Long-Term Monitoring of SARS-COV-2 RNA in Wastewater in Brazil: A More Responsive and Economical Approach

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As in most countries of the world, in Brazil, the consequences of the COVID-19 pandemic have been catastrophic. The increasing of deaths and the decrease of available beds in the hospitals, especially in 2021, have disturbed the health authorities. Several studies have reported the fecal shedding of SARS-CoV-2 RNA titers from infected symptomatic and asymptomatic individuals. Therefore, the quantification of SARS-CoV-2 in wastewater can be used to track the virus spread in a population via Wastewater-based Epidemiology (WBE). In this study, samples of untreated wastewater were collected weekly between June 9th, 2020 and March 17th, 2021 (41 weeks) at five sampling sites in the ABC Region, São Paulo, Brazil. This long-term monitoring was performed to evaluate the SARS-CoV-2 occurrence in the sewerage system. SARS-CoV-2 RNA titers were detected throughout the period. The viral RNA concentration ranged from 2.7 to 7.1 log 10 genome copies.L − 1 , with peaks in the last weeks of monitoring. Furthermore, we observed a positive correlation between the viral load in wastewater samples and the epidemiological/clinical data, with the former preceding the latter by approximately two weeks. The COVID-19 prevalence for each sampling site was estimated using the viral load observed in wastewater and other parameters, via Monte-Carlo simulation. The mean predicted prevalence ranged 0.05 to 0.38%, slightly higher than reported (0.016 ± 0.005%) in the ABC Region for the same period. These results highlight the viability of the WBE approach for COVID-19 infection monitoring in the largest urban agglomeration in South America. Environmental surveillance can be especially useful for health agencies and public decision-makers in predicting SARS-CoV-2 outbreaks, as well as in local tracing of infection clusters. Health Economics & Outcomes Research Infectious Diseases COVID-19 Wastewater-based epidemiology SARS-CoV-2 Environmental surveillance Sewage Coronavirus Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), the etiological agent of Co rona Vi rus D isease 2019 (COVID-19), was first identified in Wuhan, China, in December 2019. Since then, this novel Coronavirus has caused millions of deaths worldwide. The principal symptoms of COVID-19 are dry cough, fever, and difficulty in breathing and the main routes of transmission are through the spread of respiratory droplets, direct contact with infected individuals, and contaminated surfaces 1 . According to WHO (2021) 2 , until March 21st, 2021, Brazil has registered a total of 11,871,390 cases of COVID-19 with 290,314 deaths. On March 4th, 2021, the country reached the mark of 1,641 deaths, 46 more than the peak of the “first wave” in July 2020. Since then, the number of deaths has increased, overcoming the mark of 2,000 deaths for several days and reaching the higher mark of 2,841 on March 18th, 2021. Brazil is the country with more deaths reported in 24 hours and the second country with more deaths since the pandemic’s beginning. Brazil has administered 13,028,391 vaccine doses until March 19th, 2021, with 9,721,865 people having been vaccinated with at least one dose. However, this last one represents only 4.57% of all the population. On March 21st, 2021, the state of São Paulo has a rate of bed occupancy of 81% for hospital ward and 91% for intensive care unit (ICU), a dangerous mark considering the increasing of cases each day. In the metropolitan region of São Paulo, where is located the ABC Region, the rate of bed occupancy is 86.6% for hospital ward and 91.3% for ICU (data source: https://www.seade.gov.br/coronavirus/). The severity of the situation highlights the need for alternative techniques for monitoring the virus. Studies have shown that the SARS-CoV-2 is also shed in feces from infected symptomatic and asymptomatic individuals 3 . Wölfel et al. (2020) 4 determined a shedding rate greater than 10 7 RNA copies/g feces one week after symptom onset. However, no viable (infective) viral particles were found based on cell cultures. On the other hand, other recent studies have verified the presence of viable particle viral in feces 5,6 . Several studies have also shown the presence of SARS-CoV-2 in municipal wastewater samples, in The Netherlands 7 , Spain 8 , Australia 9 , USA 10 , Brazil 11 , among other countries. Randazzo et al. (2020) 8 , for example, detected RNA concentrations of 5.4 ± 0.2 log 10 genome copies/L on average in the Region of Murcia (Spain). Although the fecal-oral transmission pathway has not been proven, monitoring of wastewater in the sewer network (sewer pipes) and municipal wastewater treatment plants (WWTPs) could support in predicting new SARS-CoV-2 outbreaks, as well as in the local tracing of infection clusters 12 . Wastewater-Based Epidemiology (WBE) is a methodology originally designed to monitor the use of illicit drugs in a community that now has been applied to COVID-19. Wastewater surveillance data could complement epidemiological/clinical data to provide a robust tool for monitoring the SARS-CoV-2 circulation 