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1.
Swiss Med Wkly ; 152: w30202, 2022 06 20.
Article in English | MEDLINE | ID: covidwho-2202460

ABSTRACT

AIMS OF THE STUDY: Wastewater-based epidemiology has contributed significantly to the comprehension of the dynamics of the current COVID-19 pandemic. Its additional value in monitoring SARS-CoV-2 circulation in the population and identifying newly arising variants independently of diagnostic testing is now undisputed. As a proof of concept, we report here correlations between SARS-CoV-2 detection in wastewater and the officially recorded COVID-19 case numbers, as well as the validity of such surveillance to detect emerging variants, exemplified by the detection of the B.1.1.529 variant Omicron in Basel, Switzerland. METHODS: From July 1 to December 31, 2021, wastewater samples were collected six times a week from the inflow of the local wastewater treatment plant that receives wastewater from the catchment area of the city of Basel, Switzerland, comprising 273,075 inhabitants. The number of SARS-CoV-2 RNA copies was determined by reverse transcriptase-quantitative PCR. Spearman's rank correlation coefficients were calculated to determine correlations with the median seven-day incidence of genome copies per litre of wastewater and official case data. To explore delayed correlation effects between the seven-day median number of genome copies/litre wastewater and the median seven-day incidence of SARS-CoV-2 cases, time-lagged Spearman's rank correlation coefficients were calculated for up to 14 days. RNA extracts from daily wastewater samples were used to genotype circulating SARS-CoV-2 variants by next-generation sequencing. RESULTS: The number of daily cases and the median seven-day incidence of SARS-CoV-2 infections in the catchment area showed a high correlation with SARS-CoV-2 measurements in wastewater samples. All correlations between the seven-day median number of genome copies/litre wastewater and the time-lagged median seven-day incidence of SARS-CoV-2 cases were significant (p<0.001) for the investigated lag of up to 14 days. Correlation coefficients declined constantly from the maximum of 0.9395 on day 1 to the minimum of 0.8016 on day 14. The B.1.1.529 variant Omicron was detected in wastewater samples collected on November 21, 2021, before its official acknowledgement in a clinical sample by health authorities. CONCLUSIONS: In this proof-of-concept study, wastewater-based epidemiology proved a reliable and sensitive surveillance approach, complementing routine clinical testing for mapping COVID-19 pandemic dynamics and observing newly circulating SARS-CoV-2 variants.


Subject(s)
COVID-19 , SARS-CoV-2 , COVID-19/diagnosis , COVID-19/epidemiology , Humans , Pandemics , RNA, Viral/genetics , SARS-CoV-2/genetics , Switzerland/epidemiology , Waste Water/analysis
2.
Elife ; 112022 09 16.
Article in English | MEDLINE | ID: covidwho-2145050

ABSTRACT

Biological wastewater treatment plants (BWWTP) are considered to be hotspots for the evolution and subsequent spread of antimicrobial resistance (AMR). Mobile genetic elements (MGEs) promote the mobilization and dissemination of antimicrobial resistance genes (ARGs) and are thereby critical mediators of AMR within the BWWTP microbial community. At present, it is unclear whether specific AMR categories are differentially disseminated via bacteriophages (phages) or plasmids. To understand the segregation of AMR in relation to MGEs, we analyzed meta-omic (metagenomic, metatranscriptomic and metaproteomic) data systematically collected over 1.5 years from a BWWTP. Our results showed a core group of 15 AMR categories which were found across all timepoints. Some of these AMR categories were disseminated exclusively (bacitracin) or primarily (aminoglycoside, MLS and sulfonamide) via plasmids or phages (fosfomycin and peptide), whereas others were disseminated equally by both. Combined and timepoint-specific analyses of gene, transcript and protein abundances further demonstrated that aminoglycoside, bacitracin and sulfonamide resistance genes were expressed more by plasmids, in contrast to fosfomycin and peptide AMR expression by phages, thereby validating our genomic findings. In the analyzed communities, the dominant taxon Candidatus Microthrix parvicella was a major contributor to several AMR categories whereby its plasmids primarily mediated aminoglycoside resistance. Importantly, we also found AMR associated with ESKAPEE pathogens within the BWWTP, and here MGEs also contributed differentially to the dissemination of the corresponding ARGs. Collectively our findings pave the way toward understanding the segmentation of AMR within MGEs, thereby shedding new light on resistome populations and their mediators, essential elements that are of immediate relevance to human health.


