Wastewater-based prediction of COVID-19 cases using a highly sensitive SARS-CoV-2 RNA detection method combined with mathematical modeling

Enabling quantitative comparison of wastewater surveillance data across methods through data standardization without method standardization

Wastewater surveillance for COVID-19 and other pathogens has expanded globally. Rapid development and availability of various assays has facilitated swift adoption of wastewater surveillance in localities with diverse requirements. However, it presents challenges in comparing data due to methodological variations. Using surrogates for recovery control to address quantification biases has limitations as the recovery of surrogates and target pathogens often diverges significantly. Using non-spiked field-obtained wastewater samples as reference samples in an inter-lab study, this article proposes a straightforward, inexpensive, and most representative way of measuring relative quantification biases that occurs in analyzing field wastewater samples. Five labs participated in the study, testing five types of assays, resulting in a total of seven methods of lab-assay combinations. Each method quantified the concentration of SARS-CoV-2 and pepper mild mottle virus (PMMoV) RNAs in two types of reference samples. The results showed significant variations in quantification among methods, but the relative quantification biases were consistent across reference samples. This suggests that relative quantification biases measured with the reference samples are contingent on methods rather than wastewater samples, and that the once-determined method-specific factors can be used to correct for quantification biases in routine wastewater surveillance results. Subsequent data standardization was performed on year-long observational data from seven cities, serving as a preliminary validation of the proposed approach. This process demonstrated the potential for quantitative data comparison through the bias correction factors obtained in this inter-lab study.

Evaluating survey techniques in wastewater-based epidemiology for accurate COVID-19 incidence estimation

Wastewater-based epidemiology (WBE) requires high-quality survey methods to determine the incidence of infections in wastewater catchment areas. In this study, the wastewater survey methods necessary for comprehending the incidence of infection by WBE are clarified. This clarification is based on the correlation with the number of confirmed coronavirus disease 2019 (COVID-19) cases, considering factors such as handling non-detect data, calculation method for representative values, analytical sensitivity, analytical reproducibility, sampling frequency, and survey duration. Data collected from 15 samples per week for two and a half years using a highly accurate analysis method were regarded as gold standard data, and the correlation between severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA concentrations in wastewater and confirmed COVID-19 cases was analyzed by Monte Carlo simulation under the hypothetical situation where the quality of the wastewater survey method was reduced. Regarding data handling, it was appropriate to replace non-detect data with estimates based on distribution, and to use geometric means to calculate representative values. For the analysis of SARS-CoV-2 RNA in samples, using a highly sensitive and reproducible method (non-detect rates of <40 %; ≤0.4 standard deviation) and surveying at least three samples, preferably five samples, per week were considered desirable. Furthermore, conducting the survey over a period of time that included at least 50 weeks was necessary. A WBE that meets these survey criteria is sufficient for the determination of the COVID-19 infection incidence in the catchment. Furthermore, WBE can offer additional insights into infection rates in the catchment, such as the estimated 48 % decrease in confirmed COVID-19 cases visiting a clinic following a COVID-19 legal reclassification in Japan.

A framework for integrating wastewater-based epidemiology and public health

Wastewater-based epidemiology (WBE) is an environmental approach to monitor community health through the analysis of sewage. The COVID-19 pandemic catalyzed scientists and public health professionals to revisit WBE as a tool to optimize resource allocation to mitigate disease spread and prevent outbreaks. Some studies have highlighted the value of WBE programs that coordinate with public health professionals; however, the details necessary for implementation are not well-characterized. To respond to this knowledge gap, this article documents the framework of a successful WBE program in Arizona, titled Wastewater Analysis for Tactical Epidemiological Response Systems (WATERS), detailing the developed structure and methods of communication that enabled public health preparedness and response actions. This communication illustrates how program operations were employed to reduce outbreak severity. The structure outlined here is customizable and may guide other programs in the implementation of WBE as a public health tool.