7,13,14 . WBE has been successfully used for predicting the outbreak of Aichi virus in The Netherlands 15 and poliovirus in Israel 16 , and for monitoring the antibiotic resistance on a global scale 17 . WBE approaches are interesting especially for emerging countries whose capacity for clinical testing is limited. In Brazil, this methodology for tracking the virus spread has been used in the metropolitan region of Rio de Janeiro 11 . As in other countries, the monitoring results have been successfully used as complementary data in the COVID-19 surveillance by the local authorities. However, as attested by Daughton et al. (2020) 13 , the data published so far is insufficient for the WBE methodology implementation. There are many epidemiological (shedding profile of infected individuals, among others) and methodological (sampling strategies and experimental methods) aspects to be elucidated 18 . In this context, this study aimed to implement a low-cost WEB methodology to monitor the SARS-Cov-2 circulation in vulnerable zones of ABC Region, in Metropolitan Region of Sao Paulo, Brazil. This region has the largest urban agglomeration in South America. This methodology can support decision-making by local health agencies in combating the COVID-19 pandemic. Results SARS-CoV-2 RNA occurrence in wastewater samples. A total of 205 untreated wastewater samples from five points of the ABC Region (São Paulo, Brazil) were analyzed between June 9th, 2020 and March 17th, 2021 (41 weeks) for the SARS-CoV-2 RNA occurrence. Samples with Ct (Cycle threshold) less than 40 were considered positive and had their concentrations determined (genome copies/sample volume), according to Medema et al. (2020) 7 and Wu et al. (2020) 10 . Figure 1 shows the Ct values of the five sampling points for the entire monitoring period. The RT-qPCR N1 and N2 gene assays were analyzed for all wastewater samples. However, as shown in Fig. 1 , there was a higher SARS-CoV-2 occurrence for the N1 target. The SARS-CoV-2 RNA was detected in 40% (83/205) and 29% (60/205) of wastewater samples, for N1 and N2 gene assays, respectively. The differences among N1 and N2 assays on wastewater samples were also reported by other recent studies 7 , 8 , 10 . This could be associated with the different analytical sensitivity between the RT-qPCR gene assays 8 . Furthermore, different PCR reactions may not be identically susceptible to the inhibitory effects of the evaluated matrix 19 . As in this study, Medema et al. (2020) 7 verified a higher sensitivity for N1 gene assay, detecting SARS-CoV-2 RNA titers for a greater number of monitoring points even when clinical data indicated a low prevalence of 1 case per 100,000 inhabitants. As shown in Fig. 1 , the SARS-CoV-2 occurrence in the first weeks was less frequent. However, from November onwards, the SARS-CoV-2 RNA titers were detected in most of the wastewater samples. Coincidentally, at that same time, there were signs of the beginning of a “second wave” and/or recurring local outbreaks of COVID-19 in Brazil 20 . In January, February, and March 2021, the SARS-CoV-2 RNA was detected in 91% (10/11), 100% (11/11), 82% (9/11), 64% (7/11), and 82% (9/11) of the wastewater samples from points 1, 2, 3, 4 and 5, respectively, clearly indicating the late spread of SARS-CoV-2 in the ABC Region. Figure 2 shows the Box Plot of the SARS-CoV-2 viral load (N1 assay) per sampling point for the entire monitoring period. There were no statistical differences among the mean SARS-CoV-2 RNA concentrations, as determined by the one-way analysis of variance (ANOVA), considering a significance level of 0.05. The mean concentrations were 5.1 ± 1.2, 5.2 ± 1.0, 5.3 ± 1.2, 5.4 ± 1.6, and 4.9 ± 0.9 log 10 genome copies.L − 1 for points 1, 2, 3, 4 and 5, respectively. The average SARS-CoV-2 RNA titers in wastewater samples were equivalent to those detected by Randazzo et al. (2020) 8 in Spain, about 5.1 ± 0.3 log 10 genome copies.L − 1 for the N1 assay. Similarly, Wu et al. (2020) 10 detected a viral load between 4 and 5 log 10 genome copies.L − 1 , but in Massachusetts, USA. The maximum and minimum values of SARS-CoV-2 RNA found in this study were 7.1 and 2.7 log 10 genome copies.L − 1 , respectively, considering the entire data set. Other studies have also observed this wide range of concentrations. Wurtzer et al. (2020) 21 , for example, detected a viral load between 4 and 7 log 10 genome copies.L − 1 , in France. Gonzales et al. (2020) 22 detected values between 2 and 5 log 10 genome copies.L − 1 , in Virginia, USA. According to Prado et al. (2021) 11 , different factors can influence the viral load determination, including the circumstances of the COVID-19 pandemic experienced in each region. Besides, we believe that the specificities of the sewage and the sewer network in each region also affect the experimental determinations. In Brazil, sewage, and surface run-off (rainwater and stormwater) are collected separately. However, there is a high rate of clandestine connections to the sewer network, which promotes the dilution of sewage during rainy events. Therefore, the viral load measured in the wastewater samples from the ABC Region was consistent with those found in