The current COVID-19 pandemic has emphasized the vulnerability of communities living in the urban outskirts and informal settlements. The lack of reliable COVID-19 case data highlights the importance and application of wastewater-based epidemiology. This study aimed to monitor the COVID-19 trends in four vulnerable urban communities (slums and low-income neighborhoods) in metropolitan São Paulo by assessing the SARS-CoV-2 RNA viral load in wastewater. We analyzed 160 samples from May 2020 to June 2021 with weekly or fortnightly samplings. The samples were ultracentrifuged with glycine elution and quantified by N1/N2 SARS-CoV-2 RT-qPCR. The results of positivity were 100% (Paraisópolis, Heliópolis and Cidade Tiradentes) and 76.9% (Vila Brasilândia). The new case numbers of COVID-19, counted from the onset of symptoms, positively correlated with SARS-CoV-2 N1 viral loads from the two largest communities (p<0.001). SARS-CoV-2 infectivity was tested in Vero E6 cells after concentration with the two techniques, ultrafiltration (Centricon® Plus-70 10 kDa) and sucrose cushion ultracentrifugation, but none of the evaluated samples presented positive results. Next-generation sequencing (NGS) analysis from samples collected in March and August 2021 revealed the presence of the clade 20 J (lineage P.1) belonging to the most prevalent circulating variant in the country. Our results showed that wastewater surveillance data can be used as complementary indicators to monitor the dynamics and temporal trends of COVID-19. The infectivity test results strengthened the evidence of low risk of infection associated with SARS-CoV-2 in wastewater.

  • SARS-CoV-2 WBE can be a valuable tool to follow trends of COVID-19 cases in vulnerable communities (slums and low-income neighborhoods).

  • Positive samples for SARS-CoV-2 RNA presented negative results in the infectivity assays performed with Vero E6 culture cell.

  • Preliminary results of NGS sequencing analysis showed the circulation of lineage P.1, the predominant SARS-CoV-2 variant in clinical genomic surveillance

Graphical Abstract

Graphical Abstract
Graphical Abstract

The first occurrence of COVID-19 in Latin America was confirmed in São Paulo, Brazil, on February 26, 2020, two weeks before the World Health Organization declared COVID-19 a pandemic (Cucinotta & Vanelli 2020). Since then, the world has been under a global crisis with a severe impact on the public health system and economy (CRS 2021). The challenges the world has faced with the pandemic triggered an accelerated scientific and technological development in the areas of diagnosis, vaccine, logistics, modeling and epidemiological surveillance.

Environmental surveillance has been used for decades to evidence the circulation of waterborne pathogens in the population, particularly poliovirus, and more recently antimicrobial-resistant microorganisms, as a complementary approach to infectious diseases surveillance (Asghar et al. 2014; GPEI 2016; Sims & Kasprzyk-Hordern 2020; WHO 2020; Xagoraraki & O'Brien 2020). The first report of SARS-CoV-2 RNA detection in sewage in March 2020, in the Netherlands (Medema et al. 2020b), indicated that wastewater surveillance was also a sensitive tool to assess the circulation of SARS-Cov-2 in the community and the COVID infection trends. Since then, several research groups worldwide have been using this approach as an epidemiological device and as an early warning system (Haramoto et al. 2020; La Rosa et al. 2020; Wu et al. 2020; Ahmed et al. 2021; https://www.covid19wbec.org/collaborators). It has also been used as a valuable instrument to provide information about the emergence and abundance of SARS-CoV-2 strains (Nemudryi et al. 2020; Rimoldi et al. 2020; Crits-Christoph et al. 2021; Jahn et al. 2021). Many countries have already implemented a national wastewater surveillance system or program to support the government in formulating epidemic prevention policies for COVID-19, including the Netherlands (Rijksoverheid 2021), the USA (CDC 2021; Kirby et al. 2021), the UK (Wade et al. 2022), South Africa (NICD 2021), Japan (Takeda et al. 2021) and others.

Brazil has been severely affected by the COVID pandemic. By July 1, 2021, it was the second country in a total number of deaths (520,000), only ranking behind the USA (605,000). It was also ranked ninth in the number of deaths per million inhabitants (Ritchie et al. 2020), with daily death rates ranging from 3,000 to 4,000 between March and April 2021. Given the testing limitations and the challenges faced in obtaining accurate clinical and epidemiological national data, several wastewater-based epidemiology (WBE) programs have been implemented by local governments and/or research projects mostly driven to assess spatial and temporal SARS-CoV-2 load fluctuations in the sewer system and wastewater treatment plants (WWTPs) of large metropolitan regions, supporting decision-makers on COVID-19 infection trends at the population level (ANA 2021; Claro et al. 2021; Fongaro et al. 2021; Mota el al. 2021; Prado et al. 2021).

Assessing the circulation of SARS-CoV-2 in the wastewater of a huge city as São Paulo has been a major challenge. With around 13 million people, the city is served by four WWTPs, responsible for the treatment of around 75% of the sewage generated. The remaining 25% are ultimately released into surface waters.

The city of São Paulo is responsible for the highest number of cases and deaths by COVID-19 among Brazilian cities, since the beginning of the pandemic. The city has the largest number of slums (favelas), with more than 1,700 reported by the Municipal Housing Secretariat (SEHAB, http://www.habitasampa.inf.br/habitacao/). It is estimated that there are more than 390,000 homes with more than two million inhabitants living in slums in São Paulo, about 11% of the city's population. The mortality rate of the new coronavirus in São Paulo can be up to 10 times higher in neighborhoods with the worst social conditions that coincide with the areas with the highest slum concentrations (PMSP 2020; Figueiredo 2021). However, cases confirmed by laboratory examination were not predominant, which may show greater difficulty in obtaining confirmation of suspected cases in these areas (PMSP 2020).

Poor sanitation conditions, water scarcity, hyper-dense dwellings, crowded households and lack of health care access, among other factors, make urban slums and other vulnerable communities major COVID-19 hotspots and relevant areas for the application of WBE programs; however, few studies have been reported in these communities. Mota et al. (2021) and Prado et al. (2021) in decentralized monitoring of SARS-CoV-2 RNA in sewages from Belo Horizonte and Rio de Janeiro, two other large Brazilian cities, showed that wastewater monitoring data is more sensitive for identifying hotspots in vulnerable areas than clinical/epidemiological data, allowing early intervention actions by public health authorities. Razzolini et al. (2021) followed the evolution of SARS-CoV-2 RNA concentration for 7 months in a contaminated stream that receives raw sewage from an urban underprivileged settlement in the city of São Paulo and observed a statistically significant correlation between SARS-CoV-2 concentration in water and COVID-19 cases in the community. The authors concluded that virus concentration in the environment reflects the epidemiological status of the community. Iglesias et al. (2021) in a similar study conducted in a low-resource community in Buenos Aires noted that SARS-CoV-2 measurements in the lagoon that receives sewage from the community could be applied to estimate the changes in the COVID-19 prevalence. Another frequent concern in these highly vulnerable regions is the possibility of fecal–oral and fecal–respiratory transmissions, although the viability of SARS-CoV-2 in sewage samples has not been demonstrated to date (Rimoldi et al. 2020; Westhaus et al. 2021).

The present study is part of the São Paulo State wastewater surveillance program for COVID-19 initiated in April 2020 and focused on assessing trends in SARS-CoV-2 RNA concentrations in wastewater from the two main favelas of the city of São Paulo (Paraisópolis and Heliópolis) and two low-income neighborhoods (Vila Brasilândia and Cidade Tiradentes), areas of high vulnerability considered as a priority for the fight against COVID-19 by the city Health Service System. SARS-CoV-2 concentration methods, genetic diversity and viability of SARS-CoV-2 were also evaluated.

Sampling

Four communities with different population sizes were selected for SARS-CoV-2 monitoring, including Paraisópolis and Heliópolis, the two largest favelas in the City of São Paulo and two communities located in low-income neighborhoods (Cidade Tiradentes and Vila Brasilândia), the most affected by COVID-19 at the start of the study (Figure 1). The selection of the sampling sites had the support of the São Paulo State Sanitation Company (SABESP), which provided information about the sub-sewershed localization, flow rate and the respective population that contributes to the sewage discharge at the collection point, enabling the definition of sampling sites of each community. Paraisópolis does not have a sewage network infrastructure and monitoring was carried out by sampling surface water at a point in the stream that receives sewage discharge from the community. The other communities were monitored at manhole points of the collection network in sewer systems. Except for Cidade Tiradentes, where the collection point represents the entire sanitary sewage basin of the neighborhood, in the other locations, due to the complexity of the sanitary sewage system, points of easier and safer access, representative of the community, were established. The geographic and population characteristics of each area are shown in Table 1. The average flow rates were calculated from the micromeasured flow data of treated water.

Table 1

Wastewater sampling sites in low-income communities and estimated population residing in the area corresponding to each sub-sewershed

Sampling siteCollection pointGeographic coordinateAverage flow rate (L/day)Estimated population
Paraisópolis (slum) Stream 23° 36′28″ S
46° 43′11″ W 
9,070,000 51,000 
Heliópolis (slum) Sewer pipeline (manhole) 23° 36′38″ S
46° 35′11″ W 
2,060,000 9,800 
Cidade Tiradentes Sewer pipeline (manhole) 23° 33′36″ S
46° 25′06″ W 
23,100,000 130,000 
Vila Brasilândia Sewer pipeline (manhole) 23° 27′54″ S
46° 41′30″ W 
170,000 1,700 
Sampling siteCollection pointGeographic coordinateAverage flow rate (L/day)Estimated population
Paraisópolis (slum) Stream 23° 36′28″ S
46° 43′11″ W 
9,070,000 51,000 
Heliópolis (slum) Sewer pipeline (manhole) 23° 36′38″ S
46° 35′11″ W 
2,060,000 9,800 
Cidade Tiradentes Sewer pipeline (manhole) 23° 33′36″ S
46° 25′06″ W 
23,100,000 130,000 
Vila Brasilândia Sewer pipeline (manhole) 23° 27′54″ S
46° 41′30″ W 
170,000 1,700 
Figure 1

Sampling locations and associated sub-sewersheds for the four selected vulnerable urban communities in São Paulo city.

Figure 1

Sampling locations and associated sub-sewersheds for the four selected vulnerable urban communities in São Paulo city.

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Grab samples were collected once a week, during the period from May to September 2020. From October 2020 onwards, the frequency of sampling was reduced to fortnightly.

For viruses' analyses, wastewater samples were manually collected in stainless steel buckets and transferred to sterile 1 L polypropylene bottles (APHA; AWWA; WEF 2017a). Temperature and pH were measured during sampling. Samples were transported to the laboratory on ice and kept refrigerated (4 °C) before analyses for a maximum of 24 h. Additionally, volumes of 500 mL were collected for chemical assay of total suspended solids (TSS) (APHA; AWWA; WEF 2017).

