Technical framework for wastewater-based epidemiology of SARS-CoV-2.
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ABSTRACT:
SUBMITTER: Wu J
PROVIDER: S-EPMC8195746 | biostudies-literature |
REPOSITORIES: biostudies-literature
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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 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:SARS-CoV-2 pandemic is having a devastating effect on human lives. Recent reports have shown that majority of the individuals recovered from COVID-19 have serious health complications, which is going to be a huge economic burden globally. Given the wide-spread transmission of SARS-CoV-2 it is almost impossible to test every individual in densely populated countries. Recent reports have shown that sewage-based surveillance can be used as holistic approach to understand the spread of the pandemic within a population or area. Here we have estimated the spread of SARS-CoV-2 in the city of Hyderabad, India, which is a home for nearly 10 million people. The sewage samples were collected from all the major sewage treatment plants (STPs) and were processed for detecting the viral genome using the standard Reverse Transcription Polymerase Chain Reaction (RT-PCR) method. Interestingly, inlet samples of STPs were positive for SARS-CoV-2, while the outlets were negative, which indicates that the standard sewage treatment methods are efficient in eliminating the SARS-CoV-2 viral particles. Based on the detected viral gene copies per litre and viral particle shedding per individual, the total number of individuals exposed to SARS-CoV-2 was estimated. Through this study we suggest that sewage-based surveillance is an effective approach to study the infection dynamics, which helps in efficient management of the SARS-CoV-2 spread.
| S-EPMC7787060 | 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
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: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) is useful for the surveillance of severe acute
| S-EPMC8340581 | biostudies-literature
Project description:SARS-CoV-2 RNA quantification in wastewater is an important tool for monitoring the prevalence of COVID-19 disease on a community scale which complements case-based surveillance systems. As novel variants of concern (VOCs) emerge there is also a need to identify the primary circulating variants in a community, accomplished to date by sequencing clinical samples. Quantifying variants in wastewater offers a cost-effective means to augment these sequencing efforts. In this study, SARS-CoV-2 N1 RNA concentrations and daily loadings were determined and compared to case-based data collected as part of a national surveillance programme to determine the validity of wastewater surveillance to monitor infection spread in the greater Dublin area. Further, sequencing of clinical samples was conducted to determine the primary SARS-CoV-2 lineages circulating in Dublin. Finally, digital PCR was employed to determine whether SARS-CoV-2 VOCs, Alpha and Delta, were quantifiable from wastewater. No lead or lag time was observed between SARS-CoV-2 wastewater and case-based data and SARS-CoV-2 trends in Dublin wastewater significantly correlated with the notification of confirmed cases through case-based surveillance preceding collection with a 5-day average. This demonstrates that viral RNA in Dublin's wastewater mirrors the spread of infection in the community. Clinical sequence data demonstrated that increased COVID-19 cases during Ireland's third wave coincided with the introduction of the Alpha variant, while the fourth wave coincided with increased prevalence of the Delta variant. Interestingly, the Alpha variant was detected in Dublin wastewater prior to the first genome being sequenced from clinical samples, while the Delta variant was identified at the same time in clinical and wastewater samples. This work demonstrates the validity of wastewater surveillance for monitoring SARS-CoV-2 infections and also highlights its effectiveness in identifying circulating variants which may prove useful when sequencing capacity is limited.
| S-EPMC9110007 | biostudies-literature
Project description:While wastewater-based epidemiology has proven a useful tool for epidemiological surveillance during the COVID-19 pandemic, few quantitative models comparing virus concentrations in wastewater samples and cumulative incidence have been established. In this work, a simple mathematical model relating virus concentration and cumulative incidence for full contagion waves was developed. The model was then used for short-term forecasting and compared to a local linear model. Both scenarios were tested using a dataset composed of samples from 32 wastewater treatment plants and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) incidence data covering the corresponding geographical areas during a 7-month period, including two contagion waves. A population-averaged dataset was also developed to model and predict the incidence over the full geography. Overall, the mathematical model based on wastewater data showed a good correlation with cumulative cases and allowed us to anticipate SARS-CoV-2 incidence in one week, which is of special relevance in situations where the epidemiological monitoring system cannot be fully implemented.
| S-EPMC9443647 | biostudies-literature