that WBE surveillance using digital PCR can be an efficient monitoring and management tool.

Surveillance of SARS-CoV-2, rotavirus, norovirus genogroup II, and human adenovirus in wastewater as an epidemiological tool to anticipate outbreaks of COVID-19 and acute gastroenteritis in a city without a wastewater treatment plant in the Peruvian Highlands

The coronavirus disease 2019 (COVID-19) pandemic has demonstrated that Wastewater Based Epidemiology is a fast and economical alternative for monitoring severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) at the community level in high-income countries. In the present study, wastewater from a city in the Peruvian Highlands, which lacks a wastewater treatment plant, was monitored for one year to assess the relationship between the concentration of SARS-CoV-2 and the reported cases of COVID-19 in the community. Additionally, we compared the relationship between rotavirus (RV), norovirus genogroup II (NoV GGII), and human adenovirus (HAdV) with the number of reported cases of acute gastroenteritis. Before commencing the analysis of the samples, the viral recovery efficacy of three processing methods was determined in spiked wastewater with SARS-CoV-2. This evaluation demonstrated the highest recovery rate with direct analysis (72.2 %), as compared to ultrafiltration (50.8 %) and skimmed milk flocculation (5.6 %). Wastewater monitoring revealed that 72 % (36/50) of the samples tested positive for SARS-CoV-2, with direct analysis yielding the highest detection frequency and quantification of SARS-CoV-2. Furthermore, a strong correlation was observed between the concentration of SARS-CoV-2 in wastewater and the reported cases of COVID-19, mainly when we shift the concentration of SARS-CoV-2 by two weeks, which allows us to anticipate the onset of the fourth and fifth waves of the pandemic in Peru up to two weeks in advance. All samples processed using the skimmed milk flocculation method tested positive and showed high concentrations of RV, NoV GGII, and HAdV. In fact, the highest RV concentrations were detected up to four weeks before outbreaks of acute gastroenteritis reported in children under four years of age. In conclusion, the results of this study suggest that periodic wastewater monitoring is an excellent epidemiological tool for surveillance and can anticipate outbreaks of infectious diseases, such as COVID-19, in low- and middle-income countries.

Metagenomic sequencing detects human respiratory and enteric viruses in air samples collected from congregate settings

Innovative methods for evaluating virus risk and spread, independent of test-seeking behavior, are needed to improve routine public health surveillance, outbreak response, and pandemic preparedness. Throughout the COVID-19 pandemic, environmental surveillance strategies, including wastewater andair sampling, have been used alongside widespread individual-based SARS-CoV-2 testing programs to provide population-level data. These environmental surveillance strategies have predominantly relied on pathogen-specific detection methods to monitor viruses through space and time. However, this provides a limited picture of the virome present in an environmental sample, leaving us blind to most circulating viruses. In this study, we explore whether pathogen-agnostic deep sequencing can expand the utility of air sampling to detect many human viruses. We show that sequence-independent single-primer amplification sequencing of nucleic acids from air samples can detect common and unexpected human respiratory and enteric viruses, including influenza virus type A and C, respiratory syncytial virus, human coronaviruses, rhinovirus, SARS-CoV-2, rotavirus, mamastrovirus, and astrovirus.

The detection of Japanese encephalitis virus in municipal wastewater during an acute disease outbreak

AIM

To demonstrate the capability of wastewater-based surveillance (WBS) as a tool for detecting potential cases of Japanese Encephalitis Virus (JEV) infection in the community.

METHODS AND RESULTS

In this study, we explore the potential of WBS to detect cases of JEV infection by leveraging from an established SARS-CoV-2 wastewater surveillance program. We describe the use of two reverse transcriptase quantitative polymerase chain reaction (RTqPCR) assays targeting JEV to screen archived samples from two wastewater treatment plants (WWTPs). JEV was detected in wastewater samples collected during a timeframe coinciding with a cluster of acute human encephalitis cases, alongside concurrent evidence of JEV detection in mosquito surveillance and the sentinel chicken programs within South Australia's Riverland and Murraylands regions.