COVID-19 and SARI data

Correlation analyses were performed between confirmed cases of COVID-19 and SARI (Severe Acute Respiratory Infection) in the resident population of the area corresponding to the sub-sewersheds and SARS-CoV-2 RNA viral loads in wastewater samples. Confirmed cases of COVID-19 were defined according to the following criteria: laboratory tests, clinical (flu-like syndrome cases associated with anosmia or acute ageusia), clinical–epidemiological (flu-like syndrome cases and SARI that had close contact with a confirmed COVID-19 patient during the 14 days before the appearance of the symptoms) and clinical imaging (flu-like syndrome cases and SARI with tomography diagnose) results. A unified database with geocoded information of the residence addresses of all cases was provided by the Municipal Health Department of São Paulo, which gathered data from the SIVEP-Gripe and e-SUS Notifica databases (https://www.prefeitura.sp.gov.br/cidade/secretarias/saude/vigilancia_em_saude/doencas_e_agravos/coronavirus/index.php?p=313773). Confirmed cases of COVID-19 and SARI were counted considering the symptom onset date.

Temporal evaluation of SARS-CoV-2 RNA viral load

Virus concentration by ultracentrifugation and glycine elution

The ultracentrifugation and glycine elution method was used to concentrate the wastewater samples (Pina et al. 1998). Volumes of 40 mL of each concentrated sample were aliquoted and contaminated with 106 copies of bovine coronavirus (BCoV). After incubation of 1 h at 4 °C, samples were ultracentrifuged at 110,000 g for 1 h at 4 °C using a Sorvall RC 90 ultracentrifuge.

The supernatant was discarded, and the pellet was eluted with 4 mL of 0.25 N glycine buffer (pH 9.5) in an ice bath for 30 min, mixing by vortex each 5 min. After adding 4 mL of 2×phosphate-buffered saline (PBS, pH 7.2), the samples were centrifuged at 3,000 g for 20 min at 4 °C. Then, the supernatant was transferred to a new tube, previously weighed. After ultracentrifugation at 110,000 g for 1 h at 4 °C, the supernatant was carefully discarded, and the tube weight was re-recorded.

The pellet eluted in the remaining supernatant (approximately 300 μL) was transferred to 1.5 mL microtubes. The samples were kept under refrigeration (2–8 °C) until RNA extraction.

RNA extraction and RT-qPCR for SARS-CoV-2 and BCoV

The concentrated sample (140 μL) was extracted with the QIAamp® Viral RNA Mini kit (Qiagen, Germany) according to the manufacturer's instructions. Elution was performed in two steps with 40 μL of elution buffer (AVE), adding up to nearly 80 μL. Nucleic acid extracts were stored at −70 °C until viral quantification by RT-qPCR.

The RT-qPCR for SARS-CoV-2 was performed by quantifying the N1 and N2 gene regions according to primers, probes and reaction parameters described by the CDC protocol (CDC 2020). In addition, BCoV RNA concentrations were determined using primers and the probe previously described by Decaro et al. (2008). The BCoV probe was labeled with HEX at the 5′ and Black Hole Quencher 1 (BHQ1) at the 3′ end.

RT-qPCR assays for N1 and N2 were performed separately in 20 μL reactions containing 5 μL TaqPath™ 1-Step RT-qPCR Master Mix, CG (ThermoFisher, MA, USA), 1.5 μL of N1 or N2 primer and probe (2019-nCoV RUO kit, IDT, IA, USA) and 5 μL of extracted RNA. The thermal cycling conditions of one-step RT-qPCR were: 25 °C for 2 min, reverse transcription at 50 °C for 15 min, preheating at 95 °C for 2 min, 45 cycles of amplification at 95 °C for 3 s and 55 °C for 30 s. BCoV RNA was quantified using 900 nM of each primer and 200 nM of probe at the same conditions of N1 and N2.

The N1 assay was performed in a multiplex reaction with an RNA internal positive control (IPC), with the addition of 0.8 μL of VetMAX™ Xeno™ Internal Positive Control VIC Assay (Applied Biosystems, ThermoFisher) and 0.1 μL of Xeno RNA Control (Applied Biosystems, Thermo Fisher Scientific, MA, USA). If the Cq value of a wastewater sample increases more than 1−Cq compared with the reference Cq value, the sample is considered to have PCR inhibitors.

Standard quantification curves for N1 and N2 SARS-CoV-2 regions were constructed using serial dilutions of the 2019-nCoV_N_Positive Control kit (IDT, IA, USA), consisting of 2×104 to 2 copies/μL of a plasmid cloned with the N1 and N2 sequences of SARS-CoV-2. BCoV standard curves were constructed with serial dilutions of synthetic fragments (GeneArt, Thermo Fisher Scientific, MA, USA) containing BCoV sequences ranging from 2.5×105 to 2.5 copies/μL. Negative template control was included in all qPCR assays. All reactions were performed in duplicate in a StepOne Plus instrument (ABI, CA, USA), and data were extracted from ABI StepOne Software version 2.3.

The assay limits of detection (LODs) were defined as the minimum copy number/reaction with a 95% probability of detection. Samples that had at least one RT-qPCR replicate amplified with a Cq value of >40 were considered detected but not quantifiable.

The concentrations of N1 and N2 of SARS-CoV-2 and BCoV were obtained by the following equation:
where RNAtotal is the total volume of RNA eluted (0.08 mL), RNAPCR is the volume of purified RNA tested in RT-qPCR (0.005 mL), concentratetotal is the total volume of water concentrate: Estimated volume of final concentrate=weight of tube with sample (g)−weight of tube (g), concentrateextracted is the volume of wastewater concentrate from which RNA was extracted (0.140 mL) and sample is the volume of original wastewater sample processed with ultracentrifugation procedure (40 mL).

To calculate the recovery efficiency of the total method, BCoV gene copies detected in the initial volume of 40 mL were divided by the BCoV gene copies spiked, and the result was expressed as a percentage.

Table 2 summarizes the RT-qPCR parameters obtained in the N1 and N2 SARS-CoV-2 and BCoV assays, for temporal evaluation of SARS-CoV-2 RNA concentrations in wastewater samples.

Table 2

Performance of N1 and N2 SARS-CoV-2 and M gene BCoV RT-qPCR assays from a composite of seven standard curves

Assay target geneRange
Genome copies/reaction
Efficiency (E) (%)Linearity (R2)SlopeY-interceptLOD
SARS-CoV-2 N1 91.3–101.8 0.994–0.998 −3.290 to −3.572 36.213–38.089 10 
SARS-CoV-2 N2 91.2–101.1 0.996–1.000 −3.296 to −3.552 37.608–39.304 10 
BcoV M gene 91.6–102.5 0.991–0.998 −3.262 to −3.511 45.593–48.782 12 
Assay target geneRange
Genome copies/reaction
Efficiency (E) (%)Linearity (R2)SlopeY-interceptLOD
SARS-CoV-2 N1 91.3–101.8 0.994–0.998 −3.290 to −3.572 36.213–38.089 10 
SARS-CoV-2 N2 91.2–101.1 0.996–1.000 −3.296 to −3.552 37.608–39.304 10 
BcoV M gene 91.6–102.5 0.991–0.998 −3.262 to −3.511 45.593–48.782 12 

Infectivity assay of SARS-CoV-2

Wastewater samples with N1 and N2 concentrations greater than 104 GC/L in the RT-qPCR assays were submitted to the SARS-CoV-2 viability assay in cell culture.

The samples were processed using two different methods: ultrafiltration with Centricon® Plus-70 and ultracentrifugation in sucrose cushion (Summer & Smith 1987).

Ultrafiltration – Centricon® Plus-70

Approximately 200 mL of water or wastewater sample were filtered through a 0.22 μm PES filter (Stericup, Merck Millipore, MA, USA). Then, 60 mL of the filtered sample were centrifuged at 3,000 g for 20 min in a Centricon® Plus-70 filter (10 kDa) (Merck Millipore, MA, USA). The procedure was repeated until the volume of concentrate was reduced to approximately 1 mL. The filter was inverted and the concentrate was recovered after centrifugation (1,000 g for 2 min) and treated with 10 μl of 10 mg/mL gentamicin (Gibco, China).

Ultracentrifugation in sucrose cushion

The volume of 60 mL of 0.22 μm filtered sample was carefully added to the ultracentrifuge tube containing 6 mL of sucrose solution (25% w/w sucrose, 5 mM NaCl, 10 mM EDTA). After ultracentrifugation at 55,000 g for 75 min at 4 °C, the supernatant was discarded, and the pellet was resuspended in 1 mL of PBS (pH 7.2). The sample was treated with 10 μl of 10 mg/mL gentamicin (Gibco, China).

An aliquot of 140 μL of the concentrated samples was subjected to nucleic acid extraction and N1/N2 RT-qPCR, and the remaining samples were stored at −80 °C and posteriorly sent to the Laboratory of the Federal University of São Paulo.

Cell culture for viral isolation

All experiments were conducted using two biological replicates and two technical duplicates. The experiments for viral isolation and initial passages were performed in a biosafety level 3 laboratory (BLS3), in accordance with WHO recommendations and under the laboratory biosafety guidance required for the SARS-CoV-2 at the BLS3 facilities at the Federal University of São Paulo.

Concentrated samples which previously tested positive for SARS-CoV-2 RNA were managed for viral isolation and cytopathic effect (CPE) observation in cell culture (Araujo et al. 2020). We used for the experiments the Vero E6 cell line (ATCC® CRL-1586™) maintained in Minimum Essential Medium (MEM; Gibco), supplemented with 10% fetal bovine serum (FBS) (Gibco) and 1% penicillin/streptomycin (Gibco). The Vero E6 cells were kept in a humidified 37 °C incubator in an atmosphere of 5% CO2. After reaching 80% confluent monolayer, we seeded cells in 24-well plates within a concentration of 5×105 cells for each well and in 6-well plates within a concentration of 7.5×105 cells for each well. After 24 h, for the 24-well plates, we removed the culture medium, washed the wells three times with PBS 1× and inoculated them with aliquots of 150 μL of MEM medium supplemented with 150 μL of concentrated wastewater sample. For the 6-well plates, 200 μL of the concentrated wastewater sample were added to 800 μL of supplemented MEM medium. For both experiments, after 3 h of incubation, the supernatants were removed to perform the N RT-qPCR assay (Corman et al. 2020) to evaluate eventually remaining virus in the supernatants after incubation. Then, the medium volumes in each well in 24 or 6-well plates were completed to 500 μL and 3 mL of MEM supplemented with 2.5% FBS and 1% penicillin–streptomycin, respectively. The Vero E6 cells were incubated under humidified 37 °C with a 5% CO2 atmosphere and were observed daily for CPE up to 6 days. The supernatant was collected, and virus replication was confirmed through CPE with an optical microscope and RT-qPCR for SARS-CoV-2. We used a positive control of SARS-CoV-2 for all experiments, kindly given by Prof. José Luiz Proença-Módena (University of Campinas – UNICAMP, SP, Brazil).