other studies around the world. Environmental surveillance. Supplementary Fig. 1 shows the spread of the SARS-CoV-2 in the ABC region for different dates, considering the viral load measured (N1 assay) in the wastewater samples. As observed in Supplementary Fig. 1, the SARS-CoV-2 RNA concentration from all sampling sites increased gradually over time, indicating the spread of COVID-19 infection in the ABC Region. At the beginning of monitoring (June 2020), the amount of SARS-CoV-2 RNA titers in wastewater samples were much less expressive than those found in the last weeks. As previously shown, this behavior was also observed for the Ct results (Fig. 1 ). The presentation of monitoring results through heat maps (Supplementary Fig. 1) can be especially useful for health agencies since it allows the spatial analysis of the pandemic situation. Epidemiological/clinical data on COVID-19 in the ABC Region was obtained from the publicly available repository of the Federal University of ABC, “Onde tem coronavirus?” project (available at https://ondetemcoronavirus.ufabc.edu.br/ ). Figure 3 shows the new cases during the monitoring period normalized by each city’s population. The cumulative prevalence of COVID-19 (in percentage), considering all municipalities of the ABC Region, was also plotted. As shown in Fig. 3 , although there is a wide variability of data, an upward trend of COVID-19 cases in the ABC Region can be observed, especially from November onwards. As previously discussed, the SARS-CoV-2 occurrences in wastewater samples showed the same behavior (Fig. 2 ). Figure 4 shows the SARS-CoV-2 viral load (N1 assay) in the five sampling points throughout the monitoring period. Despite the relatively large variance, an upward trend in SARS-CoV-2 viral load (red arrow) was observed for the five monitoring points (Fig. 4 ), following the continuous increase of reported clinical cases (Fig. 3 ). Therefore, there is a correlation between the amount of SARS-CoV-2 in wastewater and the hospitalization data, as observed by other authors 7 , 11 , 23 , 24 . However, few studies have reported long-term monitoring (over several months) of SARS-CoV-2 in wastewater 11 , 25 . A large dataset allows for more robust statistical analysis and, therefore a more reliable model for WBE 13 . Figure 5 shows the SARS-CoV-2 amount (N1 assay) for samples of Point 1 (WWTP ABC) in relation to the number of new COVID-19 cases of ABC Region. The temporal delay between the SARS-CoV-2 RNA peaks (for wastewater samples) and clinical data are indicated with black arrows. Point 1 was chosen for this comparative analysis, since it receives most of the wastewater generated in the ABC region and, therefore is the most representative monitoring point. As observed in Fig. 5 , there is a correlation between the SARS-CoV-2 amount variation in wastewater and the clinical data on COVID-19 with the former preceding the latter by approximately 14 days (two weeks). These results are consistent since the transmission of SARS-CoV-2 generally precedes the notification of a positive test by 2 to 3 weeks. This time interval corresponds to an incubation period between 2 and 14 days followed by clinical testing about a week after symptoms onset 25 – 27 . Other studies have observed the same behavior 7,25,27−30 . Saguti et al. (2021) 25 , for example, verified peaks of SARS-CoV-2 in wastewater samples from a WWTP in Sweden occurring 3 to 4 weeks before clinical notification. Ahmed et al. (2021) 28 , detected SARS-CoV-2 in wastewater samples from three WWTPs in Australia up to 3 weeks before the first reported clinical case. Peccia et al. (2020) 27 , observed a shorter delay of about a week when analyzing samples of primary sludge from a WWTP in USA. In Florianopolis (Santa Catarina, Brazil), the viral genome of SARS-CoV-2 was detected in wastewater samples in November 2019, before the first case in the Americas was reported. The SARS-CoV-2 occurrence was confirmed by genome sequencing analysis. The mean concentration was 5.83 ± 0.12 log 10 genome copies.L − 1 , while the maximum and minimum values were 6.68 ± 0.02 and 5.49 ± 0.02 log 10 genome copies.L − 1 , respectively 31 . These results indicate that wastewater surveillance can be used successfully as an early warning system for monitoring COVID-19. This methodology allows verifying the increase or reduction in the number of active cases about 2 weeks in advance. In addition, since the monitoring can be regionalized by sewer sub-basins, control actions by health agencies can be directed to the infection and transmission clusters. Prevalence estimate. The COVID-19 prevalence for each sampling site, shown in Table 1 , was estimated using the SARS-CoV-2 RNA titers data (N1 assay) and other parameters (methods: equations 1 and 2). The positive results (for SARS-CoV-2 occurrence) of all monitoring weeks were considered. The predicted prevalence of COVID-19, resulting from the Monte-Carlo simulation, was summarized in Table 1 as mean and 95% confidence interval (CI) (lower and upper). Table 1 Predicted prevalence of COVID-19 for each sampling point. Sampling point Mean (%) 95% Confidence Interval Lower (%) Upper (%) Point 1: WWTP ABC 0.20 0.05 2.10 Point 