Detection of mpox virus in wastewater provides forewarning of clinical cases in Canadian cities

Wastewater-based surveillance (WBS) has shown to be an effective tool in monitoring the spread of SARS-CoV-2 and has helped guide public health actions. Consequently, WBS has expanded to now include the monitoring of mpox virus (MPXV) to contribute to its mitigation efforts. In this study, we demonstrate a unique sample processing and a molecular diagnostic strategy for MPXV detection that can inform on the epidemiological situation of mpox outbreaks through WBS. We conducted WBS for MPXV in 22 Canadian wastewater treatment plants (WWTPs) for 14 weeks. Three MPXV qPCR assays were assessed in this study for the detection of MPXV which include the G2R assays (G2R_WA and G2R_G) developed by the Centers for Disease Control and Prevention (CDC) in 2010, and an in-house-developed assay that we have termed G2R_NML. The G2R_NML assay was designed using reference genomes from the 2022 MPXV outbreak and provides a larger qPCR amplicon size to facilitate Sanger sequencing. Results show that all three assays have similar limits of detection and are able to detect the presence of MPXV in wastewater. The G2R_NML assay produced a significantly greater number of Sanger sequence-confirmed MPXV results compared to the CDC G2R assays. Detection of MPXV was possible where provincial surveillance indicated overall low caseloads, and in some sites forewarning of up to several weeks was observed. Overall, this study proposes that WBS of MPXV provides additional information to help fill knowledge gaps in clinical case-surveillance and is potentially an essential component to the management of mpox.

SARS-CoV-2 detection in pediatric dental clinic wastewater reflects the number of local COVID-19 cases in children under 10 years old

This was the first longitudinal study to analyze dental clinic wastewater to estimate asymptomatic SARS-CoV-2 infection trends in children. We monitored wastewater over a 14-month period, spanning three major COVID-19 waves driven by the Alpha, Delta, and Omicron variants. Each Saturday, wastewater was sampled at the Pediatric Dental Clinic of the only dental hospital in Japan's Saitama Prefecture. The relationship between the weekly number of cases in Saitama Prefecture among residents aged < 10 years (exposure) and wastewater SARS-CoV-2 RNA detection (outcome) was examined. The number of cases was significantly associated with wastewater SARS-CoV-2 RNA positivity (risk ratio, 5.36; 95% confidence interval, 1.72-16.67; Fisher's exact test, p = 0.0005). A sample from Week 8 of 2022 harbored the Omicron variant. Compared to sporadic individual testing, this approach allows continuous population-level surveillance, which is less affected by healthcare seeking and test availability. Since wastewater from pediatric dental clinics originates from the oral cavities of asymptomatic children, such testing can provide important information regarding asymptomatic COVID-19 in children, complementing clinical pediatric data.

Correspondence of SARS-CoV-2 genomic sequences obtained from wastewater samples and COVID-19 patient at long-term care facilities

Wastewater-based epidemiology (WBE) has been in the spotlight because of applicability of early detection of virus outbreak and new variants at the catchment area. However, there has been a notable absence of research directly confirming the association between SARS-CoV-2 in wastewater and patient specimens. In this study, we performed a quantitative and qualitative investigation with a genetic-level comparison of SARS-CoV-2 between COVID-19 patients and SARS-CoV-2 positive wastewater samples at long-term care facilities. Wastewater samples were collected via passive sampling from manholes, and SARS-CoV-2 load in wastewater was determined by qPCR. We performed correlation analysis between SARS-CoV-2 load and COVID-19 case number, which suggested that SARS-CoV-2 was detected from wastewater earlier than ascertainment of COVID-19 case. Six and six RNA samples from COVID-19 positive cases and wastewater, respectively, from two facilities were then applied for amplicon sequencing analysis. Mutation analysis revealed high sequence similarity of SARS-CoV-2 variants between wastewater and patient samples (>99 %). To the best of our knowledge, this is the first study demonstrating that WBE is also effective in predicting predominant SARS-CoV-2 variant at facility-level, which is helpful to develop early-warning system for outbreak occurrence with predominant variant.