Subject(s)
Bacteriophages , Fosfomycin , Water Purification , Humans , Drug Resistance, Microbial/genetics , Waste Water , Bacitracin , Metagenomics , Anti-Bacterial Agents/pharmacology , Bacteriophages/genetics , Aminoglycosides , Sulfonamides , Genes, Bacterial
3.
Sci Data ; 9(1): 713, 2022 11 18.
Article in English | MEDLINE | ID: covidwho-2133500

ABSTRACT

Nationwide, wastewater-based monitoring was newly established in Scotland to track the levels of SARS-CoV-2 viral RNA shed into the sewage network, during the COVID-19 pandemic. We present a curated, reference dataset produced by this national programme, from May 2020 to February 2022. Viral levels were analysed by RT-qPCR assays of the N1 gene, on RNA extracted from wastewater sampled at 162 locations. Locations were sampled up to four times per week, typically once or twice per week, and in response to local needs. We report sampling site locations with geographical coordinates, the total population in the catchment for each site, and the information necessary for data normalisation, such as the incoming wastewater flow values and ammonia concentration, when these were available. The methodology for viral quantification and data analysis is briefly described, with links to detailed protocols online. These wastewater data are contributing to estimates of disease prevalence and the viral reproduction number (R) in Scotland and in the UK.


Subject(s)
COVID-19 , RNA, Viral , Humans , Pandemics , RNA, Viral/genetics , SARS-CoV-2 , Waste Water , Scotland
4.
PLoS One ; 17(11): e0277154, 2022.
Article in English | MEDLINE | ID: covidwho-2116302

ABSTRACT

The potential of wastewater-based epidemiology (WBE) as a surveillance and early warning tool for the COVID-19 outbreak has been demonstrated. For areas with limited testing capacity, wastewater surveillance can provide information on the disease dynamic at a community level. A predictive model is a key to generating quantitative estimates of the infected population. Modeling longitudinal wastewater data can be challenging as biomarkers in wastewater are susceptible to variations caused by multiple factors associated with the wastewater matrix and the sewersheds characteristics. As WBE is an emerging trend, the model should be able to address the uncertainties of wastewater from different sewersheds. We proposed exploiting machine learning and deep learning techniques, which are supported by the growing WBE data. In this article, we reviewed the existing predictive models, among which the emerging machine learning/deep learning models showed great potential. However, most models are built for individual sewersheds with few features extracted from the wastewater. To fulfill the research gap, we compared different time-series and non-time-series models for their short-term predictive performance of COVID-19 cases in 9 diverse sewersheds. The time-series models, long short-term memory (LSTM) and Prophet, outcompeted the non-time-series models. Besides viral (SARS-CoV-2) loads and location identity, domain-specific features like biochemical parameters of wastewater, geographical parameters of the sewersheds, and some socioeconomic parameters of the communities can contribute to the models. With proper feature engineering and hyperparameter tuning, we believe machine learning models like LSTM can be a feasible solution for the COVID-19 trend prediction via WBE. Overall, this is a proof-of-concept study on the application of machine learning in COVID-19 WBE. Future studies are needed to deploy and maintain the model in more real-world applications.


Subject(s)
COVID-19 , Humans , COVID-19/epidemiology , SARS-CoV-2 , Waste Water , Wastewater-Based Epidemiological Monitoring , Disease Outbreaks , Machine Learning , RNA, Viral
5.
J Water Health ; 20(2): 277-286, 2022 Feb.
Article in English | MEDLINE | ID: covidwho-2117661

ABSTRACT

Wastewater-based epidemiology (WBE) is a recognised tool for tracking community transmission of COVID-19. From the second half of 2020, the emergence of new, highly infective, more pathogenic or vaccine-escape SARS-CoV-2 variants is the major public health concern. Variant analysis in sewage might assist the early detection of new mutations. Weekly raw sewage samples from 22 wastewater treatment plants (WWTPs) in Hungary (representing 40% of the population) were analysed between December 2020 and March 2021 for signature mutations N501Y and del H69/V70 of B.1.1.7 lineage by melting point genotyping and RT-digital droplet PCR (RT-ddPCR). The latter method proved to be more efficient in parallel detection of different variants and also provides quantitative information. Wastewater surveillance indicated that the B.1.1.7 variant first emerged in Budapest in early January 2021 and rapidly became dominant in the entire country. Results are in close agreement with the available clinical data (Pearson's correlation coefficient, R = 0.9153). RT-ddPCR was confirmed to be a reliable tool for tracking emerging variant ratios in wastewaters. It is a rapid and cost-effective method compared to whole-genome sequencing, but only applicable for the detection of known mutations. Efficient variant surveillance might require the combination of multiple methods.