CONCLUSIONS

Current surveillance measures for JEV encounter multiple constraints, which may miss the early stages of JEV circulation or fail to capture the full extent of transmission. The detection of JEV in wastewater during a disease outbreak highlights the potential WBS has as a complementary layer to existing monitoring efforts forming part of the One Health approach required for optimal disease response and control.

Operationalizing an open-source dashboard for communicating results of wastewater-based surveillance

COVID-19 saw the expansion of public health tools to manage the pandemic. One tool that saw extensive use was the public health dashboard, web-based visualization tools that communicate information to users in easy-to-read graphics. Dashboards were widely used prior to the pandemic, but COVID-19 saw expanded use and development. To date, dashboards have become an important part of public health surveillance programs around the world helping decisionmakers use data to evaluate different public health metrics including caseloads, hospitalizations, and environmental surveillance results from testing wastewater. Wastewater surveillance provides community-based, spatially relevant data on disease trends within communities to assess the scale of infection in a region, which makes it an excellent candidate for dashboard development to improve public health. We developed a dashboard for New York State's wastewater surveillance program using open-source, reproducible web programming. The dashboard we developed has been used for the COVID-19 response in New York, and our methods can be adapted to other programs and pathogens. We provide:•descriptions of how the dashboard was developed and maintained•specific guidance for reproducing our dashboard in other areas and for other pathogens•fully reproducible code with step-by-step instructions for researchers and professionals to make their own data dashboards.

Crykey: Rapid Identification of SARS-CoV-2 Cryptic Mutations in Wastewater

We present Crykey, a computational tool for rapidly identifying cryptic mutations of SARS-CoV-2. Specifically, we identify co-occurring single nucleotide mutations on the same sequencing read, called linked-read mutations, that are rare or entirely missing in existing databases, and have the potential to represent novel cryptic lineages found in wastewater. While previous approaches exist for identifying cryptic linked-read mutations from specific regions of the SARS-CoV-2 genome, there is a need for computational tools capable of efficiently tracking cryptic mutations across the entire genome and for tens of thousands of samples and with increased scrutiny, given their potential to represent either artifacts or hidden SARS-CoV-2 lineages. Crykey fills this gap by identifying rare linked-read mutations that pass stringent computational filters to limit the potential for artifacts. We evaluate the utility of Crykey on >3,000 wastewater and >22,000 clinical samples; our findings are three-fold: i) we identify hundreds of cryptic mutations that cover the entire SARS-CoV-2 genome, ii) we track the presence of these cryptic mutations across multiple wastewater treatment plants and over a three years of sampling in Houston, and iii) we find a handful of cryptic mutations in wastewater mirror cryptic mutations in clinical samples and investigate their potential to represent real cryptic lineages. In summary, Crykey enables large-scale detection of cryptic mutations representing potential cryptic lineages in wastewater.

Analyzing community wastewater in sub-sewersheds for the small-scale detection of SARS-CoV-2 variants in a German metropolitan area

Wastewater surveillance of SARS-CoV-2 proved useful, including for identifying the local appearance of newly identified virus variants. Previous studies focused on wastewater treatment plants (WWTP) with sewersheds of several hundred thousand people or at single building level, representing only a small number of people. Both approaches may prove inadequate for small-scale intra-urban inferences for early detection of emerging or novel virus variants. Our study aims (i) to analyze SARS-CoV-2 single nucleotide variants (SNVs) in wastewater of sub-sewersheds and WWTP using whole genome sequencing in order to (ii) investigate the potential of small-scale detection of novel known SARS-CoV-2 variants of concern (VOC) within a metropolitan wastewater system. We selected three sub-sewershed sampling sites, based on estimated population- and built environment-related indicators, and the inlet of the receiving WWTP in the Ruhr region, Germany. Untreated wastewater was sampled weekly between October and December 2021, with a total of 22 samples collected. SARS-CoV-2 RNA was analyzed by RT-qPCR and whole genome sequencing. For all samples, genome sequences were obtained, while only 13 samples were positive for RT-qPCR. We identified multiple specific SARS-CoV-2 SNVs in the wastewater samples of the sub-sewersheds and the WWTP. Identified SNVs reflected the dominance of VOC Delta at the time of sampling. Interestingly, we could identify an Omicron-specific SNV in one sub-sewershed. A concurrent wastewater study sampling the same WWTP detected the VOC Omicron one week later. Our observations suggest that the small-scale approach may prove particularly useful for the detection and description of spatially confined emerging or existing virus variants circulating in populations. Future studies applying small-scale sampling strategies taking into account the specific features of the wastewater system will be useful to analyze temporal and spatial variance in more detail.