Real-Time On-Site Monitoring of Viruses in Wastewater Using Nanotrap® Particles and RICCA Technologies

Wastewater-based epidemiology (WBE) is an effective and efficient tool for the early detection of infectious disease outbreaks in a community. However, currently available methods are laborious, costly, and time-consuming due to the low concentration of viruses and the presence of matrix chemicals in wastewater that may interfere with molecular analyses. In the present study, we designed a highly sensitive "Quick Poop (wastewater with fecal waste) Sensor" (termed, QPsor) using a joint approach of Nanotrap microbiome particles and RICCA (RNA Isothermal Co-Assisted and Coupled Amplification). Using QPsor, the WBE study showed a strong correlation with standard PEG concentrations and the qPCR technique. Using a closed format for a paper-based lateral flow assay, we were able to demonstrate the potential of our assay as a real-time, point-of-care test by detecting the heat-inactivated SARS-CoV-2 virus in wastewater at concentrations of 100 copies/mL and within one hour. As a proof-of-concept demonstration, we analyzed the presence of viral RNA of the SARS-CoV-2 virus and PMMoV in raw wastewater samples from wastewater treatment plants on-site and within 60 min. The results show that the QPsor method can be an effective tool for disease outbreak detection by combining an AI-enabled case detection model with real-time on-site viral RNA extraction and amplification, especially in the absence of intensive clinical laboratory facilities. The lab-free, lab-quality test capabilities of QPsor for viral prevalence and transmission in the community can contribute to the efficient management of pandemic situations.

Targeted amplicon sequencing facilitated a novel risk assessment framework for assessing the prevalence of broad spectrum bacterial and coronavirus diseases

How to effectively leverage wastewater data to estimate the risk of various infectious diseases remains a great challenge. To address this issue, we conducted continuous wastewater surveillance in Dalian city during the summer-autumn seasons of 2022, targeting coronavirus and bacterial diseases. The surveillance included daily sampling at a wastewater treatment plant (WWTP) and weekly sampling in three sewersheds. Targeting the bacteria's 16S rRNA gene and the coronavirus's RNA-dependent RNA polymerase (RdRp) gene, we first employed RT-PCR and amplicon sequencing techniques to analyze the presence and phylogenetic relationship of detected coronavirus and bacterial pathogens. Next, qPCR was used to quantify the abundances of detected coronavirus and bacterial species. Based on the daily shedding dynamics of SARS-CoV-2, a novel model was developed to predict daily new cases. Based on the medium shedding density of 12 pathogens, two thresholds of sewage pathogen load (indicating 0.1 % and 1 % infection rates) were proposed. Our PanCoV RT-PCR detected coronavirus on 12th August and from 26th August to 12th September 2022. Targeted amplicon sequencing further identified human coronavirus OC43 (hCoV-OC43) on 12th August and the SARS-CoV-2 Omicron variant since 26th August in samples from WWTPs and sewersheds. Phylogenetic analysis revealed that hCoV-OC43 from this study belonged to genotype K and suggested a close relationship between the amplified coronavirus sequences from wastewater and clinical samples in a local COVID-19 outbreak on 26th August. Amplicon sequencing targeting the bacterial 16S rRNA gene also revealed the presence of several bacterial pathogens. Finally, we assessed the microbial risk of specific pathogens in sewersheds and identified a number of pathogens that reached high (>1 % prevalence) and medium risk levels (>0.1 % prevalence) at sewershed B. Our findings underline wastewater surveillance as a valuable early warning system for coronavirus and other waterborne bacterial diseases, complementing public health response measures.

Confined Space Nanoarchitectonics for Dynamic Functions and Molecular Machines

Nanotechnology has advanced the techniques for elucidating phenomena at the atomic, molecular, and nano-level. As a post nanotechnology concept, nanoarchitectonics has emerged to create functional materials from unit structures. Consider the material function when nanoarchitectonics enables the design of materials whose internal structure is controlled at the nanometer level. Material function is determined by two elements. These are the functional unit that forms the core of the function and the environment (matrix) that surrounds it. This review paper discusses the nanoarchitectonics of confined space, which is a field for controlling functional materials and molecular machines. The first few sections introduce some of the various dynamic functions in confined spaces, considering molecular space, materials space, and biospace. In the latter two sections, examples of research on the behavior of molecular machines, such as molecular motors, in confined spaces are discussed. In particular, surface space and internal nanospace are taken up as typical examples of confined space. What these examples show is that not only the central functional unit, but also the surrounding spatial configuration is necessary for higher functional expression. Nanoarchitectonics will play important roles in the architecture of such a total system.

