Wastewater-Based Epidemiology for Community Monitoring of SARS-CoV-2: Progress and Challenges
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PROVIDER: S-EPMC8340581 | biostudies-literature | 2021 Jul
REPOSITORIES: biostudies-literature
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Project description:Raw municipal wastewater from five wastewater treatment plants representing the vast majority of the Qatar population was sampled between the third week of June 2020 and the end of August 2020, during the period of declining cases after the peak of the first wave of infection in May 2020. The N1 region of the SARS-CoV-2 genome was used to quantify the viral load in the wastewater using RT-qPCR. The trend in Ct values in the wastewater samples mirrored the number of new daily positive cases officially reported for the country, confirmed by RT-qPCR testing of naso-pharyngeal swabs. SARS-CoV-2 RNA was detected in 100% of the influent wastewater samples (7889 ± 1421 copy/L - 542,056 ± 25,775 copy/L, based on the N1 assay). A mathematical model for wastewater-based epidemiology was developed and used to estimate the number of people in the population infected with COVID-19 from the N1 Ct values in the wastewater samples. The estimated number of infected population on any given day using the wastewater-based epidemiology approach declined from 542,313 ± 51,159 to 31,181 ± 3081 over the course of the sampling period, which was significantly higher than the officially reported numbers. However, seroprevalence data from Qatar indicates that diagnosed infections represented only about 10% of actual cases. The model estimates were lower than the corrected numbers based on application of a static diagnosis ratio of 10% to the RT-qPCR identified cases, which is assumed to be due to the difficulty in quantifying RNA losses as a model term. However, these results indicate that the presented WBE modeling approach allows for a realistic assessment of incidence trend in a given population, with a more reliable estimation of the number of infected people at any given point in time than can be achieved using human biomonitoring alone.
| S-EPMC7870436 | biostudies-literature
Project description:Wastewater-based epidemiology (WBE) approach for COVID-19 surveillance is largely based on the assumption of SARS-CoV-2 RNA shedding into sewers by infected individuals. Recent studies found that SARS-CoV-2 RNA concentration in wastewater (CRNA) could not be accounted by the fecal shedding alone. This study aimed to determine potential major shedding sources based on literature data of CRNA, along with the COVID-19 prevalence in the catchment area through a systematic literature review. Theoretical CRNA under a certain prevalence was estimated using Monte Carlo simulations, with eight scenarios accommodating feces alone, and both feces and sputum as shedding sources. With feces alone, none of the WBE data was in the confidence interval of theoretical CRNA estimated with the mean feces shedding magnitude and probability, and 63% of CRNA in WBE reports were higher than the maximum theoretical concentration. With both sputum and feces, 91% of the WBE data were below the simulated maximum CRNA in wastewater. The inclusion of sputum as a major shedding source led to more comparable theoretical CRNA to the literature WBE data. Sputum discharging behavior of patients also resulted in great fluctuations of CRNA under a certain prevalence. Thus, sputum is a potential critical shedding source for COVID-19 WBE surveillance.
| S-EPMC8908579 | biostudies-literature
Project description:Since 2020, developed countries have rapidly shared both publicly and academically relevant wastewater surveillance information. Data on SARS-CoV-2 circulation is pivotal for guiding public health policies and improving the COVID-19 pandemic response. Conversely, low- and middle-income countries, such as Latin America and the Caribbean, showed timid activities in the Wastewater-Based Epidemiology (WBE) context. In these countries, isolated groups perform viral wastewater monitoring, and the data are unevenly shared or accessible to health agencies and the scientific community. This manuscript aims to highlight the relevance of a multiparty effort involving research, public health, and governmental agencies to support usage of WBE methodology to its full potential during the COVID-19 pandemic as part of a joint One Health surveillance approach. Thus, in this study, we explored the results obtained from wastewater surveillance in different regions of Brazil as a part of the COVID-19 Wastewater Monitoring Network ANA (National Water Agency), MCTI (Ministry of Science, Technology, and Innovations) and MS (Ministry of Health). Over the epidemiological weeks of 2021 and early 2022, viral RNA concentrations in wastewater followed epidemiological trends and variations. The highest viral loads in wastewater samples were detected during the second Brazilian wave of COVID-19. Corroborating international reports, our experience demonstrated usefulness of the WBE approach in viral surveillance. Wastewater surveillance allows hotspot identification, and therefore, early public health interventions. In addition, this methodology allows tracking of asymptomatic and oligosymptomatic individuals, who are generally underreported, especially in emerging countries with limited clinical testing capacity. Therefore, WBE undoubtedly contributes to improving public health responses in the context of this pandemic, as well as other sanitary emergencies.