Statistical analysis

For statistical analysis, concentrations below the LODs were assigned a value equal to the LOD divided by the square root of 2 (Croghan & Egeghy 2003). The population-normalized viral loads (gene copies/capita/day) were estimate by multiplying (gene copies/L)×(L wastewater/day)×(1/sub-sewershed population).

The values of N1 and N2 RNA concentrations were transformed using the log10 function. The Wilcoxon test for paired data (Bauer 1972) was used to compare mean N1 concentrations for the two methods of concentration of SARS-CoV-2. The significance level adopted for the statistical tests was 5%.

Scatterplot diagrams were created to assess correlations between quantitative variables (recovery percentage and TSS). Linear models were fitted to assess the significance of correlations and trends. Confidence intervals of 95 and 99% were fitted for linear models. Multiple comparisons were tested using the statistics proposed by Dunn (1961).

Spearman correlations of population-normalized viral loads with new cases of COVID-19 (7-day cumulative) were calculated using IBM® SPSS® Statistics version 27.0, with a p-value statistically significant of <0.05. A cross-correlation analysis (Haugh 1976; Polanco-Martinez et al. 2019) was used to assess whether a time displacement of the COVID-19/SARI case dataset (7-day cumulative) impacts the strength of the correlation between N1 SARS-CoV-2 viral loads dataset quantified in wastewater samples. For viral loads, the frequency was fortnightly and when more than one data was available in the fortnight, the mean values were used. Analyses were performed using the R language with RStudio.

SARS-CoV-2 sequencing

Whole viral genome sequencing was carried out by a Brazilian reference laboratory located at the Instituto Adolfo Lutz (IAL, Center for Interdisciplinary Procedures, Strategic Laboratory). The partnership for sample sequencing was only made possible from March 2021, and therefore, only samples with concentrations above 105 GC N1 L−1, collected from that date onwards, were sequenced. An aliquot of extracted RNA underwent a retrotranscription (RT) step where cDNA was synthesized using the SuperScript IV VILO Master Mix according to the manufacturer's instructions (Thermo Fisher Scientific, MA, USA). Fifteen microliters of cDNA were used to amplify SARS-CoV-2 full-length genome using an AmpliSeq SARS-CoV-2 panel (Thermo Fisher Scientific, MA, USA) (Alessandrini et al. 2020). The library was adjusted to 30 pM and then loaded onto an Ion Chef instrument (Thermo Fisher Scientific, MA, USA) for emulsion PCR, enrichment and loading onto an Ion 530 chip on the Ion GeneStudio S5 Prime Series system (Thermo Fisher Scientific, MA, USA). Reads from the library were aligned with the Wuhan-Hu-1 NCBI Reference Genome (Accession No. MN908947.3) in Torrent Suite v. 5.10.1. For mutation calling, the following plugins were used: Coverage Analysis (v. 5.10.0.3), Variant Caller (v.5.10.1.19) and COVID19AnnotateSnpEff (v.1.0.0), a plugin specifically developed for SARS-CoV-2 that can predict the effect of base substitution (Alessandrini et al. 2020). The software Geneious R9 was used to visualize each sample's torrent variant caller (TVC) bam files to check the consistency of nucleotide calls and analyze sequencing data. Raw sequence reads were aligned to the complete genome of the SARS-CoV-2 Wuhan-Hu-1 isolate (Genbank Accession No. NC_045512.2).

The resulting contigs were subjected to a blast search using Ugene software (Okonechnikov et al. 2012) to identify members of the betacoronavirus genera. To classify and determine the mutational pattern of our sequences, NextClade v1.5.0 clade assigner (https://clades.nextstrain.org/) was used.

SARS-CoV-2 RNA quantification

We detected N1 or N2 of SARS-CoV-2 RNA in 153 (95.6%) of the 160 wastewater samples analyzed over the study period. Of these 153 samples, 150 (93.8%) had quantifiable concentrations of SARS-CoV-2 RNA. The frequency of positive samples in each community and the SARS-CoV-2 RNA concentration ranges are summarized in Table 3.

Table 3

Frequency, average and range concentrations of SARS-CoV-2 RNA (N1 and N2) in wastewater samples at four vulnerable communities of São Paulo city, Brazil

Sampling siteNumber of analyzed samplesFrequency of positive samples (%)
SARS-CoV-2 RNA Average (range) genome copies/L
N1N2N1N2
Paraisópolis 40 97.5 100 9.7×104
(<LOD – 7.2×105
5.5×104
(DNQ – 5.8×105
Heliópolis 41 97.6 100 1.5×105
(<LOD – 1.7×106
6.3×104
(2.1×103–8.4×105
Cidade Tiradentes 40 100 100 1.2×105
(1.3×104–5.8×105
6.0×104
(4.9×103–5.8×105
Brasilândia 39 76.9 76.9 4.1×104
(<LOD – 2.7×105
2.6×104
(<LOD – 2.0×105
Sampling siteNumber of analyzed samplesFrequency of positive samples (%)
SARS-CoV-2 RNA Average (range) genome copies/L
N1N2N1N2
Paraisópolis 40 97.5 100 9.7×104
(<LOD – 7.2×105
5.5×104
(DNQ – 5.8×105
Heliópolis 41 97.6 100 1.5×105
(<LOD – 1.7×106
6.3×104
(2.1×103–8.4×105
Cidade Tiradentes 40 100 100 1.2×105
(1.3×104–5.8×105
6.0×104
(4.9×103–5.8×105
Brasilândia 39 76.9 76.9 4.1×104
(<LOD – 2.7×105
2.6×104
(<LOD – 2.0×105

DNQ, detected but not quantifiable; LOD, limit of detection.

The N2 RT-qPCR detected SARS-CoV-2 RNA in 152 (95%) samples, while for the N1 RT-qPCR assay, 149 (93%) samples were positive. No significant differences were observed between the concentrations (genome copies/L) of N1 and N2 in wastewater samples, although N1 values were systematically higher than N2. The linear model (GAMLSS) relating N1 to N2 concentrations presented an R2 value of 99.85%. Therefore, only results related to N1 data were presented for statistical analyses.

Concentration methods

Preliminary tests were performed to select the most effective concentration method for recovering SARS-CoV-2 RNA from wastewater samples (n=13) naturally contaminated. The ultracentrifugation and glycine elution method was compared with the ultrafiltration method using Centricon® Plus-70 (10 kDa). Statistical analysis (Wilcoxon test) showed that the ultracentrifugation results were statistically superior to ultrafiltration (W=85.0; p=0.003418; Figure 2).

Figure 2

Comparison between log-transformed N1 concentration (copies/L) results in wastewater samples concentrated by ultracentrifugation (with glycine elution) and ultrafiltration with Centricon® Plus-70 methods.

Figure 2

Comparison between log-transformed N1 concentration (copies/L) results in wastewater samples concentrated by ultracentrifugation (with glycine elution) and ultrafiltration with Centricon® Plus-70 methods.

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Recovery efficiency

The recovery efficiency using BCoV control was evaluated only in samples ultracentrifuged with glycine elution. Considering all the results obtained in the laboratory including other sampling locations, with the analysis of wastewater samples (n=267), the average recovery percentage was 8.3% (s.d. 11.7%). The characteristics of the different sample matrices significantly influenced the results. Statistical analysis showed that these differences in recovery efficiencies might be associated with variations in TSS. Samples with higher amounts of TSS showed lower recovery rates of BCoV (t=−4,933 with g.l.=335; p<0.001; 95% confidence interval (−0.3570 to −0.1580); Figure 3).

Figure 3

Influence of the variable total suspended solids amount (TSS) on the recovery efficiencies of SARS-CoV-2 RNA by ultracentrifugation and the glycine elution method.

Figure 3

Influence of the variable total suspended solids amount (TSS) on the recovery efficiencies of SARS-CoV-2 RNA by ultracentrifugation and the glycine elution method.

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RT-qPCR inhibition

All the N1 SARS-CoV-2 assays were performed in multiplex RT-qPCR with an internal RNA positive control to ascertain inhibitors' interference present in the RNA samples. The presence of inhibitors in the samples was not observed since the IPC amplification results did not show differences greater than 1 Cq compared to the reference control IPC. RNA extracts (n=9) were also analyzed in different volumes (5, 1 and 0.5 μL) by N1 and N2 RT-qPCR, and no significant differences were observed between the values of copies/L concentrations.

SARS-CoV-2 RNA in wastewater samples and COVID-19/SARI cases

The evolution of population-normalized SARS-CoV-2 loads observed in wastewater from May 2020 to June 2021 in the four communities is shown in Figure 4(a). The periods of highest concentrations (May to June 2020, December 2020 to January 2021 and March 2021) coincided with records of an increase in new cases of COVID-19 in the city of São Paulo (Figure 4(b)). The alert levels of the São Paulo Coronavirus Control Plan that were in force during the study period are also plotted in Figure 4(b) (see Supplementary material for classifying criteria).

Figure 4

Population-normalized SARS-CoV-2 loads for the four communities (a) and new cases and 7-day moving average new cases (7dMA) of COVID-19 (b).

Figure 4

Population-normalized SARS-CoV-2 loads for the four communities (a) and new cases and 7-day moving average new cases (7dMA) of COVID-19 (b).

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Considering the population-normalized viral load data in the wastewater of each community (Figure 5), we observed a greater similarity to the new cases of temporal variations only in the Cidade Tiradentes and Paraisópolis communities, which correspond to the sub-sewersheds of the largest contributing population. This observation was confirmed by the statistical analysis of the Spearman correlation, in which significant positive correlations were observed in both communities (p<0.001) (Figure 6).

Figure 5

Population-normalized SARS-CoV-2 RNA viral loads in wastewater and number of COVID-19/SARI cases in vulnerable communities from São Paulo city, sampled between May 2020 and June 2021. Hollow dots indicate samples below the limit of detection.

Figure 5

Population-normalized SARS-CoV-2 RNA viral loads in wastewater and number of COVID-19/SARI cases in vulnerable communities from São Paulo city, sampled between May 2020 and June 2021. Hollow dots indicate samples below the limit of detection.

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Figure 6

Spearman's correlation between population-normalized SARS-CoV-2 viral load and weekly new cases of COVID-19 from (a) Cidade Tiradentes and (b) Paraisópolis data.

Figure 6

Spearman's correlation between population-normalized SARS-CoV-2 viral load and weekly new cases of COVID-19 from (a) Cidade Tiradentes and (b) Paraisópolis data.