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.

Tracking community infection dynamics of COVID-19 by monitoring SARS-CoV-2 RNA in wastewater, counting positive reactions by qPCR

Wastewater-based epidemiology has proved useful for monitoring the COVID-19 infection dynamics in communities. However, in regions of low prevalence, low concentrations of SARS-CoV-2 RNA in wastewater make this difficult. Here, we used real-time reverse-transcription PCR (RT-qPCR) to monitor SARS-CoV-2 RNA in wastewater from October 2020 to December 2022 during the third, fourth, fifth, sixth, seventh, and eighth waves of the COVID-19 outbreak in Japan. Viral RNA was below the limit of detection in all samples during the third and fourth waves. However, by counting the number of positive replicates in qPCR of each sample, we found that the positive ratio to all replicates in wastewater was significantly correlated with the number of clinically confirmed cases by the date of symptom onset during the third, fourth, and fifth waves. Time-step analysis indicated that, for 2 days either side of symptom onset, COVID-19 patients excreted in their feces large amounts of virus that wastewater surveillance could detect. We also demonstrated that the viral genome copy number in wastewater, as estimated from the positive ratio of SARSA-CoV-2 RNA, was correlated with the number of clinically confirmed cases. The positive count method is thus useful for tracing COVID-19 dynamics in regions of low prevalence.

The growing need to establish a global wastewater surveillance consortium for future pandemic preparedness

Recognizing the risk of pandemic and the importance of monitoring and data sharing, we highlight the importance of establishing a global wastewater surveillance consortium, particularly under the umbrella of an international organization such as WHO, to strengthen future pandemic preparedness.

Association of wastewater SARS-CoV-2 load with confirmed COVID-19 cases at a university hospital in Sapporo, Japan during the period from February 2021 to February 2023

Wastewater surveillance for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been used to monitor trends in SARS-CoV-2 prevalence in a community without being influenced by clinical testing resources or healthcare-seeking behaviors. Since the rate of mortality from COVID-19 is higher in elderly patients with comorbidities, it is important to protect hospitalized patients from nosocomial infections caused by SARS-CoV-2. SARS-CoV-2 dissemination within a hospital ward was mostly mediated by healthcare workers (HCWs) and patients. HCWs need to understand the occurrence of COVID-19 and reflect this in their infection control measures. The aim of the present study was to determine the potential of SARS-CoV-2 RNA in wastewater as a leading indicator of confirmed COVID-19 cases at a university hospital. The trend of the geometric mean RNA concentrations in wastewater collected in Sapporo corresponded well with that of the number of newly confirmed COVID-19 cases at Hokkaido University Hospital between February 15, 2021 and February 26, 2023 (Pearson's r = 0.8823, p < 0.0001). Our results showed that monitoring SARS-CoV-2 RNA in municipal wastewater was useful for estimating the number of COVID-19 patients in healthcare facilities in the city.

Long-term SARS-CoV-2 surveillance in wastewater and estimation of COVID-19 cases: An application of wastewater-based epidemiology