Subject(s)
COVID-19 , SARS-CoV-2 , Humans , SARS-CoV-2/genetics , Waste Water , COVID-19/epidemiology , Wastewater-Based Epidemiological Monitoring , Sewage , Hungary/epidemiology
6.
Sci Rep ; 12(1): 19171, 2022 Nov 10.
Article in English | MEDLINE | ID: covidwho-2117357

ABSTRACT

Azithromycin is one of the most widely used antibiotics in medicine prescribed for various infectious diseases such as COVID-19. A significant amount of this drug is always disposed of in hospital effluents. In this study, the removal of azithromycin using Cobalt-Ferrite magnetic nanoparticles (MNP) is investigated in the presence of UV light. For this purpose, magnetic nanoparticles are synthesized and added to the test samples as a catalyst in specific proportions. To determine the structural and morphological properties of nanoparticles, characterization tests including scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), vibrating-sample magnetometer (VSM), and Energy-dispersive X-ray spectroscopy (EDX) are performed. 27 runs have been implemented based on the design of experiments using the Box-Behnken Design (BBD) method. Parameters are the initial concentration of azithromycin (20-60 mg/L), contact time (30-90 min), pH (6-10), and the dose of magnetic nanoparticles (20-60 mg/L). The obtained model interprets test results with high accuracy (R2 = 0.9531). Also, optimization results by the software show that the contact time of 90 min, MNP dosage of 60 mg/L, pH value of 6.67, and azithromycin initial concentration of 20 mg/L leads to the highest removal efficiency of 89.71%. These numbers are in the range of other studies in this regard.


Subject(s)
COVID-19 , Magnetite Nanoparticles , Humans , Waste Water , Azithromycin , Spectroscopy, Fourier Transform Infrared
7.
Water Environ Res ; 94(11): e10807, 2022 Nov.
Article in English | MEDLINE | ID: covidwho-2119474

ABSTRACT

Wastewater surveillance, also known as wastewater-based epidemiology (WBE), has been successfully used to detect SARS-CoV-2 and other viruses in sewage in many locations in the United States and globally. This includes implementation of the surveillance on college and university campuses. A two-phase study was conducted during the 2020-2021 academic year to test the feasibility of a WBE system on campus and to supplement the clinical COVID-19 testing performed for the student, staff, and faculty body. The primary objective during the Fall 2020 semester was to monitor a large portion of the on-campus population and to obtain an understanding of the spreading of the SARS-CoV-2 virus. The Spring 2021 objective was focused on selected residence halls and groups of residents on campus, as this was more efficient and relevant for an effective follow-up response. Logistical problems and planning oversights initially occurred but were corrected with improved communication and experience. Many lessons were learned, including effective mapping, site planning, communication, personnel organization, and equipment management, and obtained along the way, thereby paving an opportune guide for future planning efforts. PRACTITIONER POINTS: WBE was successful in the detection of many SARS-CoV-2 variants incl. Alpha, Beta, Gamma, Delta, Lambda, Mu, and Omicron. Careful planning and contingencies were essential for a successful implementation of a SARS-CoV-2 monitoring program. A surveillance program may be important for detection and monitoring of other public health relevant targets in wastewater incl. bacteria, viruses, fungi and viruses. Diverse lessons were learned incl. effective mapping, site planning, communication, personnel organization, and equipment management, thereby providing a guide for future planning efforts.


Subject(s)
COVID-19 , SARS-CoV-2 , Humans , Waste Water , Wastewater-Based Epidemiological Monitoring , COVID-19 Testing , Universities , COVID-19/epidemiology
8.
Genome Biol ; 23(1): 236, 2022 Nov 08.
Article in English | MEDLINE | ID: covidwho-2108879

ABSTRACT

Effectively monitoring the spread of SARS-CoV-2 mutants is essential to efforts to counter the ongoing pandemic. Predicting lineage abundance from wastewater, however, is technically challenging. We show that by sequencing SARS-CoV-2 RNA in wastewater and applying algorithms initially used for transcriptome quantification, we can estimate lineage abundance in wastewater samples. We find high variability in signal among individual samples, but the overall trends match those observed from sequencing clinical samples. Thus, while clinical sequencing remains a more sensitive technique for population surveillance, wastewater sequencing can be used to monitor trends in mutant prevalence in situations where clinical sequencing is unavailable.