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To assess the differences in the behavior of the temporal curves, the viral loads of SARS-CoV-2 RNA were correlated to the curves of the number of 7-day cumulative cases for each community using a cross-correlation analysis. Although the viral load curves in wastewater and reported cases of COVID-19/SARI presented similar trends in different periods, no significant correlation was observed considering the entire study period (Figure 7). Only time series of Cidade Tiradentes, for lag 0, the correlation coefficient was close to the limit of 95%; however, the representativeness was not guaranteed, since it did not reach robust CI.

Figure 7

Cross-correlation analysis between SARS-CoV-2 RNA viral loads and 7-day cumulative cases of COVID-19/SARI time series, in four vulnerable communities of São Paulo city, from May 2020 to June 2021.

Figure 7

Cross-correlation analysis between SARS-CoV-2 RNA viral loads and 7-day cumulative cases of COVID-19/SARI time series, in four vulnerable communities of São Paulo city, from May 2020 to June 2021.

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Virus isolation

The positive concentrated samples for N1 and N2 SAR-CoV-2 RT-qPCR were inoculated in the previously prepared plates of Vero E6 cells. The samples were frozen and then used for virus isolation with daily observation for CPE by optical microscopy. Twenty-four hours post-infection (24 h.p.i.), all samples were compared with the positive control, and no morphological changes were observed. Three days post-infection (3 d.p.i), the cultures continued without any sign of CPE. After 6 d.p.i, no virus propagation was observed by CPE in the cultures, but all supernatants were collected. Samples of wastewater tested in the Vero E6 cell line had no sign of virus infectivity (Figure 8(a) and 8(d)). For all independent experiments performed in cell culture, CPE was observed in positive controls for the SARS-CoV-2, and negative controls had no signs of morphological changes (Figure 8(e) and 8(f), respectively). RT-qPCR further confirmed these results, corroborating the lack of SARS-CoV-2 detection in the 6 d.p.i culture supernatants (Table 4). Similar results were observed for negative control samples. Positive controls used in the Vero E6 cells presented clear CPE and SARS-CoV-2 RNA detection in the supernatant by RT-qPCR using gene N as target.

Table 4

SARS-CoV-2 RNA concentrations in wastewater samples submitted to infectivity assay in Vero E6 cells, and the corresponding results of evaluating the presence of CPE and the detection of SARS-CoV-2 replication by RT-qPCR

Sample IDSampling siteSampling dateConcentration methodSARS-CoV-2, N1 copies/L (Cq)aCell culture (CPE-SARS-CoV-2)SARS-CoV-2 N gene detection post- infection
2006032 Paraisópolis 26/05/2020 Ultrafiltration 2.1×104 (35.4) Negative Negative 
2006073 Cidade Tiradentes 02/06/2020 Ultrafiltration 4.6×104 (32.9) Negative Negative 
2006075 Heliópolis 02/06/2020 Ultrafiltration 8.9×104 (34.4) Negative Negative 
2006199 Heliópolis 16/06/2020 Ultrafiltration 1.1×106 (30.4) Negative Negative 
2006201 Cidade Tiradentes 15/06/2020 Ultrafiltration 1.6×106 (30.0) Negative Negative 
2006250 Cidade Tiradentes 22/06/2020 Ultrafiltration 1.5×105 (33.9) Negative Negative 
2006248 Heliópolis 23/06/2020 Ultrafiltration 7.1×104 (35.8) Negative Negative 
2006251 Paraisópolis 23/06/2020 Ultrafiltration 1.1×105 (33.2) Negative Negative 
2011128 Cidade Tiradentes 14/12/2020 Ultrafiltration 2.8×105 (31.7) Negative Negative 
Sucrose cushion ultracentrifugation 6.2×104 (34.1) Negative Negative 
2011132 Heliópolis 15/12/2020 Ultrafiltration 8.8×105 (30.1) Negative Negative 
Sucrose cushion ultracentrifugation 1.1×105 (33.2) Negative Negative 
2103497 Paraisópolis 15/03/2021 Ultrafiltration 2.4×105 (32.0) Negative Negative 
Sucrose cushion ultracentrifugation 2.6×105 (31.8) Negative Negative 
Sample IDSampling siteSampling dateConcentration methodSARS-CoV-2, N1 copies/L (Cq)aCell culture (CPE-SARS-CoV-2)SARS-CoV-2 N gene detection post- infection
2006032 Paraisópolis 26/05/2020 Ultrafiltration 2.1×104 (35.4) Negative Negative 
2006073 Cidade Tiradentes 02/06/2020 Ultrafiltration 4.6×104 (32.9) Negative Negative 
2006075 Heliópolis 02/06/2020 Ultrafiltration 8.9×104 (34.4) Negative Negative 
2006199 Heliópolis 16/06/2020 Ultrafiltration 1.1×106 (30.4) Negative Negative 
2006201 Cidade Tiradentes 15/06/2020 Ultrafiltration 1.6×106 (30.0) Negative Negative 
2006250 Cidade Tiradentes 22/06/2020 Ultrafiltration 1.5×105 (33.9) Negative Negative 
2006248 Heliópolis 23/06/2020 Ultrafiltration 7.1×104 (35.8) Negative Negative 
2006251 Paraisópolis 23/06/2020 Ultrafiltration 1.1×105 (33.2) Negative Negative 
2011128 Cidade Tiradentes 14/12/2020 Ultrafiltration 2.8×105 (31.7) Negative Negative 
Sucrose cushion ultracentrifugation 6.2×104 (34.1) Negative Negative 
2011132 Heliópolis 15/12/2020 Ultrafiltration 8.8×105 (30.1) Negative Negative 
Sucrose cushion ultracentrifugation 1.1×105 (33.2) Negative Negative 
2103497 Paraisópolis 15/03/2021 Ultrafiltration 2.4×105 (32.0) Negative Negative 
Sucrose cushion ultracentrifugation 2.6×105 (31.8) Negative Negative 

aQuantification prior to inoculation in cell culture.

Figure 8

Wastewater samples tested in Vero E6 cells, observed under optical microscopy. (a,b) Cells infected with the wastewater samples concentrated by ultrafiltration with Centricon® Plus-70 and (c,d) ultracentrifugation in sucrose cushion. The samples of wastewater tested in the cell culture had no sign of virus infectivity. (e,f) Cells infected with the SARS-CoV-2 virus and uninfected cells, respectively.

Figure 8

Wastewater samples tested in Vero E6 cells, observed under optical microscopy. (a,b) Cells infected with the wastewater samples concentrated by ultrafiltration with Centricon® Plus-70 and (c,d) ultracentrifugation in sucrose cushion. The samples of wastewater tested in the cell culture had no sign of virus infectivity. (e,f) Cells infected with the SARS-CoV-2 virus and uninfected cells, respectively.

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SARS-CoV-2 genomes in wastewater samples

We performed NGS on three ultracentrifuged (with glycine elution) wastewater samples, and we were able to recover only one nearly complete genome of SARS-CoV-2. The results of the BlastN search using these genomes indicated that our sequences presented similarities ranging from 96.0 to 99.9% with SARS-CoV-2 reference sequences. Based on the SARS-CoV-2 Spike region (3,822 bp), we performed genotyping of the sequences identified in this study (Table 5). According to the clade assignment of the NextClade application, sequences 915 and 920 belong to the clade 20 J (also known as lineage P.1 or variant gamma).

Table 5

Genotyping of SARS-CoV-2 detected in wastewater samples by illumina sequencing

Sample IDSampling siteSampling dateN1 (copies/L) CqGenome coverage (%)NextCladea
Complete genomeOrf1abSpike
915 Paraisópolis 10/05/2021 2.0×105 32.4 99.6 20 J (Gamma, V3) 20B 20 J (Gamma, V3) 
918 Heliópolis 09/03/2021 2.4×105 32.1 97.1 NA NA NA 
920 Paraisópolis 29/03/2021 1.9×105 32.4 80 NA NA 20 J (Gamma, V3) 
Sample IDSampling siteSampling dateN1 (copies/L) CqGenome coverage (%)NextCladea
Complete genomeOrf1abSpike
915 Paraisópolis 10/05/2021 2.0×105 32.4 99.6 20 J (Gamma, V3) 20B 20 J (Gamma, V3) 
918 Heliópolis 09/03/2021 2.4×105 32.1 97.1 NA NA NA 
920 Paraisópolis 29/03/2021 1.9×105 32.4 80 NA NA 20 J (Gamma, V3) 

NA, genotype not assigned due to poor quality of the sequence.

aNextClade v1.5.0 clade assigner (https://clades.nextstrain.org/).

This study presents data from SARS-CoV-2 monitoring in wastewater samples from vulnerable communities (slums and low-income neighborhoods) located in São Paulo city. During the 1-year study from May 2020 to June 2021, SARS-CoV-2 RNA (N1 or N2) was detectable in all samples from Cidade Tiradentes (n=40, ∼130,000 individuals), Paraisópolis (n=40, ∼51,000 individuals) and Heliópolis (n=41, ∼10,000 individuals), and in 30 of a total of 39 from Vila Brasilândia, with an estimated population of 1,700 individuals contributing to sub-sewershed. The temporal variations of SARS-CoV-2 RNA were evaluated in ultracentrifuged wastewater samples, and the N1 and N2 concentrations ranged from 1×103 to 1×106 gene copies/L (Table 3). These values are within the range of SARS-CoV-2 RNA concentrations reported in wastewater around the world (https://www.covid19wbec.org/covidpoops19; https://sphere.waterpathogens.org/map).

Ultracentrifugation and glycine elution was the method of choice to determine the time series of SARS-CoV-2 RNA concentrations as it resulted in significantly higher SARS-CoV-2 N1 concentrations than the Centricon® Plus-70 ultrafiltration method (p<0.001) (Figure 2). Some authors, when comparing the recovery results of SARS-CoV-2 or enveloped viruses (MHV, BCoV, PMMoV) obtained by different concentration methods, observed that ultrafiltration using Centricon® Plus-70 had better performance than other methods such as CP-Select™ ultrafiltration (Forés et al. 2021), PEG 8000 precipitation (Gerrity et al. 2021), Amicon, Macrosep, Vivaspin ultrafiltration, PEG 8000 precipitation (Boogaerts et al. 2021), PEG 6000 precipitation (Bertrand et al. 2021) and the adsorption–elution method using electronegative membranes (Sherchan et al. 2020). Our comparative study was based on a few samples (n=13) previously filtered on 0.22 μm membranes (not centrifuged) before the Centricon concentration step, a difference that may have reduced the effectiveness of the method but was necessary for the viability test in cell culture.

Few studies have comparatively evaluated the ultracentrifugation method. Ahmed et al. (2020) compared seven different concentration methods and had better recovery efficiency of MHV in ultracentrifugation (33.5±12.1%) than Centricon® Plus-70 ultrafiltration (28.0±9.10%), although the most efficient was the adsorption–extraction method with MgCl2 pretreatment (65.7±23.8%). Prado et al. (2021) reported mean recoveries of 27.4±8.64% of BRSV, using the same ultracentrifugation method in the wastewater sample concentration. The viral recoveries reported within this study (8.3±11.7%) are in line with those reported by other comparative studies with recoveries of BCoV of no greater than 20% for the majority of samples (Jafferali et al. 2021; Philo et al. 2021; Wilder et al. 2021).