2: Vila Vilma 0.19 0.05 2.16 Point 3: Califórnia Paulista 0.23 0.06 2.61 Point 4: Parque Indaiá 0.38 0.10 4.39 Point 5: WTTP Parque Andreense 0.05 0.01 0.55 The average prevalence (in percentage) in the ABC Region for the same period (June 9th, 2020 - March 17th, 2021) was 0.016 ± 0.005%. As shown in Table 1 , the predicted values were much higher (about 10 times) than the observed COVID-19 prevalence (considering epidemiological/clinical data). Wu et al. (2020) 10 , also estimated prevalence values (0.1–5%) higher than those reported (about 0.026%) in Massachusetts, USA. Discussion In this study, a long-term monitoring of SARS-CoV-2 occurrence in wastewater samples was performed. Five sampling sites in the ABC Region (São Paulo, Brazil) were evaluated. The viral RNA concentration ranged from 2.7 to 7.1 log 10 genome copies.L − 1 , with peaks being detected in the last weeks of monitoring. We observed a positive correlation between the viral load in wastewater samples and the epidemiological/clinical data, with the former preceding the latter by approximately two weeks. The long-term monitoring (41 weeks) allowed to assess different moments of the pandemic in the studied area, such as the increase in cases of COVID-19 in the first months of 2021, the worst period of the pandemic in the ABC Region. Although the WBE approach emerges as a promising and attractive tool for epidemiological surveillance, there are still some challenges to be considered. Some factors that influence the prevalence estimation model. The municipal wastewater is a complex matrix with different chemical (organic and inorganic) and biological compounds. In general, it provides a lot of information about the habits and characteristics of a population. However, some of its constituents may contribute to the degradation of SARS-CoV-2 RNA 18 , 32 . Bivins et al. (2020) 33 , observed a time for 90% reduction (T 90 ) of viable SARS-CoV-2 in untreated wastewater about 26.2 days at 20°C. Ahmed et al. (2020) 34 , verified a T 90 of 20.4 and 12.6 days at 15 and 25°C, respectively. As expected, the decay rate increases as the temperature also increase. Although the wastewater residence times in the sewer network are not so long, the effect of degradation must be considered, especially in warm climate regions 18 . Furthermore, sampling sites that represent large areas and basins, such as the WWTP points, are more susceptible to the degradation effect of the viral genome during wastewater travel. There are discrepancies regarding viral shedding by humans. Some studies have indicated that only 60–70% of infected individuals shed SARS-CoV-2 RNA titers 10 , 25 , 35 . On the other hand, other studies have already proved that asymptomatic and undiagnosed carriers also shedding SARS-CoV-2 RNA titers 3 , 36 , 37 . There are also discrepancies in the viral load excreted by an infected individual. Wölfel et al. (2020) 4 observed a shedding rate between 2.56 and 7.67 log 10 genome copies.g feces − 1 . The shedding rates reported by Kitajima et al. (2020) 3 range from 5.79 to 8.11 log 10 genome copies.g feces − 1 . There is still a great variation in the mass of feces eliminated by each infected individual, as previously discussed. Another important aspect is the time that an infected individual remains to eliminate the SARS-CoV-2 RNA titers. Zheng et al. (2020) 35 observed that the SARS-CoV-2 duration in feces samples is significantly longer than in serum and respiratory samples. The average duration of the virus in feces samples was 22 days (interquartile range from 17 to 31 days), while in respiratory and serum samples were 18 and 16 days, respectively. Thus, an infected individual remains to eliminate the virus even when it is no longer considered an active case. In addition to the above-mentioned factors, there are still limitations and uncertainty in the sampling strategies, and in the methods of concentration, extraction, and quantification of SARS-CoV-2 RNA 13 , 18 . Therefore, an absolute comparison between the observed COVID-19 prevalence and the SARS-CoV-2 RNA concentrations in wastewater is significantly complex. On other hand, as also attested by Medema et al. (2020) 7 , the epidemiological surveillance based on relative changes in SARS-CoV-2 RNA titers from wastewater samples of sewer pipes and WWTP over time can be used as an additional monitoring tool capable of providing an early warning of SARS-CoV-2 circulation. The WBE is especially useful for health agencies and public decision-makers and should be incorporated in the country’s epidemiological surveillance and public policy development. This research is a step towards the improvement of WBE to track COVID-19. Methods Sampling sites and samples collection. Untreated wastewater from five points of the ABC Region, São Paulo, Brazil, was collected and analyzed for 41 weeks, between June 9th, 2020 and March 17th, 2021, for the SARS-CoV-2 RNA occurrence. All monitored points are shown in Supplementary Fig. 2. Additional information on sampling points is shown in Supplementary Table 1. The sampling points represent the different income levels in the region. Besides, there are also differences in the level of urbanization, access