Metagenomic sequencing detects human respiratory and enteric viruses in air samples collected from congregate settings

Innovative methods for evaluating virus risk and spread, independent of test-seeking behavior, are needed to improve routine public health surveillance, outbreak response, and pandemic preparedness. Throughout the COVID-19 pandemic, environmental surveillance strategies, including wastewater and air sampling, have been used alongside widespread individual-based SARS-CoV-2 testing programs to provide population-level data. These environmental surveillance strategies have predominantly relied on pathogen-specific detection methods to monitor viruses through space and time. However, this provides a limited picture of the virome present in an environmental sample, leaving us blind to most circulating viruses. In this study, we explore whether pathogen-agnostic deep sequencing can expand the utility of air sampling to detect many human viruses. We show that sequence-independent single-primer amplification sequencing of nucleic acids from air samples can detect common and unexpected human respiratory and enteric viruses, including influenza virus type A and C, respiratory syncytial virus, human coronaviruses, rhinovirus, SARS-CoV-2, rotavirus, mamastrovirus, and astrovirus.

Wastewater-based surveillance models for COVID-19: A focused review on spatio-temporal models

The evident shedding of the SARS-CoV-2 RNA particles from infected individuals into the wastewater opened up a tantalizing array of possibilities for prediction of COVID-19 prevalence prior to symptomatic case identification through community testing. Many countries have therefore explored the use of wastewater metrics as a surveillance tool, replacing traditional direct measurement of prevalence with cost-effective approaches based on SARS-CoV-2 RNA concentrations in wastewater samples. Two important aspects in building prediction models are: time over which the prediction occurs and space for which the predicted case numbers is shown. In this review, our main focus was on finding mathematical models which take into the account both the time-varying and spatial nature of wastewater-based metrics into account. We used six main characteristics as our assessment criteria: i) modelling approach; ii) temporal coverage; iii) spatial coverage; iv) sample size; v) wastewater sampling method; and vi) covariates included in the modelling. The majority of studies in the early phases of the pandemic recognized the temporal association of SARS-CoV-2 RNA concentration level in wastewater with the number of COVID-19 cases, ignoring their spatial context. We examined 15 studies up to April 2023, focusing on models considering both temporal and spatial aspects of wastewater metrics. Most early studies correlated temporal SARS-CoV-2 RNA levels with COVID-19 cases but overlooked spatial factors. Linear regression and SEIR models were commonly used (n = 10, 66.6 % of studies), along with machine learning (n = 1, 6.6 %) and Bayesian approaches (n = 1, 6.6 %) in some cases. Three studies employed spatio-temporal modelling approach (n = 3, 20.0 %). We conclude that the development, validation and calibration of further spatio-temporally explicit models should be done in parallel with the advancement of wastewater metrics before the potential of wastewater as a surveillance tool can be fully realised.

Analysis of Wastewater Samples to Explore Community Substance Use in the United States: Pilot Correlative and Machine Learning Study

BACKGROUND

Substance use disorder and associated deaths have increased in the United States, but methods for detecting and monitoring substance use using rapid and unbiased techniques are lacking. Wastewater-based surveillance is a cost-effective method for monitoring community drug use. However, the examination of the results often focuses on descriptive analysis.