The ultracentrifugation technique employed in this work was originally developed to concentrate non-enveloped enteric viruses that exhibit lower partitioning to solids present in wastewater compared to enveloped viruses (Ye et al. 2016). Due to the strong interaction of enteric viruses with solid particles, the considerable viral loss probably occurs during the glycine elution step followed by centrifugation. This could explain the lower recovery rates in samples with higher amounts of TSS.

SARS-CoV-2 (N1 and N2 gene regions) was detected consistently throughout the monitoring period, except in Vila Brasilândia, where the virus was not found in 23% of the samples, probably as a consequence of false-negative results due to the low flow rate (Weidhaas et al. 2021).

The SARS-CoV-2 densities were greater than 4 log10 GC/L for about 80% of the samples, with 36% of these showing values greater than 5 log GC/L. Heliópolis was the place that showed the highest concentrations of viral RNA, 39% of the values were above 5 log GC/L, with a peak in December (1.7×106 GC/L), followed by Cidade Tiradentes (32.5%), Paraisópolis (30%) and Vila Brasilândia (12.8%) (Supplementary Fig. S1). These data are in line with those reported in other Brazilian cities (Claro et al. 2021; Mota et al. 2021; Prado et al. 2021).

Comparing the population-normalized viral load in the four monitored sites, both slums, Heliópolis and Paraisópolis, presented higher values of N1/capita/day over the course of the 1-year study (Figure 4(a)). Trends in viral load showed an increase in three different periods, at the beginning of the monitoring (May/June 2020), at Christmas time (December 2020/January 201) and after the carnival holiday (March 2021), which coincide with the peaks of the curve of COVID-19 new cases (daily and 7-day moving average (dMA)) reported by the São Paulo city (Figure 4(b)). After July, new COVID-19 cases and deaths decreased, but the facilitation of preventive measures in mid-October 2020 (partial opening) increased the number of daily deaths cases, exceeding 1,000 per day in mid-December (Ministério da Saúde 2020). The emergency of the new SARS-CoV-2 variant lineage P.1 in January 2021 in the city of Manaus, with more significant transmission potential, added to a greater people circulation in Carnival (mid-February) led to a new peak of cases and deaths in March.

However, when we look at the sub-sewershed level, trends in SARS-CoV-2 wastewater loads versus COVID-19 cases are distinct (Figure 5). At Cidade Tiradentes and Paraisópolis, the wastewater viral loads followed a trend similar to new cases of COVID-19 (daily and 7 dMA) reported for the population of these watersheds over the monitoring time period (p<0.001) (Figure 6); the same was not observed for the other two communities. Also, the cross-correlation analysis, considering the viral loads of SARS-CoV-2 RNA and the 7-day cumulative COVID-19 cases for each community, showed no significant temporal dependence among the COVID-19 cases and the respective virus load for the four communities. Although it was not possible to establish a model for predicting cases of COVID-19 in these communities based on the SARS-CoV-2 data in the respective sewersheds, following the trends of SARS-CoV-2 wastewater loads in the four vulnerable communities allows the Public Health Service to have a more refined picture of infected individuals circulating within these sewersheds.

It is important to mention some limitations in our study. The monitoring was conducted with weekly or fortnightly sampling with weekly or biweekly frequency, which may have reduced the representativeness of SARS-CoV-2 RNA concentrations, especially in the Brasilândia and Heliópolis communities. The sampling frequency is one important aspect to identifying the COVID-19 incidence dynamics. Some studies indicated that the least resource-intensive sampling scheme that maintained a high degree of confidence in capturing case trends was two nonconsecutive days per week (Feng et al. 2021; Graham et al. 2021). Another important aspect is that the lack of reliable data on COVID-19 cases does not allow a more accurate comparison with SARS-CoV-2 wastewater surveillance results. This issue is mainly due to the high rate of underreported cases, resulting from the limitation in the number of tests for COVID-19, with rates much lower than in developed countries (https://www.worldometers.info/coronavirus/). Veiga Silva et al. (2020) estimated an underreporting rate of deaths of 35.28% in the city of São Paulo, considering data up to May 2020. Kupek (2021) estimated an underreporting rate of deaths by COVID-19 of 22.62% in 2020 in Brazil. Furthermore, the underreporting rate is more accentuated in low-income communities due to the limited availability of tests in the public health system (de Souza et al. 2020). Another limiting factor for comparing data is the considerable percentage of missing data (date of initial symptoms, ZIP code), especially in slum residents.

A few studies have evaluated the infectivity in cell cultures of SARS-CoV-2 isolated from water and sewage samples (Rimoldi et al. 2020; Razzolini et al. 2021; Westhaus et al. 2021), and as reported in the present study, the presence of infectious SARS-CoV-2 was also not evidenced. Factors like the propagation process, susceptibility of cells and incubation time may affect the sensitivity of viral culture. Furthermore, pretreatment processes by filtration and concentration of samples (Giacobbo et al. 2021), as well as storage and freezing–thawing procedures, can also contribute to the inactivation of SARS-CoV-2 particles. Therefore, regarding viral culture, there is a lack of standardized methods for the recovery of viable SARS-CoV-2.

Although studies have shown that viral particles of SARS-CoV-2 remain viable at 20–24 °C in river water samples (1.9–2.3 days, T90) and sewage (1.2–1.6 days, T90) experimentally contaminated (Bivins et al. 2020; de Oliveira et al. 2021; Sala-Comorera et al. 2021), many factors contributed to the inactivation of SARS-CoV-2 in environmental waters such as temperature, pH, exposure to UV, antagonistic microorganisms and presence of disinfectants (La Rosa et al. 2020; Giacobbo et al. 2021; Paul et al. 2021; Tran et al. 2021). However, due to the high viral load released into the environment, especially in communities without access to a sewage collection network, the low rate of sewage treatment in Brazil (SNIS 2020) and the low rate of virus removal in conventional sewage treatments from activated sludge processes, there is a clear need for further investigation to clarify the real microbiological risk associated with waterborne transmission of COVID-19 (Kumar et al. 2021).

The preliminary sequencing analyses detected the circulating variants, identified as the 20 J (P.1) variant, in two of the three samples evaluated. However, the complete virus genome was achieved in only one sample. Additional tests are needed to assess the sequencing approach's feasibility, including the RNA concentration and RNA purification steps. The results agreed with the SARS-CoV-2 genomic surveillance data in clinical samples conducted during the same period of collection of wastewater samples. In São Paulo city, the P.1 variant was identified in 64.4% of the 73 samples analyzed in the first week of March 2021 (http://www.prefeitura.sp.gov.br/cidade/secretarias/upload/saude/situacao_covid19_03_26_03_2021.pdf). Considering the variants circulating in the metropolitan São Paulo in 2021, P.1 was predominant in March (84.6%), April (94.7%) and May (96.3%) (https://butantan.gov.br/assets/files/Covid/Boletim_epidemiologico/relatorio.pdf).

SARS-CoV-2 wastewater surveillance proved to be an efficient tool to detect and monitor trends and the prevalence of COVID-19 cases in communities, as demonstrated in several studies worldwide carried out from wastewater analysis (WWTP, sub-sewershed and facilities) and surface water (Medema et al. 2020a). In low-income communities with high rates of transmission and underreporting cases, SARS-CoV-2 WBE has an even more critical role because it can provide information on infection trends without being influenced by the availability and access to clinical testing resources or data on healthcare-seeking behavior.

This study demonstrated that in vulnerable communities such as low-income neighborhoods and slums, where underreporting rates are pronounced, wastewater surveillance could be an essential tool to provide reliable indicators of COVID-19 prevalence, despite the difficulty of validating the strategy and demonstrating its value to clinical surveillance.

The ultracentrifugation with the glycine method showed greater sensitivity in quantifying SARS-CoV-2 compared to the Centricon® Plus-70 ultrafiltration method. We observed that the different characteristics of wastewater matrices influenced the concentration method recovery. The total solids amount negatively influenced the recovery efficiency.

Wastewater-based genomic epidemiology can provide complementary and more comprehensive data on the circulation of variants in a community. Preliminary results of NGS sequencing evidenced the circulation of the P.1 lineage, the predominant variant in the area and periods analyzed. SARS-CoV-2 RNA positive sewage samples showed no replication of SARS-CoV-2 in Vero E6 cell culture, which reinforces the evidence suggesting a low risk of transmission of SARS-CoV-2 by wastewater contamination.

The authors thank the Fundação de Amparo à Pesquisa do Estado de São Paulo – FAPESP: Brazil (2020/08943-5) (C.T.B, J.T.M. and L.M.R.J) and to the Conselho Nacional de Desenvolvimento Científico e Tecnológico – CNPq: 405691/2018-1 (C.T.B); we thank the CETESB team from Sampling Division and Inorganic Chemistry Laboratory; José Augusto de Areas, Ivo Freitas de Oliveira and Ana Maria Brokelmann (CETESB) for assistance in the wastewater samples processing; Paulo Brandão (Faculty of Veterinary Medicine and Animal Science of University of São Paulo) for kindly providing the BCoV control; Cláudio Tavares Sacchi (IAL – Center for Interdisciplinary Procedures, Strategic Laboratory) for sequencing and Ana Carolina Aguiar de Carvalho (City Hall of São Paulo, Acute Communicable Diseases Nucleus, Health Surveillance Coordination) for sharing the SIVEP/e-SUS database.

The authors declare that there are no competing interests in this article.

All relevant data are included in the paper or its Supplementary Information.