to basic sanitation, and the flow of wastewater generated. In the WWTPs (points 1 and 5), 24-hour composite sampling of 1000 mL (proportional to the hourly flow rate) was performed using a refrigerated Hach automatic sampler (model AWRS AS950), with a storage temperature of 4°C. In the sewer pipes (points 2, 3, and 4), 4-hour semi-composite sampling (proportional to the hourly flow rate) was carried out using the same automatic sampler. These sampling strategies allow a greater representation of the wastewater characteristics. The sampling frequency was weekly at all monitoring sites. Wastewater concentration and RNA extraction. Before the concentration step, wastewater samples were pasteurized at 60°C for 90 minutes to inactivate the virus, according to Wu et al. (2020) 10 . Viral particles were concentrated by the precipitation method, as described by Wu et al. (2020) 10 . Briefly, 40 mL of composite samples were centrifuged at 8000xg for 120 min at 4°C and the pellet was resuspended in 0,4 mL of 1x PBS (pH 7.2). For sample cleaning, 1 mL of acid phenol was added and mixed strongly (Cabral et al. 2020) 38 . An aqueous phase was then formed by centrifugation at 12.000xg for 10 min and transferred to a microtube containing 0.3 mL of lysis buffer. RNA extraction was performed using the PureLink™ Viral RNA/DNA Mini Kit (Thermo Fisher Scientific), according to the manufacturer’s protocol. The concentration of RNA was measured in the Nanodrop Lite (Thermo Fisher Scientific). The enveloped bovine respiratory syncytial virus (BRSV – Inforce™ 3, Zoetis, US) was used for evaluated concentration methods recovery capacity. Viral detection and quantification. The presence of RNA SARS-CoV-2 was determined using the 2019-nCoV TaqMan RT-PCR Kit (Norgen, Cat. TM67120) based on the assays and protocols developed by the Centers for Disease Control and Prevention (CDC). The RT-qPCR was carried out following the manufacturer’s instructions, recommended standards, and positive controls in a Rotor-Gene Q (Qiagen) instrument. Each RNA extract was analyzed in duplicate. A calibration curve was performed using the 2019-nCoV_N_Positive Control (Norgen, Cat. PC67102). Reactions were performed in a final volume of 20 µL, using 5 µL of the extracted RNA, 10 µL of 2x One-Step RT-PCR Master Mix, 1.5 µL of Primer&Probe Mix, and 3.5 µL of RNAse free water. A series of positive (four 10-fold serial dilutions) and negative controls (extraction and PCR) were included for each RT-qPCR run by N1 and N2 genome target. The cycling program followed: cycle 1–50°C for 30 minutes; cycle 2–95°C for 3 minutes; cycle 3–45x95°C for 3 seconds and 55°C for 30 seconds (acquiring fluorescence in the green filter, as the probes contain FAM fluorescence). Cycle threshold (Ct) values were used to calculate GC/L in the original sample. Ct values lower than 40 were considered positive for SARS-CoV-2, as proposed previously 7 , 10 . BRSV RT- qPCR reactions were performed according to previous protocols described by Rajal et al. (2007) 39 and Boxus et al. (2005) 40 . Prevalence estimation. The infected population in the ABC Region was estimated using the SARS-CoV-2 viral load measured in wastewater and other parameters, according to the following Eq. 9 ,23 : Where: C RNA = SARS-CoV-2 RNA concentration measured in wastewater samples (genome copies.L − 1 ). F = Wastewater volumetric flow rate (L.d − 1 ). α = Fecal load (g.person − 1 .d − 1 ). β = SARS-CoV-2 shedding rate by an infected individual (genome copies.g − 1 ). The SARS-CoV-2 RNA titers in wastewater (C RNA ) were measured for the five monitoring points in the ABC Region, as previously described. In the WWTPs (points 1 and 5), the wastewater volumetric flow rate (F) was measured in loco, while for the sewer pipes points (points 2, 3, and 4), it was estimated from the per capita wastewater generation (160 L.person − 1 .d − 1 ) and the contributing population (Supplementary Table 1). The daily feces mass (α) produced by humans from low-income countries usually ranges from 75 to 520 grams per person (with an average value of 149 ± 95 g.person − 1 .d − 1 ), according to Rose et al. (2015) 41 . The SARS-CoV-2 shedding rate by an infected individual (β) usually ranges from 6.3x10 5 to 1.3x10 8 genome copies.g − 1 , according to Kitajima et al. (2020) 3 and Gholipour et al. (2021) 42 . Thus, the COVID-19 average prevalence was estimated for each sampling site considering the contributing population (Supplementary Table 1), according to the following equation: Statistical analysis. The one-way analysis of variance (ANOVA) was used to determine whether there were differences among the mean SARS-CoV-2 RNA concentrations of each sampling site, considering a significance level of 0.05. The statistical Monte-Carlo approach was incorporated into the calculation of the prevalence estimate (Equations 1 and 2), since parameters such as Fecal load (α) and SARS-CoV-2 shedding rate (β) have large variation, which makes it difficult to interpret the results of the infected population (N) and, consequently, the predicted prevalence. Monte-Carlo simulation with 10,000 random samplings from each parameter/input of Eq. 1 was implemented. The parameters values and