The role of wastewater-based epidemiology (WBE), a powerful tool to complement clinical surveillance, has increased as many grassroots-level facilities, such as municipalities and cities, are actively involved in wastewater monitoring, and the clinical testing of coronavirus disease 2019 (COVID-19) is downscaled widely. This study aimed to conduct long-term wastewater surveillance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Yamanashi Prefecture, Japan, using one-step reverse transcription-quantitative polymerase chain reaction (RT-qPCR) assay and estimate COVID-19 cases using a cubic regression model that is simple to implement. Influent wastewater samples (n = 132) from a wastewater treatment plant were collected normally once weekly between September 2020 and January 2022 and twice weekly between February and August 2022. Viruses in wastewater samples (40 mL) were concentrated by the polyethylene glycol precipitation method, followed by RNA extraction and RT-qPCR. The K-6-fold cross-validation method was used to select the appropriate data type (SARS-CoV-2 RNA concentration and COVID-19 cases) suitable for the final model run. SARS-CoV-2 RNA was successfully detected in 67 % (88 of 132) of the samples tested during the whole surveillance period, 37 % (24 of 65) and 96 % (64 of 67) of the samples collected before and during 2022, respectively, with concentrations ranging from 3.5 to 6.3 log10 copies/L. This study applied a nonnormalized SARS-CoV-2 RNA concentration and nonstandardized data for running the final 14-day (1 to 14 days) offset models to estimate weekly average COVID-19 cases. Comparing the parameters used for a model evaluation, the best model showed that COVID-19 cases lagged 3 days behind the SARS-CoV-2 RNA concentration in wastewater samples during the Omicron variant phase (year 2022). Finally, 3- and 7-day offset models successfully predicted the trend of COVID-19 cases from September 2022 until February 2023, indicating the applicability of WBE as an early warning tool.

Correlation between wastewater and COVID-19 case incidence rates in major California sewersheds across three variant periods

Monitoring for COVID-19 through wastewater has been used for adjunctive public health surveillance, with SARS-CoV-2 viral concentrations in wastewater correlating with incident cases in the same sewershed. However, the generalizability of these findings across sewersheds, laboratory methods, and time periods with changing variants and underlying population immunity has not been well described. The California Department of Public Health partnered with six wastewater treatment plants starting in January 2021 to monitor wastewater for SARS-CoV-2, with analyses performed at four laboratories. Using reported PCR-confirmed COVID-19 cases within each sewershed, the relationship between case incidence rates and wastewater concentrations collected over 14 months was evaluated using Spearman's correlation and linear regression. Strong correlations were observed when wastewater concentrations and incidence rates were averaged (10- and 7-day moving window for wastewater and cases, respectively, ρ = 0.73-0.98 for N1 gene target). Correlations remained strong across three time periods with distinct circulating variants and vaccination rates (winter 2020-2021/Alpha, summer 2021/Delta, and winter 2021-2022/Omicron). Linear regression revealed that slopes of associations varied by the dominant variant of concern, sewershed, and laboratory (β = 0.45-1.94). These findings support wastewater surveillance as an adjunctive public health tool to monitor SARS-CoV-2 community trends.

Making waves: Integrating wastewater surveillance with dynamic modeling to track and predict viral outbreaks

Wastewater surveillance has proved to be a valuable tool to track the COVID-19 pandemic. However, most studies using wastewater surveillance data revolve around establishing correlations and lead time relative to reported case data. In this perspective, we advocate for the integration of wastewater surveillance data with dynamic within-host and between-host models to better understand, monitor, and predict viral disease outbreaks. Dynamic models overcome emblematic difficulties of using wastewater surveillance data such as establishing the temporal viral shedding profile. Complementarily, wastewater surveillance data bypasses the issues of time lag and underreporting in clinical case report data, thus enhancing the utility and applicability of dynamic models. The integration of wastewater surveillance data with dynamic models can enhance real-time tracking and prevalence estimation, forecast viral transmission and intervention effectiveness, and most importantly, provide a mechanistic understanding of infectious disease dynamics and the driving factors. Dynamic modeling of wastewater surveillance data will advance the development of a predictive and responsive monitoring system to improve pandemic preparedness and population health.

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

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

Omicron COVID-19 Case Estimates Based on Previous SARS-CoV-2 Wastewater Load, Regional Municipality of Peel, Ontario, Canada

We determined correlations between SARS-CoV-2 load in untreated water and COVID-19 cases and patient hospitalizations before the Omicron variant (September 2020-November 2021) at 2 wastewater treatment plants in the Regional Municipality of Peel, Ontario, Canada. Using pre-Omicron correlations, we estimated incident COVID-19 cases during Omicron outbreaks (November 2021-June 2022). The strongest correlation between wastewater SARS-CoV-2 load and COVID-19 cases occurred 1 day after sampling (r = 0.911). The strongest correlation between wastewater load and COVID-19 patient hospitalizations occurred 4 days after sampling (r = 0.819). At the peak of the Omicron BA.2 outbreak in April 2022, reported COVID-19 cases were underestimated 19-fold because of changes in clinical testing. Wastewater data provided information for local decision-making and are a useful component of COVID-19 surveillance systems.