Ahmed
W.
,
Bertsch
P. M.
,
Bivins
A.
,
Bibby
K.
,
Farkas
K.
,
Gathercole
A.
,
Haramoto
E.
,
Gyawali
P.
,
Korajkic
A.
,
McMinn
B. R.
,
Mueller
J. F.
,
Simpson
S. L.
,
Smith
W. J. M.
,
Symonds
E. M.
,
Thomas
K. V.
,
Verhagen
R.
&
Kitajima
M.
2020
.
Comparison of virus concentration methods for the RT-qPCR-based recovery of murine hepatitis virus, a surrogate for SARS-CoV-2 from untreated wastewater
.
Science of Total Environment
739
,
139960
.
https://doi.org/10.1016/j.scitotenv.2020.139960
Ahmed
W.
,
Tscharke
B.
,
Bertsch
P. M.
,
Bibby
K.
,
Bivins
A.
,
Choi
P.
,
Clarke
L.
,
Dwyer
J.
,
Edson
J.
,
Nguyen
T. M. H.
,
O'Brien
J. W.
,
Simpson
S. L.
,
Sherman
P.
,
Thomas
K. V.
,
Verhagen
R.
,
Zaugg
J.
&
Mueller
J. F.
2021
SARS-CoV-2 RNA monitoring in wastewater as a potential early warning system for COVID-19 transmission in the community: a temporal case study
.
Science of the Total Environment
761
,
144216
.
https://doi.org/10.1016/j.scitotenv.2020.144216
.
Alessandrini
F.
,
Caucci
S.
,
Onofri
V.
,
Melchionda
F.
,
Tagliabracci
A.
,
Bagnarelli
P.
,
Di
S. L.
,
Turchi
C.
&
Menzo
S.
2020
Evaluation of the Ion AmpliSeq SARS-CoV-2 Research Panel by massive parallel sequencing
.
Genes
11
(
8
),
1
14
.
https://doi.org/10.3390/genes11080929
.
ANA Agência Nacional de Águas
2021
Monitoramento COVID Esgotos – Painel Monitoramento COVID em Esgotos (COVID Sewage Monitoring – COVID Sewage Monitoring Dashboard)
.
Report for Agency of Water and Sanitation (ANA)
,
Brasília
,
Brazil
. .
APHA; AWWA; WEF
2017a
Standard Methods for the Examination of Water and Wastewater: 1060 Collection and Preservation of Samples
, 23rd edn.
Washington, DC.
APHA; AWWA; WEF
2017b
Standard Methods for the Examination of Water and Wastewater: 2540 D Total Suspended Solids Dried from 103 to 105 °C
, 23rd edn.
Washington, DC
.
Araujo
D. B.
,
Machado
R. R. G.
,
Amgarten
D. E.
,
Malta
F. d. M.
,
de Araujo
G. G.
,
Monteiro
C. O.
,
Candido
E. D.
,
Soares
C. P.
,
de Menezes
F. G.
,
Pires
A. C. C.
,
Santana
R. A. F.
,
Viana
A. d. O.
,
Dorlass
E.
,
Thomazelli
L.
,
Ferreira
L. C. d. S.
,
Botosso
V. F.
,
Carvalho
C. R. G.
,
Oliveira
D. B. L.
,
Pinho
J. R. R.
&
Durigon
E. L.
2020
SARS-CoV-2 isolation from the first reported patients in Brazil and establishment of a coordinated task network
.
Memorias Do Instituto Oswaldo Cruz
115
(
12
),
1
8
.
https://doi.org/10.1590/0074-02760200342
.
Asghar
H.
,
Diop
O. M.
,
Weldegebriel
G.
,
Malik
F.
,
Shetty
S.
,
El Bassioni
L.
,
Akande
A. O.
,
Al Maamoun
E.
,
Zaidi
S.
,
Adeniji
A. J.
,
Burns
C. C.
,
Deshpande
J.
,
Oberste
M. S.
&
Lowther
S. A.
2014
Environmental surveillance for polioviruses in the global polio eradication initiative
.
Journal of Infectious Diseases
210
(
suppl 1
),
S294
S303
.
https://doi.org/10.1093/infdis/jiu384
.
Bauer
D. F.
1972
Constructing confidence sets using rank statistics
.
Journal of the American Statistical Association
67
(
339
),
687
690
.
https://doi.org/10.2307/2284469
.
Bertrand
I.
,
Challant
J.
,
Jeulin
H.
,
Hartard
C.
,
Mathieu
L.
,
Lopez
S.
,
Schvoerer
E.
,
Courtois
S.
&
Gantzer
C.
2021
Epidemiological surveillance of SARS-CoV-2 by genome quantification in wastewater applied to a city in the northeast of France: comparison of ultrafiltration- and protein precipitation-based methods
.
International Journal of Hygiene and Environmental Health
233
.
https://doi.org/10.1016/j.ijheh.2021.113692
Bivins
A.
,
Greaves
J.
,
Fischer
R.
,
Yinda
K. C.
,
Ahmed
W.
,
Kitajima
M.
,
Munster
V. J.
&
Bibby
K.
2020
Persistence of SARS-CoV-2 in water and wastewater
.
Environmental Science and Technology Letters
7
(
12
),
937
942
.
https://doi.org/10.1021/acs.estlett.0c00730
.
Boogaerts
T.
,
Jacobs
L.
,
De Roeck
N.
,
Van den Bogaert
S.
,
Aertgeerts
B.
,
Lahousse
L.
,
van Nuijs
A. L. N.
&
Delputte
P.
2021
An alternative approach for bioanalytical assay optimization for wastewater-based epidemiology of SARS-CoV-2
.
Science of the Total Environment
789
,
148043
.
https://doi.org/10.1016/J.SCITOTENV.2021.148043
.
CDC
2020
US CDC RT-PCR-Panel-for-Detection-Instructions V20.01.27.
pp.
1
12
.
CDC
2021
National Wastewater Surveillance System (NWSS)
.
Waterborne Disease & Outbreak Surveillance
.
Claro
I. C. M.
,
Cabral
A. D.
,
Augusto
M. R.
,
Duran
A. F. A.
,
Graciosa
M. C. P. G.
,
Fonseca
F. L. A.
,
Speranca
M. A.
&
Bueno
R. F.
2021
Long-term monitoring of SARS-COV-2 RNA in wastewater in Brazil: a more responsive and economical approach
.
Water Research
203
,
117534
.
https://doi.org/10.1016/j.watres.2021.117534
.
Corman
V. M.
,
Landt
O.
,
Kaiser
M.
,
Molenkamp
R.
,
Meijer
A.
,
Chu
D. K. W.
,
Bleicker
T.
,
Schneider
J.
,
Schmidt
M. L.
,
Mulders
D. G. J. C.
,
Haagmans
B. L.
,
Veer
B. V. D.
,
Den
S. V.
,
Wijsman
L.
,
Goderski
G.
,
Ellis
J.
,
Zambon
M.
,
Peiris
M.
,
Goossens
H.
,
Reusken
C.
,
Koopmans
M. P. G.
&
Drosten
C.
2020
Detection of 2019 novel coronavirus (2019-nCoV) by real-time RT-PCR
.
Eurosurveillance
25
(
3
).
https://doi.org/10.2807/1560-7917.ES.2020.25.3.2000045
Crits-Christoph
A.
,
Kantor
R. S.
,
Olm
M. R.
,
Whitney
O. N.
,
Al-Shayeb
B.
,
Lou
Y. C.
,
Flamholz
A.
,
Kennedy
L. C.
,
Greenwald
H.
,
Hinkle
A.
,
Hetzel
J.
,
Spitzer
S.
,
Koble
J.
,
Tan
A.
,
Hyde
F.
,
Schroth
G.
,
Kuersten
S.
,
Banfield
J. F.
&
Nelson
K. L.
2021
Genome sequencing of sewage detects regionally prevalent SARS-CoV-2 variants
.
mBio
12
(
1
),
1
9
.
https://doi.org/10.1128/mBio.02703-20
.
Croghan
C. W.
&
Egeghy
P. P.
2003
Methods of Dealing with Values below the Limit of Detection Using SAS
.
US-EPA, Research Triangle Park, NC.
CRS Report
2021
Global Economic Effects of COVID-19. November 10, 2021. R46270. Available from: https://crsreports.congress.gov.
Cucinotta
D.
&
Vanelli
M.
2020
WHO declares COVID-19 a pandemic
.
Acta Biomedica
91
(
1
),
157
160
.
doi:10.23750/abm.v91i1.9397
.
Decaro
N.
,
Elia
G.
,
Campolo
M.
,
Desario
C.
,
Mari
V.
,
Radogna
A.
,
Colaianni
M. L.
,
Cirone
F.
,
Tempesta
M.
&
Buonavoglia
C.
2008
Detection of bovine coronavirus using a TaqMan-based real-time RT-PCR assay
.
Journal of Virological Methods
151
(
2
),
167
171
.
https://doi.org/10.1016/J.JVIROMET.2008.05.016
.
de Oliveira
L. C.
,
Torres-Franco
A. F.
,
Lopes
B. C.
,
Santos
B. S. Á. d. S.
,
Costa
E. A.
,
Costa
M. S.
,
Reis
M. T. P.
,
Melo
M. C.
,
Polizzi
R. B.
,
Teixeira
M. M.
&
Mota
C. R.
2021
Viability of SARS-CoV-2 in river water and wastewater at different temperatures and solids content
.
Water Research
195
,
117002
.
https://doi.org/10.1016/J.WATRES.2021.117002
.
de Souza
W. M.
,
Buss
L. F.
,
Candido
D. d. S.
,
Carrera
J. P.
,
Li
S.
,
Zarebski
A. E.
,
Pereira
R. H. M.
,
Prete
C. A.
,
de Souza-Santos
A. A.
,
Parag
K. V.
,
Belotti
M. C. T. D.
,
Vincenti-Gonzalez
M. F.
,
Messina
J.
,
da Silva Sales
F. C.
,
Andrade
P. d. S.
,
Nascimento
V. H.
,
Ghilardi
F.
,
Abade
L.
,
Gutierrez
B.
,
Kraemer
M. U. G.
,
Braga
C. K. V.
,
Aguiar
R. S.
,
Alexander
N.
,
Mayaud
P.
,
Brady
O. J.
,
Marcilio
I.
,
Gouveia
N.
,
Li
G.
,
Tami
A.
,
de Oliveira
S. B.
,
Porto
V. B. G.
,
Ganem
F.
,
de Almeida
W. A. F.
,
Fantinato
F. F. S. T.
,
Macário
E. M.
,
de Oliveira
W. K.
,
Nogueira
M. L.
,
Pybus
O. G.
,
Wu
C. H.
,
Croda
J.
,
Sabino
E. C.
&
Faria
N. R.
2020
Epidemiological and clinical characteristics of the COVID-19 epidemic in Brazil
.
Nature Human Behaviour
4
(
8
),
856
865
.
https://doi.org/10.1038/s41562-020-0928-4
.
Dunn
O. J.
1961
Multiple comparisons among means
.
Journal of the American Statistical Association
56
(
293
),
52
64
.
https://doi.org/10.1080/01621459.1961.10482090
.
Feng
S.
,
Roguet
A.
,
McClary-Gutierrez
J. S.
,
Newton
R. J.
,
Kloczko
N.
,
Meiman
J. G.
&
McLellan
S. L.
2021
Evaluation of Sampling, Analysis, and Normalization Methods for SARS-CoV-2 Concentrations in Wastewater to Assess COVID-19 Burdens in Wisconsin Communities
.
ACS ES&T Water
1
,
1955
1965
.
https://doi.org/10.1021/acsestwater.1c00160