| S-EPMC9295330 | biostudies-literature
Project description:Wastewater-based epidemiology (WBE) is expected to become a powerful tool to monitor the dissemination of SARS-CoV-2 at the community level, which has attracted the attention of scholars all over the world. However, there is not yet a standard protocol to guide its implementation. In this paper, we proposed a comprehensive technical and theoretical framework of relative quantification via qPCR for determining the virus abundance in wastewater and estimating the infection ratio in corresponding communities, which is expected to achieve horizontal and vertical comparability of the data using a human-specific biomarker as the internal reference. Critical factors affecting the virus detectability and the estimation of infection ratio include virus concentration methods, lag-period, per capita virus shedding amount, sewage generation rate, temperature-related decay kinetics of virus/biomarker in wastewater, and hydraulic retention time (HRT), etc. Theoretical simulation shows that the main factors affecting the detectability of virus in sewage are per capita virus shedding amount and sewage generation rate. While the decay of SARS-CoV-2 RNA in sewage is a relatively slow process, which may have limited impact on its detection. Under the ideal condition of high per capita virus shedding amount and low sewage generation rate, it is expected to detect a single infected person within 400,000 people.
| S-EPMC8195746 | biostudies-literature
Project description:Given its potential applications in confronting the COVID-19 pandemic, wastewater-based epidemiology (WBE) has attracted tremendous attention. Developing a fast, cost-effective, and practical method for SARS-CoV-2 detection in wastewater is of great significance to facilitate future WBE development. By now, the PCR-based approach serves as the reference method and “gold standard” to detect the virus in wastewater. However, we found a trend that the PCR-based method becomes almost an unshakable choice as more and more studies were published regarding SARS-CoV-2 WBE. Of note, the importance of exploring new, alternative approaches for SARS-CoV-2 detection in wastewater should not be underestimated. In this context, the prospect of using electrochemical immunosensors as the alternative detection method was investigated in this survey. Based on the previous efforts towards different virus immunoassay studies and newly published PCR-based COVD-19 WBE works, this survey provides new insights into the electrochemical immunoassay that have been widely adopted in body fluids virus detection, along with an extensive discussion of the detection mechanism, detection performance, past performances, current efforts, and potential challenges with wastewater detection. In the end, this survey concludes that using electrochemical immunosensors to analyze SARS-CoV-2 in wastewater samples quantitatively may have better feasibility and practicability than using the conventional PCR-based approach, especially when considering its fast detection, ease of miniaturization, and potential on-site measurement. Graphical abstract Unlabelled Image
| S-EPMC7934662 | biostudies-literature
Project description:Wastewater-based epidemiology (WBE) is a promising approach for estimating population-wide COVID-19 prevalence through detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA in wastewater. However, various methodological challenges associated with WBE would affect the accuracy of prevalence estimation. To date, the overall uncertainty of WBE and the impact of each step on the prevalence estimation are largely unknown. This study divided the WBE approach into five steps (i.e., virus shedding; in-sewer transportation; sampling and storage; analysis of SARS-CoV-2 RNA concentration in wastewater; back-estimation) and further summarized and quantified the uncertainties associated with each step through a systematic review. Although the shedding of SARS-CoV-2 RNA varied greatly between COVID-19 positive patients, with more than 10 infected persons in the catchment area, the uncertainty caused by the excretion rate became limited for the prevalence estimation. Using a high-frequency flow-proportional sampling and estimating the prevalence through actual water usage data significantly reduced the overall uncertainties to around 20-40% (relative standard deviation, RSD). And under such a scenario, the analytical uncertainty of SARS-CoV-2 RNA in wastewater was the dominant factor. This highlights the importance of using surrogate viruses as internal or external standards during the wastewater analysis, and the need for further improvement on analytical approaches to minimize the analytical uncertainty. This study supports the application of WBE as a complementary surveillance strategy for monitoring COVID-19 prevalence and provides methodological improvements and suggestions to enhance the reliability for future studies.
| S-EPMC7896122 | biostudies-literature
Project description:The COVID-19 pandemic was a watershed event for wastewater-based epidemiology (WBE). It highlighted the inability of existing disease surveillance systems to provide sufficient forewarning to governments on the existing stage and scale of disease spread and underscored the need for an effective early warning signaling system. Recognizing the potentiality of environmental surveillance (ES), in May 2021, COVIDActionCollaborative launched the Precision Health platform. The idea was to leverage ES for equitable mapping of the disease spread in Bengaluru, India and provide early information regarding any inflection in the epidemiological curve of COVID-19. By sampling both networked and non-networked sewage systems in the city, the platform used ES for both equitable and comprehensive surveillance of the population to derive precise information on the existing stage of disease maturity across communities and estimate the scale of the approaching threat. This was in contrast to clinical surveillance, which during the peak of the COVID-19 pandemic in Bengaluru excluded a significant proportion of poor and vulnerable communities from its ambit of representation. The article presents the findings of a sense-making tool which the platform developed for interpreting emerging signals from wastewater data to map disease progression and identifying the inflection points in the epidemiological curve. Thus, the platform accurately generated early warning signals on disease escalation and disseminated it to the government and the general public. This information enabled concerned audiences to implement preventive measures in advance and effectively plan their next steps for improved disease management.