Subject(s)
COVID-19 , SARS-CoV-2 , Humans , SARS-CoV-2/genetics , Waste Water , RNA, Viral/genetics , Transcriptome
9.
Sci Rep ; 12(1): 19085, 2022 Nov 09.
Article in English | MEDLINE | ID: covidwho-2106453

ABSTRACT

Wastewater-based epidemiology (WBE) has emerged as a valuable epidemiologic tool to detect the presence of pathogens and track disease trends within a community. WBE overcomes some limitations of traditional clinical disease surveillance as it uses pooled samples from the entire community, irrespective of health-seeking behaviors and symptomatic status of infected individuals. WBE has the potential to estimate the number of infections within a community by using a mass balance equation, however, it has yet to be assessed for accuracy. We hypothesized that the mass balance equation-based approach using measured SARS-CoV-2 wastewater concentrations can generate accurate prevalence estimates of COVID-19 within a community. This study encompassed wastewater sampling over a 53-week period during the COVID-19 pandemic in Gainesville, Florida, to assess the ability of the mass balance equation to generate accurate COVID-19 prevalence estimates. The SARS-CoV-2 wastewater concentration showed a significant linear association (Parameter estimate = 39.43, P value < 0.0001) with clinically reported COVID-19 cases. Overall, the mass balance equation produced accurate COVID-19 prevalence estimates with a median absolute error of 1.28%, as compared to the clinical reference group. Therefore, the mass balance equation applied to WBE is an effective tool for generating accurate community-level prevalence estimates of COVID-19 to improve community surveillance.


Subject(s)
COVID-19 , Wastewater-Based Epidemiological Monitoring , Humans , COVID-19/epidemiology , SARS-CoV-2 , Pandemics , Waste Water , Prevalence , RNA, Viral
10.
PLoS One ; 17(10): e0272830, 2022.
Article in English | MEDLINE | ID: covidwho-2098733

ABSTRACT

Genomic surveillance and wastewater tracking strategies were used to strengthen the public health response to an outbreak of the SARS-CoV-2 Delta AY.25 lineage associated with a university campus in Arizona. Epidemiologic and clinical data routinely gathered through contact tracing were matched to SARS-CoV-2 genomes belonging to an outbreak of AY.25 identified through ongoing phylogenomic analyses. Continued phylogenetic analyses were conducted to further describe the AY.25 outbreak. Wastewater collected twice weekly from sites across campus was tested for SARS-CoV-2 by RT-qPCR, and subsequently sequenced to identify variants. The AY.25 outbreak was defined by a single mutation (C18804T) and comprised 379 genomes from SARS-CoV-2 positive cases associated with the university and community. Several undergraduate student gatherings and congregate living settings on campus likely contributed to the rapid spread of COVID-19 across the university with secondary transmission into the community. The clade defining mutation was also found in wastewater samples collected from around student dormitories a week before the semester began, and 9 days before cases were identified. Genomic, epidemiologic, and wastewater surveillance provided evidence that an AY.25 clone was likely imported into the university setting just prior to the onset of the Fall 2021 semester, rapidly spread through a subset of the student population, and then subsequent spillover occurred in the surrounding community. The university and local public health department worked closely together to facilitate timely reporting of cases, identification of close contacts, and other necessary response and mitigation strategies. The emergence of new SARS-CoV-2 variants and potential threat of other infectious disease outbreaks on university campuses presents an opportunity for future comprehensive One Health genomic data driven, targeted interventions.