Figueiredo
A. C. C.
2021
Social housing and the Favela Urbanization Program: the Paraisópolis complex, in São Paulo
.
Risco – Journal of Architeture and Urbanisms (Online)
19
,
1
15
.
https://doi.org/10.11606/1984-4506.risco.2021.159431
.
Fongaro
G.
,
Rogovski
P.
,
Pereira Savi
B.
,
Dorighello Cadamuro
R.
,
Virgínia Faria Pereira
J.
,
Hashimoto Sant Anna
I.
,
Henrique Rodrigues
I.
,
Sobral Marques Souza
D.
,
Gregory Torres Saravia
E.
,
Rodríguez-Lázaro
D.
&
Célia da Silva Lanna
M.
2021
SARS-CoV-2 in human sewage and river water from a remote and vulnerable area as a surveillance tool in Brazil
.
Food and Environmental Virology
1
,
3
.
https://doi.org/10.1007/s12560-021-09487-9
.
Forés
E.
,
Bofill-Mas
S.
,
Itarte
M.
,
Martínez-Puchol
S.
,
Hundesa
A.
,
Calvo
M.
,
Borrego
C. M.
,
Corominas
L. L.
,
Girones
R.
&
Rusiñol
M.
2021
Evaluation of two rapid ultrafiltration-based methods for SARS-CoV-2 concentration from wastewater
.
Science of the Total Environment
768
,
144786
.
doi:10.1016/j.scitotenv.2020.144786
.
Gerrity
D.
,
Papp
K.
,
Stoker
M.
,
Sims
A.
&
Frehner
W.
2021
Early-pandemic wastewater surveillance of SARS-CoV-2 in Southern Nevada: Methodology, occurrence, and incidence/prevalence considerations
.
Water Research
10
,
100086
.
https://doi.org/10.1016/J.WROA.2020.100086.
Giacobbo
A.
,
Rodrigues
M. A. S.
,
Zoppas Ferreira
J.
,
Bernardes
A. M.
&
de Pinho
M. N.
2021
A critical review on SARS-CoV-2 infectivity in water and wastewater. What do we know?
Science of the Total Environment
774
,
145721
.
https://doi.org/10.1016/j.scitotenv.2021.145721
.
GPEI
2016
Explaining Environmental Surveillance
. .
Graham
K. E.
,
Loeb
S. K.
,
Wolfe
M. K.
,
Catoe
D.
,
Sinnott-Armstrong
N.
,
Kim
S.
,
Yamahara
K. M.
,
Sassoubre
L. M.
,
Mendoza Grijalva
L. M.
,
Roldan-Hernandez
L.
,
Langenfeld
K.
,
Wigginton
K. R.
&
Boehm
A. B.
2021
SARS-CoV-2 RNA in Wastewater Settled Solids Is Associated with COVID-19 Cases in a Large Urban Sewershed
.
Environmental Science and Technology
55
,
488
498
.
https://doi.org/10.1021/acs.est.0c06191.
Haramoto
E.
,
Malla
B.
,
Thakali
O.
&
Kitajima
M.
2020
First environmental surveillance for the presence of SARS-CoV-2 RNA in wastewater and river water in Japan
.
Science of the Total Environment
737
,
140405
.
https://doi.org/10.1016/j.scitotenv.2020.140405
.
Haugh
L. D.
1976
Checking the independence of two covariance-stationary time series: a univariate residual cross-correlation approach
.
Journal of the American Statistical Association
71
(
354
),
378
385
.
https://doi.org/10.2307/2285318
.
Hewett
P.
&
Ganser
G. H.
2007
A comparison of several methods for analyzing censored data
.
Annals of Occupational Hygiene
51
,
611
632
.
https://doi.org/10.1093/annhyg/mem045
.
Iglesias
N. G.
,
Gebhard
L. G.
,
Carballeda
J. M.
,
Aiello
I.
,
Recalde
E.
,
Terny
G.
,
Ambrosolio
S.
,
L'Arco
G.
,
Konfino
J.
&
Brardinell
J. I.
2021
SARS-CoV-2 surveillance in untreated wastewater: detection of viral RNA in a low-resource community in Buenos Aires, Argentina
.
Rev Panam Salud Publica
45
,
e137
.
https://doi.org/10.26633/RPSP.2021.137
.
Jafferali
M. H.
,
Khatami
K.
,
Atasoy
M.
,
Birgersson
M.
,
Williams
C.
&
Cetecioglu
Z.
2021
Benchmarking virus concentration methods for quantification of SARS-CoV-2 in raw wastewater
.
Science of Total Environment
755
,
142939
.
https://doi.org/10.1016/J.SCITOTENV.2020.142939.
Jahn
K.
,
Dreifuss
D.
,
Topolsky
I.
,
Kull
A.
,
Ganesanandamoorthy
P.
,
Fernandez-Cassi
X.
,
Bänziger
C.
,
Devaux
A. J.
,
Stachler
E.
,
Caduff
L.
,
Cariti
F.
,
Corzón
A. T.
,
Fuhrmann
L.
,
Chen
C.
,
Jablonski
K. P.
,
Nadeau
S.
,
Feldkamp
M.
,
Beisel
C.
,
Aquino
C.
,
Stadler
T.
,
Ort
C.
,
Kohn
T.
,
Julian
T. R.
&
Beerenwinkel
N.
2021
Detection and surveillance of SARS-CoV-2 genomic variants in wastewater
.
MedRxiv
.
https://doi.org/10.1101/2021.01.08.21249379
.
Jensen
S.
,
Nguyen
C.
&
Jewett
J.
2016
A gradient-free method for the purification of infective dengue virus for protein-level investigations
.
Journal of Virological Methods
235
,
125
130
.
https://doi.org/10.1016/J.JVIROMET.2016.05.017
.
Kirby
A. E.
,
Walters
M. S.
,
Jennings
W. C.
,
Fugitt
R.
,
LaCross
N.
,
Mattioli
M.
,
Marsh
Z. A.
,
Roberts
V. A.
,
Mercante
J. W.
,
Yoder
J.
&
Hill
V. R.
2021
Using wastewater surveillance data to support the COVID-19 response – United States, 2020–2021
.
MMWR – Morbidity and Mortality Weekly Report
70
,
1242
1244
.
http://dx.doi.org/10.15585/mmwr.mm7036a2
.
Kumar
M.
,
Joshi
M.
,
Patel
A. K.
&
Joshi
C. G.
2021
Unravelling the early warning capability of wastewater surveillance for COVID-19: a temporal study on SARS-CoV-2 RNA detection and need for the escalation
.
Environmental Research
196
,
110946
.
https://doi.org/10.1016/j.envres.2021.110946
.
Kupek
E.
2021
How many more? Under-reporting of the COVID-19 deaths in Brazil in 2020
.
Tropical Medicine and International Health
.
https://doi.org/10.1111/tmi.13628
La Rosa
G.
,
Bonadonna
L.
,
Lucentini
L.
,
Kenmoe
S.
&
Suffredini
E.
2020
Coronavirus in water environments: occurrence, persistence and concentration methods – a scoping review
.
Water Research
179
,
115899
.
https://doi.org/10.1016/j.watres.2020.115899
.
Medema
G.
,
Been
F.
,
Heijnen
L.
&
Petterson
S.
2020a
Implementation of environmental surveillance for SARS-CoV-2 virus to support public health decisions: opportunities and challenges
.
Current Opinion in Environmental Science & Health
17
,
49
71
.
https://doi.org/10.1016/J.COESH.2020.09.006
.
Medema
G.
,
Heijnen
L.
,
Elsinga
G.
,
Italiaander
R.
&
Brouwer
A.
2020b
Presence of SARS-Coronavirus-2 RNA in sewage and correlation with reported COVID-19 prevalence in the early stage of the epidemic in the Netherlands
.
Environmental Science and Technology Letters
7
(
7
),
511
516
.
https://doi.org/10.1021/ACS.ESTLETT.0C00357
.
Ministério da Saúde
2020
Brasil confirma primeiro caso da doença (Brazil confirms first case of the disease) — Português (Brasil). Available from: https://www.gov.br/saude/pt-br/assuntos/noticias/brasil-confirma-primeiro-caso-de-novo-coronavirus (accessed 28 August 2021)
.
Mota
C. R.
,
Bressani-Ribeiroa
T.
,
Araújo
J. C.
,
Leal
C. D.
,
Leroy-Freitas
D.
,
Machado
E. C.
,
Espinosa
M. F.
,
Fernandes
L.
,
Leão
T. L.
,
Chamhum-Silva
L.
,
Azevedo
L.
,
Morandia
T.
,
Freitas
G. T. O.
,
Costa
M. S.
,
Carvalho
B. O.
,
Reis
M. T. P.
,
Melo
M. C.
,
Ayrimoraes
S. R.
&
Chernicharo
C. A. L.
2021
Assessing spatial distribution of COVID-19 prevalence in Brazil using decentralised sewage monitoring
.
Water Research
202
,
117388
.
https://doi.org/10.1016/j.watres.2021.117388
.
Nemudryi
A.
,
Nemudraia
A.
,
Wiegand
T.
,
Surya
K.
,
Buyukyoruk
M.
,
Cicha
C.
,
Vanderwood
K. K.
,
Wilkinson
R.
&
Wiedenheft
B.
2020
Temporal detection and phylogenetic assessment of SARS-CoV-2 in municipal wastewater
.
Cell Reports Medicine
1
(
6
),
100098
.
https://doi.org/10.1016/J.XCRM.2020.100098
.
NICD (National Institute for Communicable Diseases)
2021
COVID 19: Wastewater-Based Epidemiology for SARS-CoV-2 in South Africa
. .
Okonechnikov
K.
,
Golosova
O.
&
Fursov
M.
2012
Genome analysis Unipro UGENE: a unified bioinformatics toolkit
.
Bioinformatics Applications Note
28
(
8
),
1166
1167
.
https://doi.org/10.1093/bioinformatics/bts091
.
Paul
D.
,
Kolar
P.
&
Hall
S. G.
2021
A review of the impact of environmental factors on the fate and transport of coronaviruses in aqueous environments
.
npj Clean Water
4
(
1
),
1
13
.
https://doi.org/10.1038/s41545-020-00096-w
.
Philo
S. E.
,
Keim
E. K.
,
Swanstrom
R.
,
Ong
A. Q. W.
,
Burnor
E. A.
,
Kossik
A. L.
,
Harrison
J. C.
,
Demeke
B. A.
,
Zhou
N. A.
,
Beck
N. K.
,
Shirai
J. H.
&
Meschke
J. S.
2021
A comparison of SARS-CoV-2 wastewater concentration methods for environmental surveillance
.
Science of the Total Environment
760
,
144215
.
https://doi.org/10.1016/J.SCITOTENV.2020.144215
