Application of neighborhood-scale wastewater-based epidemiology in low COVID-19 incidence situations

Association between confirmed COVID-19 cases at hospitals and SARS-CoV-2 levels in municipal wastewater during the pandemic and endemic phases

COVID-19 is now considered endemic in many countries. On May 8, 2023, Japan reclassified COVID-19 from a pandemic to an endemic status, shifting surveillance from universal to sentinel reporting and transitioning the testing and treatment cost of COVID-19 from public funding to individual health insurance coverage. Restrictions on movement, events, and business hours were lifted, potentially increasing cases and complicating tracking. Monitoring hospital cases remains essential to protect high-risk inpatients from nosocomial infections. In this study, 13,812 COVID-19 cases in 12 hospitals were analyzed and the results revealed a strong correlation between SARS-CoV-2 levels in municipal wastewater and weekly new cases during both the pandemic period (February 15, 2021 - February 26, 2023; Pearson's r = 0.8321) and the endemic period (May 8, 2023 - October 1, 2023; Pearson's r = 0.7501). SARS-CoV-2 RNA levels in wastewater from municipal catchment areas showed a stronger correlation with the number of COVID-19 cases at hospitals than did RNA levels in wastewater from the catchment area where the hospitals are located. The difference in correlations was more pronounced during the endemic period. During the endemic period, measurements of SARS-CoV-2 RNA levels in samples obtained from larger sewersheds may be more effective in capturing the overall trends of COVID-19 cases in a region. In other words, during the endemic period, municipal wastewater surveillance may reflect the number of COVID-19 cases in hospitals. Even for facilities that do not monitor SARS-CoV-2 in their own hospital wastewater, publicly available municipal wastewater data can be used to estimate the number of COVID-19 cases in hospitals. Furthermore, COVID-19 infection control measures within hospitals can be evaluated by comparing the number of nosocomial infection patients based on the concentration of SARS-CoV-2 in municipal wastewater.

Efficient concentration of viral nucleic acid in wastewater through surfactant releasing and a two-step magnetic bead extraction and purification

Wastewater-based epidemiology (WBE) is a valuable complement to clinical monitoring, allowing for effective surveillance of viral infections in populations, and tracking the presence and the epidemiological dynamics of various infectious pathogens in communities. However, virus loads are usually low-abundant in wastewater, and current virus concentration methods for WBE are laborious and time-consuming with low recovery efficiency. To address these challenges, we have developed a magnetic bead-based semi-automated method involving extraction and purification to directly concentrate viral nucleic acids from sewage within 55 min. Prior to concentration, 0.5 % LDS was introduced to pretreat wastewater to inactivate viruses and release viral nucleic acids from both liquid and solid fractions to improve recovery. Under optimal conditions, the concentration method combined with reverse transcription-quantitative polymerase chain reaction (RT-qPCR) can detect severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA added exogenously in wastewater as low as 4.9 copies/mL within 2.5 h, with an average recovery rate exceeding 80 %. Testing real sewages proved the applicability of the method to detect multiple viruses in different sewages. Additionally, variants of SARS-CoV-2 were successfully identified by multiplex amplicon sequencing in two samples. In conclusion, the new method could provide a much more efficient way for WBE of pathogenic viruses in various sewages.

Unveiling the silent information of wastewater-based epidemiology of SARS-CoV-2 at community and sanitary zone levels: experience in Córdoba City, Argentina

The emergence of COVID-19 in 2020 significantly enhanced the application of wastewater monitoring for detecting SARS-CoV-2 circulation within communities. From October 2021 to October 2022, we collected 406 wastewater samples weekly from the Córdoba Central Pipeline Network (BG-WWTP) and six specific sewer manholes from sanitary zones (SZs). Following WHO guidelines, we processed samples and detected SARS-CoV-2 RNA and variants using real-time PCR. Monitoring at the SZ level allowed for the development of a viral activity flow map, pinpointing key areas of SARS-CoV-2 circulation and tracking its temporal spread and variant evolution. Our findings demonstrate that wastewater-based surveillance acts as a sensitive indicator of viral activity, detecting imminent increases in COVID-19 cases before they become evident in clinical data. This study highlights the effectiveness of targeted wastewater monitoring at both municipal and SZ levels in identifying viral hotspots and assessing community-wide circulation. Importantly, the data shows that environmental wastewater studies provide valuable insights into virus presence, independent of clinical COVID-19 case records, and offer a robust tool for adapting to future public health challenges.