Subject(s)
COVID-19 , One Health , Humans , SARS-CoV-2/genetics , Waste Water , Universities , COVID-19/epidemiology , Phylogeny , Arizona/epidemiology , Wastewater-Based Epidemiological Monitoring , Disease Outbreaks , Genomics
11.
Environ Monit Assess ; 194(12): 884, 2022 Oct 14.
Article in English | MEDLINE | ID: covidwho-2093260

ABSTRACT

In the last few decades, environmental contaminants (ECs) have been introduced into the environment at an alarming rate. There is a risk to human health and aquatic ecosystems from trace levels of emerging contaminants, including hospital wastewater (HPWW), cosmetics, personal care products, endocrine system disruptors, and their transformation products. Despite the fact that these pollutants have been introduced or detected relatively recently, information about their characteristics, actions, and impacts is limited, as are the technologies to eliminate them efficiently. A wastewater recycling system is capable of providing irrigation water for crops and municipal sewage treatment, so removing ECs before wastewater reuse is essential. Water treatment processes containing advanced ions of biotic origin and ECs of biotic origin are highly recommended for contaminants. This study introduces the fundamentals of the treatment of tertiary wastewater, including membranes, filtration, UV (ultraviolet) irradiation, ozonation, chlorination, advanced oxidation processes, activated carbon (AC), and algae. Next, a detailed description of recent developments and innovations in each component of the emerging contaminant removal process is provided.


Subject(s)
Cosmetics , Endocrine Disruptors , Ozone , Water Pollutants, Chemical , Water Purification , Charcoal , Ecosystem , Endocrine Disruptors/analysis , Environmental Monitoring , Humans , Sewage , Waste Water/analysis , Water Pollutants, Chemical/analysis
12.
Appl Environ Microbiol ; 88(22): e0087422, 2022 Nov 22.
Article in English | MEDLINE | ID: covidwho-2088398

ABSTRACT

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)/coronavirus disease 2019 (COVID-19) pandemic has highlighted an important role for efficient surveillance of microbial pathogens. High-throughput sequencing technologies provide valuable surveillance tools, offering opportunities to conduct high-resolution monitoring from diverse sample types, including from environmental sources. However, given their large size and potential to contain mixtures of lineages within samples, such genomic data sets can present challenges for analyzing the data and communicating results with diverse stakeholders. Here, we report MixviR, an R package for exploring, analyzing, and visualizing genomic data from potentially mixed samples of a target microbial group. MixviR characterizes variation at both the nucleotide and amino acid levels and offers the RShiny interactive dashboard for exploring data. We demonstrate MixviR's utility with validation studies using mixtures of known lineages from both SARS-CoV-2 and Mycobacterium tuberculosis and with a case study analyzing lineages of SARS-CoV-2 in wastewater samples over time at a sampling location in Ohio, USA. IMPORTANCE High-throughput sequencing technologies hold great potential for contributing to genomic-based surveillance of microbial diversity from environmental samples. However, the size of the data sets, along with the potential for environmental samples to contain multiple evolutionary lineages of interest, present challenges around analyzing and effectively communicating inferences from these data sets. The software described here provides a novel and valuable tool for exploring such data. Though originally designed and used for monitoring SARS-CoV-2 lineages in wastewater, it can also be applied to analyses of genomic diversity in other microbial groups.


Subject(s)
COVID-19 , SARS-CoV-2 , Humans , SARS-CoV-2/genetics , Waste Water , Pandemics , Genomics
13.
Water Res ; 226: 119306, 2022 Nov 01.
Article in English | MEDLINE | ID: covidwho-2086834

ABSTRACT

Genomic surveillance of SARS-CoV-2 has provided a critical evidence base for public health decisions throughout the pandemic. Sequencing data from clinical cases has helped to understand disease transmission and the spread of novel variants. Genomic wastewater surveillance can offer important, complementary information by providing frequency estimates of all variants circulating in a population without sampling biases. Here we show that genomic SARS-CoV-2 wastewater surveillance can detect fine-scale differences within urban centres, specifically within the city of Liverpool, UK, during the emergence of Alpha and Delta variants between November 2020 and June 2021. Furthermore, wastewater and clinical sequencing match well in the estimated timing of new variant rises and the first detection of a new variant in a given area may occur in either clinical or wastewater samples. The study's main limitation was sample quality when infection prevalence was low in spring 2021, resulting in a lower resolution of the rise of the Delta variant compared to the rise of the Alpha variant in the previous winter. The correspondence between wastewater and clinical variant frequencies demonstrates the reliability of wastewater surveillance. However, discrepancies in the first detection of the Alpha variant between the two approaches highlight that wastewater monitoring can also capture missing information, possibly resulting from asymptomatic cases or communities less engaged with testing programmes, as found by a simultaneous surge testing effort across the city.