OBJECTIVE

The objective of this study was to explore community substance use in the United States by analyzing wastewater samples. Geographic differences and commonalities of substance use were explored.

METHODS

Wastewater was sampled across the United States (n=12). Selected drugs with misuse potential, prescriptions, and over-the-counter drugs and their metabolites were tested across geographic locations for 7 days. Methods used included wastewater assessment of substances and metabolites paired with machine learning, specifically discriminant analysis and cluster analysis, to explore similarities and differences in wastewater measures.

RESULTS

Geographic variations in the wastewater drug or metabolite levels were found. Results revealed a higher use of methamphetamine (z=-2.27, P=.02) and opioids-to-methadone ratios (oxycodone-to-methadone: z=-1.95, P=.05; hydrocodone-to-methadone: z=-1.95, P=.05) in states west of the Mississippi River compared to the east. Discriminant analysis suggested temazepam and methadone were significant predictors of geographical locations. Precision, sensitivity, specificity, and F1-scores were 0.88, 1, 0.80, and 0.93, respectively. Finally, cluster analysis revealed similarities in substance use among communities.

CONCLUSIONS

These findings suggest that wastewater-based surveillance has the potential to become an effective form of surveillance for substance use. Further, advanced analytical techniques may help uncover geographical patterns and detect communities with similar needs for resources to address substance use disorders. Using automated analytics, these advanced surveillance techniques may help communities develop timely, tailored treatment and prevention efforts.

Wastewater profiling of illicit drugs, an estimation of community consumption: A case study of eThekwini Metropolitan Municipality, South Africa

Estimation of community-wide consumption of illicit drugs through wastewater analysis is a new concept in Africa although widely applied in developed nations as wastewater-based epidemiology (WBE). It is an important tool that can be used in understanding supply and demand patterns of illicit drug use on a local, national, and international scale. Information on illicit drug use is currently limited in Africa, because of a lack of monitoring structures by governments and financial constraints. This study hopes to bridge that gap by contributing to Africa's baseline information on illicit drug use. This study provides the first application of wastewater analysis to quantitatively evaluate daily illicit drug use in the eThekwini Metropolitan Municipality of KwaZulu-Natal Province of South Africa by quantifying the major urinary excreted metabolites, called drug target residues (DTRs), in raw wastewater from four major wastewater treatment plants (WWTPs) sampled for a week. The results showed that cocaine was the dominant illicit drug consumed in the catchment followed by amphetamine, methamphetamine, 3,4-methylenedioxymethamphetamine and 3,4-methylenedioxyamphetamine, with a per capita use of 360-3000 mg day-1 1000 inh-1, 47-800 mg day-1 1000 inh-1, 19-120 mg day -1 1000 inh-1, not detected (ND) to 4.9 mg day-1 1000 inh-1, and ND to 410 g day-1 1000 inh-1 respectively. The weekly usage patterns between the four WWTPs differed probably due to the lifestyle of the populace serving the different WWTPs. These results provide useful data on illicit drug use in eThekwini that can be utilised by public health agencies to implement suitable response strategies.

Measures against COVID-19 affected the spread of human enteric viruses in a Swedish community, as found when monitoring wastewater

The quantification of viral genomes in wastewater reflects the prevalence of viral infections within the community. Knowledge of how the spread of common enteric viruses in the community was affected by the Swedish COVID-19 interventions is limited. To investigate this, the weekly wastewater samples collected for monitoring SARS-CoV-2 throughout the COVID-19 pandemic at the Rya sewage treatment plant in Gothenburg were also analyzed for adenovirus, norovirus GII, astrovirus, and rotavirus. The amount of each viral genome was quantified by real-time-qPCR and compared with the quantity of these viral genomes in wastewater from 2017. The results showed that the winter seasonality of norovirus GII and rotavirus in wastewater observed in 2017 was interrupted shortly after the introduction of the COVID-19 interventions, and they remained at low level throughout the pandemic. The circulation pattern of astrovirus and adenovirus was less affected. When the COVID-19 restrictions were lifted in 2022, a dramatic increase was observed in the amount of norovirus GII, rotavirus, and adenovirus genomes in wastewater. The changes in abundance and seasonality of some viruses identified through wastewater monitoring were consistent with changes in the number of patients diagnosed with these viruses. These findings suggest that moderate intervention to prevent COVID-19 significantly reduced the spread of some enteric viruses in the community. The results show that wastewater monitoring is a valuable tool for detecting the spread and outbreaks of viral infections that may cause gastroenteritis also when people do not seek medical help, such as during the COVID-19 pandemic.