their respective types of statistical distribution were shown in Supplementary Table 2. The Monte-Carlo simulation summary was reported as mean and 95% confidence interval (CI) for each sampling point. Statistical analysis was performed using Origin Pro, while the Monte-Carlo simulation was implemented in Microsoft Excel. Declarations Acknowledgments The authors would like to acknowledge the financial support from the following Brazilian institutions: Brazilian National Council of Scientific and Technological Development (CNPq) in partnership with Ministry of Science, Technology, Innovations and Communications (MCTIC), and Ministry of Health (MS), Secretariat of Science, Technology, Innovation and Strategic Inputs – Decit/SCTIE 07/2020 (Research to cope with COVID-19, its consequences and other severe acute respiratory syndromes – Process Number 402432/2020-7). Author Contributions Ieda Carolina Mantovani Claro : Conceptualization, Methodology, Formal analysis, Investigation, Writing. Aline Diniz Cabral: Conceptualization, Methodology, Formal analysis, Investigation, Writing. Matheus Ribeiro Augusto: Methodology, Formal analysis, Investigation, Writing. 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Cell Reports Med. 1 , 100098 (2020). La Rosa, G. et al. First detection of SARS-CoV-2 in untreated wastewaters in Italy. Sci. Total Environ. 736 , 139652 (2020). Fongaro, G. et al. The presence of SARS-CoV-2 RNA in human sewage in Santa Catarina, Brazil, November 2019. Sci. Total Environ. 778 , 146198 (2021). Mao, K. et al. The potential of wastewater-based epidemiology as surveillance and early warning of infectious disease outbreaks. Curr. Opin. Environ. Sci. Heal. 17 , 1–7 (2020). Bivins, A. et al. Persistence of SARS-CoV-2 in Water and Wastewater. Environ. Sci. Technol. Lett. 7 , 937–942 (2020). Ahmed, W. et al. Decay of SARS-CoV-2 and surrogate murine hepatitis virus RNA in untreated wastewater to inform application in wastewater-based epidemiology. Environ. Res. 191 , 110092 (2020). Zheng, S. et al. Viral load dynamics and disease severity in patients infected with SARS-CoV-2 in Zhejiang province, China, January-March 2020: retrospective cohort study. BMJ 369 , m1443 (2020). Chavarria-Miró, G. et al. Time Evolution of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in Wastewater during the First Pandemic Wave of COVID-19 in the Metropolitan Area of Barcelona, Spain. Appl. Environ. Microbiol. 87 , 1–9 (2021). Jiang, X. et al. Asymptomatic SARS-CoV‐2 infected case with viral detection positive in stool but negative in nasopharyngeal samples lasts for 42 days. J. Med. Virol. 92 , 1807–1809 (2020). Cabral, A. D. et al. Standardization of the method of concentration and extraction of nucleic acids in wastewater samples: A low-cost tool for use in a surveillance SARS-CoV-2. Eng. Sanit. e Ambient. 1 , 1–17 (2020). Rajal, V. B. et al. Validation of hollow fiber ultrafiltration and real-time PCR using bacteriophage PP7 as surrogate for the quantification of viruses from water samples. Water Res. 41 , 1411–1422 (2007). Boxus, M., Letellier, C. & Kerkhofs, P. Real Time RT-PCR for the detection and quantitation of bovine respiratory syncytial virus. J. Virol. Methods 125 , 125–130 (2005). Rose, C., Parker, A., Jefferson, B. & Cartmell, E. The Characterization of Feces and Urine: A Review of the Literature to Inform Advanced Treatment Technology. Crit. Rev. Environ. Sci. Technol. 45 , 1827–1879 (2015). Gholipour, S. et al. COVID-19 infection risk from exposure to aerosols of wastewater treatment plants. Chemosphere 273 , 129701 (2021). Additional Declarations There is NO Competing Interest. Supplementary Files Supplementaryinformation.docx SUPPLEMENTAL & ADDITIONAL INFORMATION Cite Share Download PDF Status: Published Journal Publication published 01 Sep, 2021 Read the published version in Water Research → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-358281","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":18255294,"identity":"d5f7afa2-c648-4c0f-aa70-38148be89b69","order_by":0,"name":"Ieda Carolina Claro","email":"","orcid":"","institution":"Federal University of ABC","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ieda","middleName":"Carolina","lastName":"Claro","suffix":""},{"id":18255295,"identity":"ec4b585a-578e-4e9b-a4c7-26401f577167","order_by":1,"name":"Aline Cabral","email":"","orcid":"","institution":"Federal University of ABC","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Aline","middleName":"","lastName":"Cabral","suffix":""},{"id":18255296,"identity":"c80ef3de-8276-4480-a33f-3ff1a3f3f446","order_by":2,"name":"Matheus Augusto","email":"","orcid":"https://orcid.org/0000-0002-0139-1619","institution":"Federal University of ABC","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Matheus","middleName":"","lastName":"Augusto","suffix":""},{"id":18255297,"identity":"5269b3ad-0787-4d9f-b860-92564e88bd45","order_by":3,"name":"Adriana Duran","email":"","orcid":"","institution":"Federal University of ABC","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Adriana","middleName":"","lastName":"Duran","suffix":""},{"id":18255298,"identity":"063ac5bc-b85a-4f59-b74d-7dd9bc25f000","order_by":4,"name":"Melissa Cristina