.
Pina
S.
,
Jofre
J.
,
Emerson
S. U.
,
Purcell
R. H.
&
Girones
R.
1998
Characterization of a strain of infectious hepatitis E virus isolated from sewage in an area where hepatitis E is not endemic
.
Applied And Environmental Microbiology
64
,
4485
4488
.
PMSP
2020
COVID-19 Relatório Situacional (COVID-19 Situation Report)
.
São Paulo
.
Polanco-Martinez
J. M.
,
Medina-Elizalde
M. A.
,
Sanchez Goni
M. F.
&
Mudelsee
M.
2019
BINCOR: an r package for estimating the correlation between two unevenly spaced time series
.
R Journal
11
(
1
),
1
14
.
https://doi.org/10.32614/rj-2019-035
.
Prado
T.
,
Fumian
T. M.
,
Mannarino
C. F.
,
Resende
P. C.
,
Motta
F. C.
,
Eppinghaus
A. L. F.
,
Chagas do Vale
V. H.
,
Braz
R. M. S.
,
de Andrade
J. d. S. R.
,
Maranhão
A. G.
&
Miagostovich
M. P.
2021
Wastewater-based epidemiology as a useful tool to track SARS-CoV-2 and support public health policies at municipal level in Brazil
.
Water Research
191
.
https://doi.org/10.1016/j.watres.2021.116810
Razzolini
M. P.
,
Funada Barbosa
M.
,
Silva de Araújo
R.
,
Freitas de Oliveira
I.
,
Mendes-Correa
M.
,
Sabino
E.
,
Garcia
S.
,
de Paula
A.
,
Villas-Boas
L.
,
Costa
S.
,
Dropa
M.
,
Brandão de Assis
D.
,
Levin
B.
,
Pedroso de Lima
A.
&
Levin
A.
2021
SARS-CoV-2 in a stream running through an underprivileged, underserved, urban settlement in São Paulo, Brazil: a 7-month follow-up
.
Environmental Pollution (Barking, Essex: 1987)
290
,
118003
.
https://doi.org/10.1016/J.ENVPOL.2021.118003
.
Rijksoverheid (Government of the Netherlands)
2021
Coronavirus Dashboard. Early Indicators: Virus Particle in Wastewater
.
Available from: https://coronadashboard.government.nl/landelijk/rioolwater (accessed 30 December 2021)
.
Rimoldi
S. G.
,
Stefani
F.
,
Gigantiello
A.
,
Polesello
S.
,
Comandatore
F.
,
Mileto
D.
,
Maresca
M.
,
Longobardi
C.
,
Mancon
A.
,
Romeri
F.
,
Pagani
C.
,
Cappelli
F.
,
Roscioli
C.
,
Moja
L.
,
Gismondo
M. R.
&
Salerno
F.
2020
Presence and infectivity of SARS-CoV-2 virus in wastewaters and rivers
.
Science of the Total Environment
744
,
140911
. https://doi.org/10.1016/j.scitotenv.2020.140911.
Ritchie
H.
,
Mathieu
E.
,
Rodés-Guirao
L.
,
Appel
C.
,
Giattino
C.
,
Ortiz-Ospina
E.
,
Hasell
J.
,
Macdonald
B.
,
Beltekian
D.
&
Roser
M.
2020
Coronavirus pandemic (COVID-19). Our World in Data
.
Sala-Comorera
L.
,
Reynolds
L. J.
,
Martin
N. A.
,
O'Sullivan
J. J.
,
Meijer
W. G.
&
Fletcher
N. F.
2021
Decay of infectious SARS-CoV-2 and surrogates in aquatic environments
.
Water Research
201
.
https://doi.org/10.1016/j.watres.2021.117090
.
Sherchan
S. P.
,
Shahin
S.
,
Ward
L. M.
,
Tandukar
S.
,
Aw
T. G.
,
Schmitz
B.
,
Ahmed
W.
&
Kitajima
M.
2020
First detection of SARS-CoV-2 RNA in wastewater in North America: a study in Louisiana, USA
.
Science of the Total Environment
743
.
https://doi.org/10.1016/j.scitotenv.2020.140621
.
Sims
N.
&
Kasprzyk-Hordern
B.
2020
Future perspectives of wastewater-based epidemiology: monitoring infectious disease spread and resistance to the community level
.
Environmental International
139
.
https://doi.org/10.1016/j.envint.2020.105689
.
SNIS–Sistema Nacional de Informações em Saneamento 2020 Série Histórica (Historical Series). http://app4.mdr.gov.br/serieHistorica/ . (accessed 10 May 2020).
Summer
M. D.
&
Smith
D. E. A.
1987
Manual of Methods of Baculovirus Vectors and Insect Cell Culture Procedures
.
Texas A&M University
,
College Station, TX
.
Takeda
T.
,
Kitajuma
M.
,
Abeynayaka
A.
,
Huong
N. T. T.
,
Dinh
N. Q.
,
Sirikanchana
K.
,
Navia
M.
,
Sam
A. A.
,
Tsudaka
M.
,
Setiadi
T.
,
Hung
D. T.
,
Haramoto
E.
2021
Governance of wastewater surveillance systems to minimize the impact of COVID-19 and future epidemics: cases across Asia-Pacific
. In:
Environmental Resilience and Transformation in Times of COVID-19
(
Ramanathan
A. L.
,
Shidambaram
S.
,
Jonathan
M. P.
,
Prasanna
M. V.
,
Kumar
P.
&
Arriola
F. M.
, eds).
Elsevier Radarweg
,
Amsterdam
,
Netherlands
,
Chapter 11
, pp.
115
126
.
https://doi.org/10.1016/B978-0-323-85512-9.00010-3
.
Tran
H. N.
,
Le
G. T.
,
Nguyen
D. T.
,
Juang
R. S.
,
Rinklebe
J.
,
Bhatnagar
A.
,
Lima
E. C.
,
Iqbal
H. M. N.
,
Sarmah
A. K.
&
Chao
H. P.
2021
SARS-CoV-2 coronavirus in water and wastewater: a critical review about presence and concern
.
Environmental Research
193
,
110265
.
https://doi.org/10.1016/j.envres.2020.110265
.
Veiga Silva
L.
,
Da Penha de Andrade Abi Harb
M.
,
Milene Teixeira Barbosa dos Santos
A.
,
André de Mattos Teixeira
C.
,
Hugo Macedo Gomes
V.
,
Helena Silva Cardoso
E.
,
da Silva
M. S.
,
Vijaykumar
N. L.
,
Venâncio Carvalho
S.
,
Ponce de Leon Ferreira de Carvalho
A.
&
Renato Lisboa Frances
C.
2020
COVID-19 mortality underreporting in Brazil: analysis of data from government internet portals
.
Journal of Medical Internet Research
22
(
8
),
e21413
.
https://doi.org/10.2196/21413
.
Wade
M. J.
,
Jacomo
A. L.
,
Armenise
L.
,
Brown
M. R.
,
Bunce
J. T.
,
Cameron
G. J.
,
Zhou Fang
Z.
,
Farkas
K.
,
Gilpin
D. F.
,
Graham
D. W.
,
Grimsley
J. M. S.
,
Alwyn Hart
A.
,
Hoffmann
T.
,
Katherine
J.
,
Jackson
K. J.
,
Jones
D. L.
,
Lilley
C. L.
,
McGrath
J. W.
,
McKinley
J. M.
,
McSparron
C.
,
Behnam
F.
,
Nejad
B. F.
,
Mario Morvan
M.
,
Quintela-Baluja
M.
,
Roberts
A. M. I.
,
Singer
A. C.
,
Souque
C.
,
Speigh
V. L.
,
Sweetapple
C.
,
Walker
D.
,
Watts
G.
,
Weightman
A.
&
Kasprzyk-Hordern
B.
2022
Understanding and managing uncertainty and variability for wastewater monitoring beyond the pandemic: lessons learned from the United Kingdom national COVID-19 surveillance programmes
.
Journal of Hazardous materials
424
(
Pt B
),
127456
.
https://doi.org/10.1016/B978-0-323-85512-9.00010-3
.
Weidhaas
J.
,
Aanderud
Z. T.
,
Roper
D. K.
,
VanDerslice
J.
,
Gaddis
E. B.
,
Ostermiller
J.
,
Hoffman
K.
,
Jamal
R.
,
Heck
P.
,
Zhang
Y.
,
Torgersen
K.
,
Laan
J. V.
&
LaCross
N.
2021
Correlation of SARS-CoV-2 RNA in wastewater with COVID-19 disease burden in sewersheds
.
Science of the Total Environment
775
,
145790
.
doi:10.1016/j.scitotenv.2021.145790
.
Westhaus
S.
,
Weber
F. A.
,
Schiwy
S.
,
Linnemann
V.
,
Brinkmann
M.
,
Widera
M.
,
Greve
C.
,
Janke
A.
,
Hollert
H.
,
Wintgens
T.
&
Ciesek
S.
2021
Detection of SARS-CoV-2 in raw and treated wastewater in Germany – suitability for COVID-19 surveillance and potential transmission risks
.
Science of the Total Environment
751
,
141750
.
https://doi.org/10.1016/j.scitotenv.2020.141750
.
WHO
2020
Status of Environmental Surveillance for SARS-CoV-2 Virus: Scientific Brief. WHO/2019-nCoV/Sci_Brief/EnvironmentalSampling/2020.1 (5 August), pp. 1–4
.
Wilder
M. L.
,
Middleton
F.
,
Larsen
D. A.
,
Du
Q.
,
Fenty
A.
,
Zeng
T.
,
Insaf
T.
,
Kilaru
P.
,
Collins
M.
,
Kmush
B.
&
Green
H. C.
2021
Co-quantification of crAssphage increases confidence in wastewater-based epidemiology for SARS-CoV-2 in low prevalence areas
.
Water Research X
11
,
100100
.
https://doi.org/10.1016/J.WROA.2021.100100
.
Wu
F.
,
Zhang
J.
,
Xiao
A.
,
Gu
X.
,
Lin Lee
W.
,
Armas
F.
,
Kauffman
K.
,
Hanage
W.
,
Matus
M.
,
Ghaeli
N.
,
Endo
N.
,
Duvallet
C.
,
Poyet
M.
,
Moniz
K.
,
Washburne
A. D.
,
Erickson
T. B.
,
Chai
P. R.
,
Thompson
J.
&
Alm
E. J.
2020
SARS-CoV-2 titers in wastewater are higher than expected from clinically confirmed cases
.
Applied and Environmental Science
5
(
4
),
e00614
20
.
https://doi.org/10.1128/mSystems.00614-20
.
Xagoraraki
I.
&
O'Brien
E.
2020
Wastewater-based epidemiology for early detection of viral outbreaks
. In:
Women in Water Quality
(
O'Bannon
D. J.
, ed.).
Springer Nature
,
Switzerland
, pp.
75
97
.
Ye
Y.
,
Ell enberg
R. M.
,
Graham
K. E.
&
Wigginton
K. R.
2016
Survivability, partitioning, and recovery of enveloped viruses in untreated municipal wastewater
.
Environmental Science and Technology
50
(
10
),
5077
5085
.
https://doi.org/10.1021/ACS.EST.6B00876
.
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