Economic Evaluation of Wastewater Surveillance Combined with Clinical COVID-19 Screening Tests, Japan

The COVID-19 pandemic has imposed substantial burdens on the global society. To find an optimal combination of wastewater surveillance and clinical testing for tracking COVID-19, we evaluated the economic efficiency of hypothetical screening options at a single facility in Japan. To conduct cost-benefit analyses, we developed standard decision models in which we assumed model parameters from literature and primary data, such as screening policies used at the Tokyo Olympic and Paralympic Village in 2021. We compared hypothetical 2-step screening options that used clinical PCR to diagnose COVID-19 after a positive result from primary screening using antigen tests (option 1) or wastewater surveillance (option 2). Our simulation results indicated that option 2 likely would be economically more justifiable than option 1, particularly at lower incidence levels. Our findings could help justify and promote the use of wastewater surveillance as a primary screening at a facility level for COVID-19 and other infectious diseases.

Influence of membrane pore-size on the recovery of endogenous viruses from wastewater using an adsorption-extraction method

The ongoing COVID-19 pandemic has emphasized the significance of wastewater surveillance in monitoring and tracking the spread of infectious diseases, including SARS-CoV-2. The wastewater surveillance approach detects genetic fragments from viruses in wastewater, which could provide an early warning of outbreaks in communities. In this study, we determined the concentrations of four types of endogenous viruses, including non-enveloped DNA (crAssphage and human adenovirus 40/41), non-enveloped RNA (enterovirus), and enveloped RNA (SARS-CoV-2) viruses, from wastewater samples using the adsorption-extraction (AE) method with electronegative HA membranes of different pore sizes (0.22, 0.45, and 0.80 µm). Our findings showed that the membrane with a pore size of 0.80 µm performed comparably to the membrane with a pore size of 0.45 µm for virus detection/quantitation (repeated measurement one-way ANOVA; p > 0.05). We also determined the recovery efficiencies of indigenous crAssphage and pepper mild mottle virus, which showed recovery efficiencies ranging from 50% to 94% and from 20% to 62%, respectively. Our results suggest that the use of larger pore size membranes may be beneficial for processing larger sample volumes, particularly for environmental waters containing low concentrations of viruses. This study offers valuable insights into the application of the AE method for virus recovery from wastewater, which is essential for monitoring and tracking infectious diseases in communities.

Impact of the COVID-19 pandemic on the prevalence of influenza A and respiratory syncytial viruses elucidated by wastewater-based epidemiology

Since the COVID-19 pandemic, a decrease in the prevalence of Influenza A virus (IAV) and respiratory syncytial virus (RSV) has been suggested by clinical surveillance. However, there may be potential biases in obtaining an accurate overview of infectious diseases in a community. To elucidate the impact of the COVID-19 on the prevalence of IAV and RSV, we quantified IAV and RSV RNA in wastewater collected from three wastewater treatment plants (WWTPs) in Sapporo, Japan, between October 2018 and January 2023, using highly sensitive EPISENS™ method. From October 2018 to April 2020, the IAV M gene concentrations were positively correlated with the confirmed cases in the corresponding area (Spearman's r = 0.61). Subtype-specific HA genes of IAV were also detected, and their concentrations showed trends that were consistent with clinically reported cases. RSV A and B serotypes were also detected in wastewater, and their concentrations were positively correlated with the confirmed clinical cases (Spearman's r = 0.36-0.52). The detection ratios of IAV and RSV in wastewater decreased from 66.7 % (22/33) and 42.4 % (14/33) to 4.56 % (12/263) and 32.7 % (86/263), respectively in the city after the COVID-19 prevalence. The present study demonstrates the potential usefulness of wastewater-based epidemiology combined with the preservation of wastewater (wastewater banking) as a tool for better management of respiratory viral diseases.

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

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