Graciosa","email":"","orcid":"","institution":"Federal University of ABC","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Melissa","middleName":"Cristina","lastName":"Graciosa","suffix":""},{"id":18255299,"identity":"4f256fe1-d97a-4c11-a3b9-f1ceb4c0a59c","order_by":5,"name":"Fernando Luiz Fonseca","email":"","orcid":"","institution":"Faculty of Medicine of ABC (FMABC)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Fernando","middleName":"Luiz","lastName":"Fonseca","suffix":""},{"id":18255300,"identity":"7af2961f-fc8e-4e71-9a1c-d2095eba98ab","order_by":6,"name":"Marcia Aparecida Speranca","email":"","orcid":"","institution":"Federal University of ABC","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Marcia","middleName":"Aparecida","lastName":"Speranca","suffix":""},{"id":18255301,"identity":"3386707b-8bf7-4284-a659-e0280cab6865","order_by":7,"name":"Rodrigo Bueno","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBklEQVRIiWNgGAWjYHACA4YEMAkCFQz8DBIgBhtMhKCWMwySDURpgZOMbURoMW9v3vbhwS8bBnOJ5GMPPs47LME/u/kBw4eywwzm0gewapE5c6x4RmJfGoPljLR0w5nbDktI3DlmwDjj3GEGy74ErFokJHKMGRJ7DjMY3Mgxk+bddriO4UYOAzNvG1DkDHaHIWnJ/ybNO+ewhDxIy19CWhJ+gG1hk+ZtOCxhANLCiE8Lz7FihsSGNB7LnmdmkjOOpUsYAv1ysOdcOlAEhxb25s2MP/7YyJmzJz+T+FBjLSF3u/nhgx9l1nLmPNi1gAEwOlClDwAxPg1A8Ae/9CgYBaNgFIxwAABaVFtZOCzHmQAAAABJRU5ErkJggg==","orcid":"","institution":"Federal University of ABC","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Rodrigo","middleName":"","lastName":"Bueno","suffix":""}],"badges":[],"createdAt":"2021-03-24 13:46:54","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-358281/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-358281/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1016/j.watres.2021.117534","type":"published","date":"2021-09-01T09:45:28+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":7314548,"identity":"4b30602a-4a8f-4f68-8e0b-1365f9bccab4","added_by":"auto","created_at":"2021-03-24 14:49:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":47396,"visible":true,"origin":"","legend":"Ct values: SARS-CoV-2 occurrence for RT-qPCR N1 (a) and N2 (b) gene assays.","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-358281/v1/ffd74fbacf97684798d0ed61.png"},{"id":7314546,"identity":"02eb9449-0449-4526-ba5f-1290153ab2e0","added_by":"auto","created_at":"2021-03-24 14:49:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":162681,"visible":true,"origin":"","legend":"Box Plot of the SARS-CoV-2 viral load (N1 assay) per sampling point.","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-358281/v1/75d089403dc26de49c269cd7.png"},{"id":7314544,"identity":"804fe59e-a9de-4438-bcb7-c67748027673","added_by":"auto","created_at":"2021-03-24 14:49:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":325768,"visible":true,"origin":"","legend":"Epidemiological/clinical data on COVID-19 in the ABC Region. Data source: https://ondetemcoronavirus.ufabc.edu.br/.","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-358281/v1/6995ed42c8a7393eeb067221.png"},{"id":7314630,"identity":"d933d3d2-048b-42d9-ac81-2cfbe4d480cc","added_by":"auto","created_at":"2021-03-24 14:52:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":149365,"visible":true,"origin":"","legend":"SARS-CoV-2 RNA concentrations (N1 assay) for the five sampling points.","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-358281/v1/920bc4959e28ef270cf84b4a.png"},{"id":7314343,"identity":"47e5ef80-4c41-413b-a182-fc4bd10462fc","added_by":"auto","created_at":"2021-03-24 14:46:15","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":202768,"visible":true,"origin":"","legend":"SARS-CoV-2 viral load (N1 assay) at Point 1 (WWTP ABC) in relation to the clinical data on COVID-19 in the ABC Region.","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-358281/v1/b61fbb24cfd8a69467dbf9b8.png"},{"id":15857283,"identity":"9aae2ead-177d-4a64-9282-148f6f715b63","added_by":"auto","created_at":"2021-11-24 09:45:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1522547,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-358281/v1/59d377f0-8b17-48dc-9cef-da882120a76d.pdf"},{"id":7314631,"identity":"4d1a031c-40ea-4b34-8681-fa596d9ed8e2","added_by":"auto","created_at":"2021-03-24 14:52:15","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1289681,"visible":true,"origin":"","legend":"SUPPLEMENTAL \u0026 ADDITIONAL INFORMATION","description":"","filename":"Supplementaryinformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-358281/v1/7f4bf0d402d02ac8fb626bfd.