Campus node-based wastewater surveillance enables COVID-19 case localization and confirms lower SARS-CoV-2 burden relative to the surrounding community

Wastewater-based surveillance (WBS) has been established as a powerful tool that can guide health policy at multiple levels of government. However, this approach has not been well assessed at more granular scales, including large work sites such as University campuses. Between August 2021 and April 2022, we explored the occurrence of SARS-CoV-2 RNA in wastewater using qPCR assays from multiple complimentary sewer catchments and residential buildings spanning the University of Calgary's campus and how this compared to levels from the municipal wastewater treatment plant servicing the campus. Real-time contact tracing data was used to evaluate an association between wastewater SARS-CoV-2 burden and clinically confirmed cases and to assess the potential of WBS as a tool for disease monitoring across worksites. Concentrations of wastewater SARS-CoV-2 N1 and N2 RNA varied significantly across six sampling sites - regardless of several normalization strategies - with certain catchments consistently demonstrating values 1-2 orders higher than the others. Relative to clinical cases identified in specific sewersheds, WBS provided one-week leading indicator. Additionally, our comprehensive monitoring strategy enabled an estimation of the total burden of SARS-CoV-2 for the campus per capita, which was significantly lower than the surrounding community (p≤0.001). Allele-specific qPCR assays confirmed that variants across campus were representative of the community at large, and at no time did emerging variants first debut on campus. This study demonstrates how WBS can be efficiently applied to locate hotspots of disease activity at a very granular scale, and predict disease burden across large, complex worksites.

Antimicrobials and antimicrobial resistance genes in a one-year city metabolism longitudinal study using wastewater-based epidemiology

This longitudinal study tests correlations between antimicrobial agents (AA) and corresponding antimicrobial resistance genes (ARGs) generated by a community of >100 k people inhabiting one city (Bath) over a 13 month randomised monitoring programme of community wastewater. Several AAs experienced seasonal fluctuations, such as the macrolides erythromycin and clarithromycin that were found in higher loads in winter, whilst other AA levels, including sulfamethoxazole and sulfapyridine, stayed consistent over the study period. Interestingly, and as opposed to AAs, ARGs prevalence was found to be less variable, which indicates that fluctuations in AA usage might either not directly affect ARG levels or this process spans beyond the 13-month monitoring period. However, it is important to note that weekly positive correlations between individual associated AAs and ARGs were observed where seasonal variability in AA use was reported: ermB and macrolides CLR-clarithromycin and dmCLR-N-desmethyl clarithromycin, aSPY- N-acetyl sulfapyridine and sul1, and OFX-ofloxacin and qnrS. Furthermore, ARG loads normalised to 16S rRNA (gene load per microorganism) were positively correlated to the ARG loads normalised to the human population (gene load per capita), which indicates that the abundance of microorganisms is proportional to the size of human population and that the community size, and not AA levels, is a major driver of ARG levels in wastewater. Comparison of hospital and community wastewater showed higher number of AAs and their metabolites, their frequency of occurrence and concentrations in hospital wastewater. Examples include: LZD-linezolid (used only in severe bacterial infections) and AMX-amoxicillin (widely used, also in community but with very low wastewater stability) that were found only in hospital wastewater. CIP-ciprofloxacin, SMX-sulfamethoxazole, TMP-trimethoprim, MTZ-metronidazole and macrolides were found at much higher concentrations in hospital wastewater while TET-tetracycline and OTC-oxytetracycline, as well as antiretrovirals, had an opposite trend. In contrast, comparable concentrations of resistant genes were observed in both community and hospital wastewater. This supports the hypothesis that AMR levels are more of an endemic nature, developing over time in individual communities. Both hospital and community wastewater had AAs that exceeded PNEC values (e.g. CLR-clarithromycin, CIP-ciprofloxacin). In general, though, hospital effluents had a greater number of quantifiable AAs exceeding PNECs (e.g. SMX-sulfamethoxazole, ERY-erythromycin, TMP-trimethoprim). Hospitals are therefore an important consideration in AMR surveillance as could be high risk areas for AMR.