Investigating illicit drug hotspots and daily variations using sewer-network wastewater analysis

Previous wastewater-based epidemiology (WBE) research on illicit drug use has predominantly focused on wastewater treatment plant (WWTP) influents, but information on sewer-network wastewater is very limited. This study represents a pioneering small-scale WBE investigation based on the analysis of sewer-network wastewater samples from different sewer manholes in suburban (Tamsui region) and urban areas (Zhongshan and Wanhua regions) and a comparison of the results with those obtained from corresponding WWTP influents. Among sixteen illicit drugs, methamphetamine exhibited the highest concentration in sewer-network wastewater across both areas. Suburban-urban variations were observed, with more types of illicit drugs detected in the suburban area. Back-calculation indicated that methamphetamine and ketamine were the most-consumed illicit drugs in both sewer-network wastewaters and WWTP influents. Similar types of illicit drugs were detected in the sewer-network wastewaters and WWTP influents, indicating the representativeness of WWTP influents in assessing regional illicit drug abuse. Nevertheless, the sewer-network wastewater results offered additional information making it possible to pinpoint potential hotspots of illicit drug and identify peak usage periods throughout the day, in contrast to the WWTP influent results. In the non-suspected suburban area of Tamsui, high potential hotspots of methamphetamine (sampling points 3 and 6) and ketamine (sampling points 1 and 8) were identified. Although the Zhongshan and Wanhua regions were chosen as suspected hotspots of illicit drug abuse, more severe illicit drug use was observed in Wanhua. Moreover, a trend toward higher illicit drug use from early morning to morning was observed. Despite sampling challenges and higher costs, small-scale WBE via sewer-network wastewater analysis provides superior identification of drug abuse hotspots and peak usage periods. Therefore, this study provides valuable insights for law enforcement and can help prevent and combat illicit drug abuse by targeting potential hotspots and understanding daily illicit drug use dynamics.

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.

Testing methods to estimate population size for wastewater treatment plants using census data: Implications for wastewater-based epidemiology

In wastewater-based epidemiology (WBE), wastewater loads are commonly reported as a per capita value. Census population counts are often used to obtain a population size to normalise wastewater loads. However, the methods used to calculate the population size of wastewater treatment plants (WWTPs) from census data are rarely reported in the WBE literature. This is problematic because the geographical extents of wastewater catchments and census area units rarely align perfectly with each other and exist at different spatial scales. This complicates efforts to estimate the number of people serviced by WWTPs in these census area units. This study compared four geospatial methods to combine wastewater catchment areas and census area units to calculate the census population size of wastewater treatment plants. These methods were applied nationally to WWTPs across New Zealand. Population estimates varied by up to 73 % between the methods, which could skew comparisons of per capita wastewater loads between sites. Variability in population estimates (relative standard deviation, RSD) was significantly higher in smaller catchments (rs = -0.727, P < .001), highlighting the importance of method selection in smaller sites. Census population estimates were broadly similar to those provided by wastewater operators, but significant variation was observed for some sites (ranging from 42 % lower to 78 % higher, RSD = 262 %). We present a widely applicable method to calculate population size from census, which involves disaggregating census area units by individual properties. The results reinforce the need for transparent reporting to maintain confidence in the comparison of WBE across sites and studies.

Building Environmental and Sociological Predictive Intelligence to Understand the Seasonal Threat of SARS-CoV-2 in Human Populations

Current modeling practices for environmental and sociological modulated infectious diseases remain inadequate to forecast the risk of outbreak(s) in human populations, partly due to a lack of integration of disciplinary knowledge, limited availability of disease surveillance datasets, and overreliance on compartmental epidemiological modeling methods. Harvesting data knowledge from virus transmission (aerosols) and detection (wastewater) of SARS-CoV-2, a heuristic score-based environmental predictive intelligence system was developed that calculates the risk of COVID-19 in the human population. Seasonal validation of the algorithm was uniquely associated with wastewater surveillance of the virus, providing a lead time of 7-14 days before a county-level outbreak. Using county-scale disease prevalence data from the United States, the algorithm could predict COVID-19 risk with an overall accuracy ranging between 81% and 98%. Similarly, using wastewater surveillance data from Illinois and Maryland, the SARS-CoV-2 detection rate was greater than 80% for 75% of the locations during the same time the risk was predicted to be high. Results suggest the importance of a holistic approach across disciplinary boundaries that can potentially allow anticipatory decision-making policies of saving lives and maximizing the use of available capacity and resources.