Subject(s)
COVID-19 , Waste Water , Humans , SARS-CoV-2/genetics , Reproducibility of Results , COVID-19/epidemiology , Wastewater-Based Epidemiological Monitoring , Genomics
14.
Viruses ; 14(11)2022 Oct 25.
Article in English | MEDLINE | ID: covidwho-2082117

ABSTRACT

Wastewater-based epidemiology (WBE) is a tool involving the analysis of wastewater for chemicals and pathogens at the community level. WBE has been shown to be an effective surveillance system for SARS-CoV-2, providing an early-warning-detection system for disease prevalence in the community via the detection of genetic materials in the wastewater. In numerous nation-states, studies have indicated the presence of SARS-CoV-2 in wastewater. Herein, we report the primary time-course monitoring of SARS-CoV-2 RNA in wastewater samples in São José do Rio Preto-SP/Brazil in order to explain the dynamics of the presence of SARS-CoV-2 RNA during one year of the SARS-CoV-2 pandemic and analyze possible relationships with other environmental parameters. We performed RNA quantification of SARS-CoV-2 by RT-qPCR using N1 and N2 targets. The proportion of positive samples for every target resulted in 100% and 96.6% for N1 and N2, respectively. A mean lag of -5 days is observed between the wastewater signal and the new SARS-CoV-2-positive cases reported. A correlation was found between the air and wastewater temperatures and therefore between the SARS-CoV-2 viral titers for N1 and N2 targets. We also observed a correlation between SARS-CoV-2 viral titers and media wastewater flow for the N1 target. In addition, we observed higher viral genome copies within the wastewater samples collected on non-rainy days for the N1 target. Thus, we propose that, based on our results, monitoring raw wastewater may be a broadly applicable strategy that might contribute to resolving the pressing problem of insufficient diagnostic testing; it may represent an inexpensive and early-warning method for future COVID-19 outbreaks, mainly in lower- and middle-income countries.


Subject(s)
COVID-19 , SARS-CoV-2 , Humans , SARS-CoV-2/genetics , Waste Water , Wastewater-Based Epidemiological Monitoring , COVID-19/epidemiology , RNA, Viral/genetics , Brazil/epidemiology
15.
PLoS Pathog ; 18(10): e1010636, 2022 10.
Article in English | MEDLINE | ID: covidwho-2079775

ABSTRACT

Wastewater-based epidemiology (WBE) is an effective way of tracking the appearance and spread of SARS-COV-2 lineages through communities. Beginning in early 2021, we implemented a targeted approach to amplify and sequence the receptor binding domain (RBD) of SARS-COV-2 to characterize viral lineages present in sewersheds. Over the course of 2021, we reproducibly detected multiple SARS-COV-2 RBD lineages that have never been observed in patient samples in 9 sewersheds located in 3 states in the USA. These cryptic lineages contained between 4 to 24 amino acid substitutions in the RBD and were observed intermittently in the sewersheds in which they were found for as long as 14 months. Many of the amino acid substitutions in these lineages occurred at residues also mutated in the Omicron variant of concern (VOC), often with the same substitutions. One of the sewersheds contained a lineage that appeared to be derived from the Alpha VOC, but the majority of the lineages appeared to be derived from pre-VOC SARS-COV-2 lineages. Specifically, several of the cryptic lineages from New York City appeared to be derived from a common ancestor that most likely diverged in early 2020. While the source of these cryptic lineages has not been resolved, it seems increasingly likely that they were derived from long-term patient infections or animal reservoirs. Our findings demonstrate that SARS-COV-2 genetic diversity is greater than what is commonly observed through routine SARS-CoV-2 surveillance. Wastewater sampling may more fully capture SARS-CoV-2 genetic diversity than patient sampling and could reveal new VOCs before they emerge in the wider human population.


Subject(s)
COVID-19 , SARS-CoV-2 , Animals , Humans , SARS-CoV-2/genetics , Waste Water , COVID-19/epidemiology , Genetic Variation
16.
PLoS One ; 17(10): e0274793, 2022.
Article in English | MEDLINE | ID: covidwho-2079736