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

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

Porcine Epidemic Diarrhea Virus, Surrogate for Coronavirus Decay Measurement in French Coastal Waters and Contribution to Coronavirus Risk Evaluation

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in infected patients mainly displays pulmonary and oronasal tropism; however, the presence of the virus has also been demonstrated in the stools of patients and consequently in wastewater treatment plant effluents, raising the question of the potential risk of environmental contamination (such as seawater contamination) through inadequately treated wastewater spillover into surface or coastal waters even if the environmental detection of viral RNA alone does not substantiate risk of infection. Therefore, here, we decided to experimentally evaluate the persistence of the porcine epidemic diarrhea virus (PEDv), considered as a coronavirus representative model, in the coastal environment of France. Coastal seawater was collected, sterile-filtered, and inoculated with PEDv before incubation for 0 to 4 weeks at four temperatures representative of those measured along the French coasts throughout the year (4, 8, 15, and 24°C). The decay rate of PEDv was determined using mathematical modeling and was used to determine the half-life of the virus along the French coast in accordance with temperatures from 2000 to 2021. We experimentally observed an inverse correlation between seawater temperature and the persistence of infectious viruses in seawater and confirm that the risk of transmission of infectious viruses from contaminated stool in wastewater to seawater during recreational practices is very limited. The present work represents a good model to assess the persistence of coronaviruses in coastal environments and contributes to risk evaluation, not only for SARS-CoV-2 persistence, but also for other coronaviruses, specifically enteric coronaviruses from livestock. IMPORTANCE The present work addresses the question of the persistence of coronavirus in marine environments because SARS-CoV-2 is regularly detected in wastewater treatment plants, and the coastal environment, subjected to increasing anthropogenic pressure and the final receiver of surface waters and sometimes insufficiently depurated wastewater, is particularly at risk. The problem also arises in the possibility of soil contamination by CoV from animals, especially livestock, during manure application, where, by soil impregnation and runoff, these viruses can end up in seawater. Our findings are of interest to researchers and authorities seeking to monitor coronaviruses in the environment, either in tourist areas or in regions of the world where centralized systems for wastewater treatment are not implemented, and more broadly, to the scientific community involved in "One Health" approaches.

Investigation of New Psychoactive Substances (NPS), Other Illicit Drugs, and Drug-Related Compounds in a Taiwanese Wastewater Sample Using High-Resolution Mass-Spectrometry-Based Targeted and Suspect Screening

The proliferation of new psychoactive substances (NPSs) in recent years has posed a significant challenge to public health. Traditional monitoring methods have proven insufficient in tracking these constantly evolving substances, leading to the development of alternative approaches such as wastewater-based epidemiology (WBE). The present study aims to utilize high-resolution mass spectrometry (HRMS)-based targeted and suspect screening to profile NPS, other illicit drugs, and drug-related compounds in a Taiwanese wastewater sample. For the targeted analysis, 8 out 18 standards of illicit drugs have been identified. The suspect screening approach based on approximately 3600 substances in the SWGDRUG library can further identify 92 compounds, including opiate analgesics, synthetic cathinones, phenylalkylamines derivatives, phenethylamine derivatives, tryptamine derivatives, steroids, and ephedrine-related compounds. Additionally, the presence of 5-methoxy-2-aminoindane (MEAI) in the wastewater indicates that drug dealers have recently sold this potential NPS to evade drug regulations. This study firstly reports the HRMS-based comprehensive profile of NPS, other illicit drugs, and drug-related compounds in Taiwan, which could be applied as biomarkers for estimating the consumption of drugs.