Portable, single nucleotide polymorphism-specific duplex assay for virus surveillance in wastewater

The Argonaute protein from the archaeon Pyrococcus furiosus (PfAgo) is a DNA-guided nuclease that targets DNA with any sequence. We designed a virus detection assay in which the PfAgo enzyme cleaves the reporter probe, thus generating fluorescent signals when amplicons from a reverse transcriptase loop-mediated isothermal amplification (RT-LAMP) assay contain target sequences. We confirmed that the RT-LAMP-PfAgo assay for the SARS-CoV-2 Delta variant produced significantly higher fluorescent signals (p < 0.001) when a single nucleotide polymorphism (SNP), exclusive to the Delta variant, was present, compared to the samples without the SNP. Additionally, the duplex assay for Pepper mild mottle virus (PMMOV) and SARS-CoV-2 detection produced specific fluorescent signals (FAM or ROX) only when the corresponding sequences were present. Furthermore, the RT-LAMP-PfAgo assay does not require dilution to reduce the impact of environmental inhibitors. The limit of detection of the PMMOV assay, determined with 30 wastewater samples, was 28 gc/μL, with a 95 % confidence interval of [11,103]. Finally, using a point-of-use device, the RT-LAMP-PfAgo assay successfully detected PMMOV in wastewater samples. Based on our findings, we conclude that the RT-LAMP-PfAgo assay can be used as a portable, SNP-specific duplex assay, which will significantly improve virus surveillance in wastewater.

Improved performance of nucleic acid-based assays for genetically diverse norovirus surveillance

Nucleic acid-based assays, such as polymerase chain reaction (PCR), that amplify and detect organism-specific genome sequences are a standard method for infectious disease surveillance. However, challenges arise for virus surveillance because of their genetic diversity. Here, we calculated the variability of nucleotides within the genomes of 10 human viral species in silico and found that endemic viruses exhibit a high percentage of variable nucleotides (e.g., 51.4% for norovirus genogroup II). This genetic diversity led to the variable probability of detection of PCR assays (the proportion of viral sequences that contain the assay's target sequences divided by the total number of viral sequences). We then experimentally confirmed that the probability of the target sequence detection is indicative of the number of mismatches between PCR assays and norovirus genomes. Next, we developed a degenerate PCR assay that detects 97% of known norovirus genogroup II genome sequences and recognized norovirus in eight clinical samples. By contrast, previously developed assays with 31% and 16% probability of detection had 1.1 and 2.5 mismatches on average, respectively, which negatively impacted RNA quantification. In addition, the two PCR assays with a lower probability of detection also resulted in false negatives for wastewater-based epidemiology. Our findings suggest that the probability of detection serves as a simple metric for evaluating nucleic acid-based assays for genetically diverse virus surveillance.IMPORTANCENucleic acid-based assays, such as polymerase chain reaction (PCR), that amplify and detect organism-specific genome sequences are employed widely as a standard method for infectious disease surveillance. However, challenges arise for virus surveillance because of the rapid evolution and genetic variation of viruses. The study analyzed clinical and wastewater samples using multiple PCR assays and found significant performance variation among the PCR assays for genetically diverse norovirus surveillance. This finding suggests that some PCR assays may miss detecting certain virus strains, leading to a compromise in detection sensitivity. To address this issue, we propose a metric called the probability of detection, which can be simply calculated in silico using a code developed in this study, to evaluate nucleic acid-based assays for genetically diverse virus surveillance. This new approach can help improve the sensitivity and accuracy of virus detection, which is crucial for effective infectious disease surveillance and control.

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.

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.

Wastewater-Based Epidemiology of SARS-CoV-2: Assessing Prevalence and Correlation with Clinical Cases

Wastewater-based epidemiology has been recognized as a tool to monitor the progress of COVID-19 pandemic worldwide. The study presented herein aimed at quantitating the SARS-CoV-2 RNA in the wastewaters, predicting the number of infected individuals in the catchment areas, and correlating it with the clinically reported COVID-19 cases. Wastewater samples (n = 162) from different treatment stages were collected from three wastewater treatment plants (WWTPs) from Mumbai city during the 2nd surge of COVID-19 (April 2021 to June 2021). SARS-CoV-2 causing COVID-19, was detected in 76.2% and 4.8% of raw and secondary treated (n = 63 each) wastewater samples respectively while all tertiary treated samples (n = 36) were negative. The quantity of SARS-CoV-2 RNA determined as gene copies/100 mL varied among all the three WWTPs under study. The gene copy numbers thus obtained were further used to estimate the number of infected individuals within the population served by these WWTPs using two published methods. A positive correlation (p < 0.05) was observed between the estimated number of infected individuals and clinically confirmed COVID-19 cases reported during the sampling period in two WWTPs. Predicted infected individuals calculated in this study were 100 times higher than the reported COVID-19 cases in all the WWTPs assessed. The study findings demonstrated that the present wastewater treatment technologies at the three WWTPs studied were adequate to remove the virus. However, SARS-CoV-2 genome surveillance with emphasis on monitoring its variants should be implemented as a routine practice to prepare for any future surge in infections.