| S-EPMC9999730 | biostudies-literature
Project description:Wastewater-based epidemiology (WBE) has been one of the most cost-effective approaches to track the SARS-CoV-2 levels in the communities since the COVID-19 outbreak in 2020. Normalizing SARS-CoV-2 concentrations by the population biomarkers in wastewater can be critical for interpreting the viral loads, comparing the epidemiological trends among the sewersheds, and identifying the vulnerable communities. In this study, five population biomarkers, pepper mild mottle virus (pMMoV), creatinine (CRE), 5-hydroxyindoleacetic acid (5-HIAA), caffeine (CAF) and its metabolite paraxanthine (PARA) were investigated for their utility in normalizing the SARS-CoV-2 loads through developed direct and indirect approaches. Their utility in assessing the real-time population contributing to the wastewater was also evaluated. The best performed candidate was further tested for its capacity for improving correlation between normalized SARS-CoV-2 loads and the clinical cases reported in the City of Columbia, Missouri, a university town with a constantly fluctuated population. Our results showed that, except CRE, the direct and indirect normalization approaches using biomarkers allow accounting for the changes in wastewater dilution and differences in relative human waste input over time regardless flow volume and population at any given WWTP. Among selected biomarkers, PARA is the most reliable population biomarker in determining the SARS-CoV-2 load per capita due to its high accuracy, low variability, and high temporal consistency to reflect the change in population dynamics and dilution in wastewater. It also demonstrated its excellent utility for real-time assessment of the population contributing to the wastewater. In addition, the viral loads normalized by the PARA-estimated population significantly improved the correlation ( rho =0.5878, p <0.05) between SARS-CoV-2 load per capita and case numbers per capita. This chemical biomarker offers an excellent alternative to the currently CDC-recommended pMMoV genetic biomarker to help us understand the size, distribution, and dynamics of local populations for forecasting the prevalence of SARS-CoV2 within each sewershed.Highlight bullet pointsThe paraxanthine (PARA), the metabolite of the caffeine, is a more reliable population biomarker in SARS-CoV-2 wastewater-based epidemiology studies than the currently recommended pMMoV genetic marker.SARS-CoV-2 load per capita could be directly normalized using the regression functions derived from correlation between paraxanthine and population without flowrate and population data.Normalizing SARS-CoV-2 levels with the chemical marker PARA significantly improved the correlation between viral loads per capita and case numbers per capita.The chemical marker PARA demonstrated its excellent utility for real-time assessment of the population contributing to the wastewater.
| S-EPMC8936110 | biostudies-literature
Project description:Wastewater-based epidemiology is a recognised source of information for pandemic management. In this study, we investigated the correlation between a SARS-CoV-2 signal derived from wastewater sampling and COVID-19 incidence values monitored by means of individual testing programs. The dataset used in the study is composed of timelines (duration approx. five months) of both signals at four wastewater treatment plants across Austria, two of which drain large communities and the other two drain smaller communities. Eight regression models were investigated to predict the viral incidence under varying data inputs and pre-processing methods. It was found that population-based normalisation and smoothing as a pre-processing of the viral load data significantly influence the fitness of the regression models. Moreover, the time latency lag between the wastewater data and the incidence derived from the testing program was found to vary between 2 and 7 days depending on the time period and site. It was found to be necessary to take such a time lag into account by means of multivariate modelling to boost the performance of the regression. Comparing the models, no outstanding one could be identified as all investigated models are revealing a sufficient correlation for the task. The pre-processing of data and a multivariate model formulation is more important than the model structure.
| S-EPMC8535556 | biostudies-literature
Project description:Wastewater-based epidemiology (WBE) has been one of the most cost-effective approaches to track the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) levels in the communities since the coronavirus disease 2019 (COVID-19) outbreak in 2020. Normalizing SARS-CoV-2 concentrations by the population biomarkers in wastewater is critical for interpreting the viral loads, comparing the epidemiological trends among the sewersheds, and identifying the vulnerable communities. In this study, five population biomarkers, pepper mild mottle virus (PMMoV), creatinine (CRE), 5-hydroxyindoleacetic acid (5-HIAA), caffeine (CAF) and its metabolite paraxanthine (PARA) were investigated and validated for their utility in normalizing the SARS-CoV-2 loads through two normalizing approaches using the data from 64 wastewater treatment plants (WWTPs) in Missouri. Their utility in assessing the real-time population contributing to the wastewater was also evaluated. The best performing candidate was further tested for its capacity for improving correlation between normalized SARS-CoV-2 loads and the clinical cases reported in the City of Columbia, Missouri, a university town with a constantly fluctuating population. Our results showed that, except CRE, the direct and indirect normalization approaches using biomarkers allow accounting for the changes in wastewater dilution and differences in relative human waste input over time regardless flow volume and population of the given WWTP. Among selected biomarkers, PARA is the most reliable population biomarker in determining the SARS-CoV-2 load per capita due to its high accuracy, low variability, and high temporal consistency to reflect the change in population dynamics and dilution in wastewater. It also demonstrated its excellent utility for real-time assessment of the population contributing to the wastewater. In addition, the viral loads normalized by the PARA-estimated population significantly improved the correlation (rho=0.5878, p < 0.05) between SARS-CoV-2 load per capita and case numbers per capita. This chemical biomarker complements the current normalization scheme recommended by CDC and helps us understand the size, distribution, and dynamics of local populations for forecasting the prevalence of SARS-CoV2 within each sewershed.
| S-EPMC9376872 | biostudies-literature