Structured Ethical Review for Wastewater-Based Testing

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

Abstract Figure

Synopsis

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

The city-wide full-scale interactive application of sewage surveillance programme for assisting real-time COVID-19 pandemic control - A case study in Hong Kong

The paper discusses the implementation of Hong Kong's tailor-made sewage surveillance programme led by the Government, which has demonstrated how an efficient and well-organized sewage surveillance system can complement conventional epidemiological surveillance to facilitate the planning of intervention strategies and actions for combating COVID-19 pandemic in real-time. This included the setting up of a comprehensive sewerage network-based SARS-CoV-2 virus surveillance programme with 154 stationary sites covering 6 million people (or 80 % of the total population), and employing an intensive monitoring programme to take samples from each stationary site every 2 days. From 1 January to 22 May 2022, the daily confirmed case count started with 17 cases per day on 1 January to a maximum of 76,991 cases on 3 March and dropped to 237 cases on 22 May. During this period, a total of 270 "Restriction-Testing Declaration" (RTD) operations at high-risk residential areas were conducted based on the sewage virus testing results, where over 26,500 confirmed cases were detected with a majority being asymptomatic. In addition, Compulsory Testing Notices (CTN) were issued to residents, and the distribution of Rapid Antigen Test kits was adopted as alternatives to RTD operations in areas of moderate risk. These measures formulated a tiered and cost-effective approach to combat the disease in the local setting. Some ongoing and future enhancement efforts to improve efficacy are discussed from the perspective of wastewater-based epidemiology. Forecast models on case counts based on sewage virus testing results were also developed with R² of 0.9669-0.9775, which estimated that up to 22 May 2022, around 2,000,000 people (~67 % higher than the total number of 1,200,000 reported to the health authority, due to various constraints or limitations) had potentially contracted the disease, which is believed to be reflecting the real situation occurring in a highly urbanized metropolis like Hong Kong.

Enabling accurate and early 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 variant of concerns (VoCs) in communities. In this paper we present QuaID, a novel bioinformatics tool for VoC detection based on quasi-unique mutations. The benefits of QuaID are three-fold: (i) provides up to 3-week earlier VoC detection, (ii) accurate VoC detection (>95% precision on simulated benchmarks), and (iii) leverages all mutational signatures (including insertions & deletions).

High-resolution characterization of short-term temporal variability in the taxonomic and resistome composition of wastewater influent

Wastewater-based epidemiology (WBE) for population-level surveillance of antimicrobial resistance (AMR) is gaining significant traction, but the impact of wastewater sampling methods on results is unclear. In this study, we characterized taxonomic and resistome differences between single-timepoint-grab and 24 h composites of wastewater influent from a large UK-based wastewater treatment work [WWTW (population equivalent: 223 435)]. We autosampled hourly influent grab samples (n=72) over three consecutive weekdays, and prepared additional 24 h composites (n=3) from respective grabs. For taxonomic profiling, metagenomic DNA was extracted from all samples and 16S rRNA gene sequencing was performed. One composite and six grabs from day 1 underwent metagenomic sequencing for metagenomic dissimilarity estimation and resistome profiling. Taxonomic abundances of phyla varied significantly across hourly grab samples but followed a repeating diurnal pattern for all 3 days. Hierarchical clustering grouped grab samples into four time periods dissimilar in both 16S rRNA gene-based profiles and metagenomic distances. 24H-composites resembled mean daily phyla abundances and showed low variability of taxonomic profiles. Of the 122 AMR gene families (AGFs) identified across all day 1 samples, single grab samples identified a median of six (IQR: 5-8) AGFs not seen in the composite. However, 36/36 of these hits were at lateral coverage <0.5 (median: 0.19; interquartile range: 0.16-0.22) and potential false positives. Conversely, the 24H-composite identified three AGFs not seen in any grab with higher lateral coverage (0.82; 0.55-0.84). Additionally, several clinically significant human AGFs (bla _VIM, bla _IMP, bla _KPC) were intermittently or completely missed by grab sampling but captured by the 24 h composite. Wastewater influent undergoes significant taxonomic and resistome changes on short timescales potentially affecting interpretation of results based on sampling strategy. Grab samples are more convenient and potentially capture low-prevalence/transient targets but are less comprehensive and temporally variable. Therefore, we recommend 24H-composite sampling where feasible. Further validation and optimization of WBE methods is vital for its development into a robust AMR surveillance approach.