Deep learning techniques for detection and prediction of pandemic diseases: a systematic literature review

Deep learning (DL) is becoming a fast-growing field in the medical domain and it helps in the timely detection of any infectious disease (IDs) and is essential to the management of diseases and the prediction of future occurrences. Many scientists and scholars have implemented DL techniques for the detection and prediction of pandemics, IDs and other healthcare-related purposes, these outcomes are with various limitations and research gaps. For the purpose of achieving an accurate, efficient and less complicated DL-based system for the detection and prediction of pandemics, therefore, this study carried out a systematic literature review (SLR) on the detection and prediction of pandemics using DL techniques. The survey is anchored by four objectives and a state-of-the-art review of forty-five papers out of seven hundred and ninety papers retrieved from different scholarly databases was carried out in this study to analyze and evaluate the trend of DL techniques application areas in the detection and prediction of pandemics. This study used various tables and graphs to analyze the extracted related articles from various online scholarly repositories and the analysis showed that DL techniques have a good tool in pandemic detection and prediction. Scopus and Web of Science repositories are given attention in this current because they contain suitable scientific findings in the subject area. Finally, the state-of-the-art review presents forty-four (44) studies of various DL technique performances. The challenges identified from the literature include the low performance of the model due to computational complexities, improper labeling and the absence of a high-quality dataset among others. This survey suggests possible solutions such as the development of improved DL-based techniques or the reduction of the output layer of DL-based architecture for the detection and prediction of pandemic-prone diseases as future considerations.

Exploration on wastewater-based epidemiology of SARS-CoV-2: Mimic relative quantification with endogenous biomarkers as internal reference

Wastewater-based epidemiology has become a powerful surveillance tool for monitoring the pandemic of COVID-19. Although it is promising to quantitatively correlate the SARS-CoV-2 RNA concentration in wastewater with the incidence of community infection, there is still no consensus on whether the viral nucleic acid concentration in sewage should be normalized against the abundance of endogenous biomarkers and which biomarker should be used as a reference for the normalization. Here, several candidate endogenous reference biomarkers for normalization of SARS-CoV-2 signal in municipal sewage were evaluated. The human fecal indicator virus (crAssphage) is a promising candidate of endogenous reference biomarker for data normalization of both DNA and RNA viruses for its intrinsic viral nature and high and stable content in sewage. Without constructing standard curves, the relative quantification of sewage viral nucleic acid against the abundance of the reference biomarker can be used to correlate with community COVID-19 incidence, which was proved via mimic experiments by spiking pseudovirus of different concentrations in sewage samples. Dilution of pseudovirus-seeded wastewater did not affect the relative abundance of viral nucleic acid, demonstrating that relative quantification can overcome the sewage dilution effects caused by the greywater input, precipitation and/or groundwater infiltration. The process of concentration, recovery and detection of the endogenous biomarker was consistent with that of SARS-CoV-2 RNA. Thus, it is necessary to co-quantify the endogenous biomarker because it can be not only an internal reference for data normalization, but also a process control.

Optimization of sewage sampling for wastewater-based epidemiology through stochastic modeling

The proliferation of the SARS-CoV-2 global pandemic has brought to attention the need for epidemiological tools that can detect diseases in specific geographical areas through non-contact means. Such methods may protect those potentially infected by facilitating early quarantine policies to prevent the spread of the disease. Sampling of municipal wastewater has been studied as a plausible solution to detect pathogen spread, even from asymptomatic patients. However, many challenges exist in wastewater-based epidemiology such as identifying a representative sample for a population, determining the appropriate sample size, and establishing the right time and place for samples. In this work, a new approach to address these questions is assessed using stochastic modeling to represent wastewater sampling given a particular community of interest. Using estimates for various process parameters, inferences on the population infected are generated with Monte Carlo simulation output. A case study at the University of Oklahoma is examined to calibrate and evaluate the model output. Finally, extensions are provided for more efficient wastewater sampling campaigns in the future. This research provides greater insight into the effects of viral load, the percentage of the population infected, and sampling time on mean SARS-CoV-2 concentration through simulation. In doing so, an earlier warning of infection for a given population may be obtained and aid in reducing the spread of viruses.