ABSTRACT

BACKGROUND: Wastewater-based epidemiology (WBE) surveillance as an early warning system (EWS) for monitoring community transmission of SARS-CoV-2 in low- and middle-income country (LMIC) settings, where diagnostic testing capacity is limited, needs further exploration. We explored the feasibility to conduct a WBE surveillance in Indonesia, one of the global epicenters of the COVID-19 pandemic in the middle of 2021, with the fourth largest population in the world where sewer and non-sewered sewage systems are implemented. The feasibility and resource capacity to collect samples on a weekly or fortnightly basis with grab and/or passive sampling methods, as well as to conduct qualitative and quantitative identification of SARS-CoV-2 ribonucleic acid (RNA) using real-time RT-PCR (RT-qPCR) testing of environmental samples were explored. MATERIALS AND METHODS: We initiated a routine surveillance of wastewater and environmental sampling at three predetermined districts in Special Region of Yogyakarta Province. Water samples were collected from central and community wastewater treatment plants (WWTPs), including manholes flowing to the central WWTP, and additional soil samples were collected for the near source tracking (NST) locations (i.e., public spaces where people congregate). RESULTS: We began collecting samples in the Delta wave of the COVID-19 pandemic in Indonesia in July 2021. From a 10-week period, 54% (296/544) of wastewater and environmental samples were positive for SARS-CoV-2 RNA. The sample positivity rate decreased in proportion with the reported incidence of COVID-19 clinical cases in the community. The highest positivity rate of 77% in week 1, was obtained for samples collected in July 2021 and decreased to 25% in week 10 by the end of September 2021. CONCLUSION: A WBE surveillance system for SARS-CoV-2 in Indonesia is feasible to monitor the community burden of infections. Future studies testing the potential of WBE and EWS for signaling early outbreaks of SARS-CoV-2 transmissions in this setting are required.


Subject(s)
COVID-19 , SARS-CoV-2 , COVID-19/diagnosis , COVID-19/epidemiology , Feasibility Studies , Humans , Indonesia/epidemiology , Pandemics , RNA, Viral/analysis , RNA, Viral/genetics , SARS-CoV-2/genetics , Sewage , Soil , Waste Water/analysis , Water/analysis
17.
Sci Rep ; 12(1): 13490, 2022 08 05.
Article in English | MEDLINE | ID: covidwho-2077088

ABSTRACT

The ribonucleic acid (RNA) of the severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) is detectable in municipal wastewater as infected individuals can shed the virus in their feces. Viral concentration in wastewater can inform the severity of the COVID-19 pandemic but observations can be noisy and sparse and hence hamper the epidemiological interpretation. Motivated by a Canadian nationwide wastewater surveillance data set, unlike previous studies, we propose a novel Bayesian statistical framework based on the theories of functional data analysis to tackle the challenges embedded in the longitudinal wastewater monitoring data. By employing this framework to analyze the large-scale data set from the nationwide wastewater surveillance program covering 15 sampling sites across Canada, we successfully detect the true trends of viral concentration out of noisy and sparsely observed viral concentrations, and accurately forecast the future trajectory of viral concentrations in wastewater. Along with the excellent performance assessment using simulated data, this study shows that the proposed novel framework is a useful statistical tool and has a significant potential in supporting the epidemiological interpretation of noisy viral concentration measurements from wastewater samples in a real-life setting.


Subject(s)
COVID-19 , SARS-CoV-2 , Bayes Theorem , COVID-19/epidemiology , Canada , Humans , Pandemics , RNA, Viral , Waste Water , Wastewater-Based Epidemiological Monitoring
18.
Sci Total Environ ; 838(Pt 4): 156535, 2022 Sep 10.
Article in English | MEDLINE | ID: covidwho-2076705

ABSTRACT

Wastewater-based epidemiology (WBE) has been utilized as an early warning tool to anticipate disease outbreaks, especially during the COVID-19 pandemic. However, COVID-19 disease models built from wastewater-collected data have been limited by the complexities involved in estimating SARS-CoV-2 fecal shedding rates. In this study, wastewater from six municipalities in Arizona and Florida with distinct demographics were monitored for SARS-CoV-2 RNA between September 2020 and December 2021. Virus concentrations with corresponding clinical case counts were utilized to estimate community-wide fecal shedding rates that encompassed all infected individuals. Analyses suggest that average SARS-CoV-2 RNA fecal shedding rates typically occurred within a consistent range (7.53-9.29 log10 gc/g-feces); and yet, were unique to each community and influenced by population demographics. Age, ethnicity, and socio-economic factors may have influenced shedding rates. Interestingly, populations with median age between 30 and 39 had the greatest fecal shedding rates. Additionally, rates remained relatively constant throughout the pandemic provided conditions related to vaccination and variants were unchanged. Rates significantly increased in some communities when the Delta variant became predominant. Findings in this study suggest that community-specific shedding rates may be appropriate in model development relating wastewater virus concentrations to clinical case counts.