A multistate assessment of population normalization factors for wastewater-based epidemiology of COVID-19

Wastewater-based epidemiology (WBE) has become a valuable tool for monitoring SARS-CoV-2 infection trends throughout the COVID-19 pandemic. Population biomarkers that measure the relative human fecal contribution to normalize SARS-CoV-2 wastewater concentrations are needed for improved analysis and interpretation of community infection trends. The Centers for Disease Control and Prevention National Wastewater Surveillance System (CDC NWSS) recommends using the wastewater flow rate or human fecal indicators as population normalization factors. However, there is no consensus on which normalization factor performs best. In this study, we provided the first multistate assessment of the effects of flow rate and human fecal indicators (crAssphage, F+ Coliphage, and PMMoV) on the correlation of SARS-CoV-2 wastewater concentrations and COVID-19 cases using the CDC NWSS dataset of 182 communities across six U.S. states. Flow normalized SARS-CoV-2 wastewater concentrations produced the strongest correlation with COVID-19 cases. The correlation from the three human fecal indicators were significantly lower than flow rate. Additionally, using reverse transcription droplet digital polymerase chain reaction (RT-ddPCR) significantly improved correlation values over samples that were analyzed with real-time reverse transcription quantitative polymerase chain reaction (rRT-qPCR). Our assessment shows that utilizing flow normalization with RT-ddPCR generate the strongest correlation between SARS-CoV-2 wastewater concentrations and COVID-19 cases.

It is time to acknowledge coronavirus transmission via frozen and chilled foods: Undeniable evidence from China and lessons for the world

Since the broke out of the novel coronavirus disease at the end of 2019, nearly 650 million people have been infected around the globe, and >6.6 million have died from this disease. The first wave of infections in mainland China had been effectively controlled within a short period, with no domestic cases of infection for 56 consecutive days from April 16, 2020. Nonetheless, the re-emergence of several outbreaks in multiple Chinese cities posed a new challenge for public health authorities after new cases of infections were found in Xinfadi Market in Beijing on June 11, 2020. In the following series of re-emergent outbreaks, findings from epidemiological investigations suggested that more than twenty re-emergent outbreaks were caused by fomite transmission, predominantly via imported frozen and chilled foods contaminated by the SARS-CoV-2 virus. Seven of the eleven incidents involving frozen and chilled foods were identified by screening individuals with occupational exposure to imported cold-chain foods and associated individuals. Evidence showed that low temperatures and poor ventilation typically maintained through cold-chain logistics create amenable environments for the survival of SARS-CoV-2, making transnational cold chain logistics a congenial vehicle to spread the virus through global transport of consumer goods. To address this gap, here we present a scrutiny of the findings from epidemiological investigations in recent re-emergent outbreaks in China caused by fomite transmission via imported foods and goods. A national regime of traceable cold-chain foods and reinforced customs inspection protocols were established by public health authorities in mainland China as emergency responses to recurring outbreaks from fomite transmission via imported goods. We urge that more attention needs to be given to this specific route of pathogenic transmission to ensure biosecurity and to increase the preparedness for epidemic or pandemic scenarios by the global food industry and logistics carriers.