Subject(s)
COVID-19 , SARS-CoV-2 , Adult , COVID-19/epidemiology , Feces , Humans , Pandemics , RNA, Viral , Waste Water , Wastewater-Based Epidemiological Monitoring
19.
JAMA Netw Open ; 5(10): e2237149, 2022 10 03.
Article in English | MEDLINE | ID: covidwho-2074858

ABSTRACT

Importance: The US Centers for Disease Control and Prevention shortened the recommended isolation period for SARS-CoV-2 infection from 10 days to 5 days in December 2021. It is unknown whether an individual with the infection may still have a positive result to a rapid antigen test and potentially be contagious at the end of this shortened isolation period. Objective: To estimate the proportion of individuals with SARS-CoV-2 infection whose rapid antigen test is still positive starting 7 days postdiagnosis. Design, Setting, and Participants: This case series analyzed student athletes at a National Collegiate Athletic Association Division I university campus who tested positive for SARS-CoV-2 between January 3 and May 6, 2022. Individuals underwent rapid antigen testing starting 7 days postdiagnosis to determine whether they could end their isolation period. Exposures: Rapid antigen testing 7 days after testing positive for SARS-CoV-2. Main Outcomes and Measures: Rapid antigen test results, symptom status, and SARS-CoV-2 variant identification via campus wastewater analysis. Results: A total of 264 student athletes (140 [53%] female; mean [SD] age, 20.1 [1.2] years; range, 18-25 years) representing 268 infections (177 [66%] symptomatic, 91 [34%] asymptomatic) were included in the study. Of the 248 infections in individuals who did a day 7 test, 67 (27%; 95% CI, 21%-33%) tests were still positive. Patients with symptomatic infections were significantly more likely to test positive on day 7 vs those who were asymptomatic (35%; 95% CI, 28%-43% vs 11%; 95% CI, 5%-18%; P < .001). Patients with the BA.2 variant were also significantly more likely to test positive on day 7 compared with those with the BA.1 variant (40%; 95% CI, 29%-51% vs 21%; 95% CI, 15%-27%; P = .007). Conclusions and Relevance: In this case series, rapid antigen tests remained positive in 27% of the individuals after 7 days of isolation, suggesting that the Centers for Disease Control and Prevention-recommended 5-day isolation period may be insufficient in preventing ongoing spread of disease. Further studies are needed to determine whether these findings are present in a more heterogeneous population and in subsequent variants.


Subject(s)
COVID-19 , SARS-CoV-2 , Humans , Female , Young Adult , Adult , Male , COVID-19/diagnosis , COVID-19/epidemiology , Prevalence , Waste Water , Athletes
20.
Int J Environ Res Public Health ; 19(20)2022 Oct 17.
Article in English | MEDLINE | ID: covidwho-2071470

ABSTRACT

Dependent on the excretion pattern, wastewater monitoring of viruses can be a valuable approach to characterizing their circulation in the human population. Using polyethylene glycol precipitation and reverse transcription-quantitative PCR, the occurrence of RNA of SARS-CoV-2 and influenza viruses A/B in the raw wastewater of two treatment plants in Germany between January and May 2022 was investigated. Due to the relatively high incidence in both exposal areas (plant 1 and plant 2), SARS-CoV-2-specific RNA was determined in all 273 composite samples analyzed (concentration of E gene: 1.3 × 104 to 3.2 × 106 gc/L). Despite a nation-wide low number of confirmed infections, influenza virus A was demonstrated in 5.2% (concentration: 9.8 × 102 to 8.4 × 104 gc/L; plant 1) and in 41.6% (3.6 × 103 to 3.0 × 105 gc/L; plant 2) of samples. Influenza virus B was detected in 36.0% (7.2 × 102 to 8.5 × 106 gc/L; plant 1) and 57.7% (9.6 × 103 to 2.1 × 107 gc/L; plant 2) of wastewater samples. The results of the study demonstrate the frequent detection of two primary respiratory viruses in wastewater and offer the possibility to track the epidemiology of influenza by wastewater-based monitoring.


Subject(s)
COVID-19 , Orthomyxoviridae , Viruses , Humans , SARS-CoV-2/genetics , Waste Water , Cities , COVID-19/epidemiology , RNA , Orthomyxoviridae/genetics , Polyethylene Glycols , RNA, Viral/genetics
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