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"\u003cp\u003eLong-Term Monitoring of SARS-COV-2 RNA in Wastewater in Brazil: A More Responsive and Economical Approach\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eSevere acute respiratory syndrome coronavirus-2 (SARS-CoV-2), the etiological agent of \u003cu\u003eCo\u003c/u\u003erona \u003cu\u003eVi\u003c/u\u003erus \u003cu\u003eD\u003c/u\u003eisease 2019 (COVID-19), was first identified in Wuhan, China, in December 2019. Since then, this novel Coronavirus has caused millions of deaths worldwide. The principal symptoms of COVID-19 are dry cough, fever, and difficulty in breathing and the main routes of transmission are through the spread of respiratory droplets, direct contact with infected individuals, and contaminated surfaces\u003csup\u003e1\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eAccording to WHO (2021)\u003csup\u003e2\u003c/sup\u003e, until March 21st, 2021, Brazil has registered a total of 11,871,390 cases of COVID-19 with 290,314 deaths. On March 4th, 2021, the country reached the mark of 1,641 deaths, 46 more than the peak of the \u0026ldquo;first wave\u0026rdquo; in July 2020. Since then, the number of deaths has increased, overcoming the mark of 2,000 deaths for several days and reaching the higher mark of 2,841 on March 18th,\u003csup\u003e\u0026nbsp;\u003c/sup\u003e2021. Brazil is the country with more deaths reported in 24 hours and the second country with more deaths since the pandemic\u0026rsquo;s beginning. Brazil has administered 13,028,391 vaccine doses until March 19th, 2021, with 9,721,865 people having been vaccinated with at least one dose. However, this last one represents only 4.57% of all the population.\u003c/p\u003e\n\u003cp\u003eOn March 21st, 2021, the state of S\u0026atilde;o Paulo has a rate of bed occupancy of 81% for hospital ward and 91% for intensive care unit (ICU), a dangerous mark considering the increasing of cases each day. In the metropolitan region of S\u0026atilde;o Paulo, where is located the ABC Region, the rate of bed occupancy is 86.6% for hospital ward and 91.3% for ICU (data source: https://www.seade.gov.br/coronavirus/). The severity of the situation highlights the need for alternative techniques for monitoring the virus.\u003c/p\u003e\n\u003cp\u003eStudies have shown that the SARS-CoV-2 is also shed in feces from infected symptomatic and asymptomatic individuals\u003csup\u003e3\u003c/sup\u003e. W\u0026ouml;lfel et al. (2020)\u003csup\u003e4\u003c/sup\u003e determined a shedding rate greater than 10\u003csup\u003e7\u003c/sup\u003e RNA copies/g feces one week after symptom onset.\u0026nbsp;However, no viable (infective) viral particles were found based on cell cultures. On the other hand, other recent studies have verified the presence of viable particle viral in feces\u003csup\u003e5,6\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eSeveral studies have also shown the presence of SARS-CoV-2 in municipal wastewater samples, in The Netherlands\u003csup\u003e7\u003c/sup\u003e, Spain\u003csup\u003e8\u003c/sup\u003e, Australia\u003csup\u003e9\u003c/sup\u003e, USA\u003csup\u003e10\u003c/sup\u003e, Brazil\u003csup\u003e11\u003c/sup\u003e, among other countries. Randazzo et al. (2020)\u003csup\u003e8\u003c/sup\u003e, for example, detected RNA concentrations of 5.4 \u0026plusmn; 0.2 log\u003csub\u003e10\u003c/sub\u003e genome copies/L on average in the Region of Murcia (Spain). Although the fecal-oral transmission pathway has not been proven, monitoring of wastewater in the sewer network (sewer pipes) and municipal wastewater treatment plants (WWTPs) could support in predicting new SARS-CoV-2 outbreaks, as well as in the local tracing of infection clusters\u003csup\u003e12\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eWastewater-Based Epidemiology (WBE) is a methodology originally designed to monitor the use of illicit drugs in a community that now has been applied to COVID-19. Wastewater surveillance data could complement epidemiological/clinical data to\u0026nbsp;provide a robust tool for monitoring the SARS-CoV-2 circulation\u003csup\u003e7,13,14\u003c/sup\u003e. WBE has been successfully used for predicting the outbreak of Aichi virus in The Netherlands\u003csup\u003e15\u003c/sup\u003e and poliovirus in Israel\u003csup\u003e16\u003c/sup\u003e, and for monitoring the antibiotic resistance on a global scale\u003csup\u003e17\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eWBE approaches are interesting especially for emerging countries whose capacity for clinical testing is limited. In Brazil, this methodology for tracking the virus spread has been used in the metropolitan region of Rio de Janeiro\u003csup\u003e11\u003c/sup\u003e. As in other countries, the monitoring results have been successfully used as complementary data in the COVID-19 surveillance by the local authorities. However, as attested by Daughton et al. (2020)\u003csup\u003e13\u003c/sup\u003e, the data published so far is insufficient for the WBE methodology implementation. There are many epidemiological (shedding profile of infected individuals, among others) and methodological (sampling strategies and experimental methods) aspects to be elucidated\u003csup\u003e18\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIn this context, this study aimed to implement a low-cost WEB methodology to monitor the SARS-Cov-2 circulation in vulnerable zones of ABC Region, in Metropolitan Region of Sao Paulo, Brazil. This region has the largest urban agglomeration in South America. This methodology can support decision-making by local health agencies in combating the COVID-19 pandemic.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eSARS-CoV-2 RNA occurrence in wastewater samples.\u003c/strong\u003e A total of 205 untreated wastewater samples from five points of the ABC Region (S\u0026atilde;o Paulo, Brazil) were analyzed between June 9th, 2020 and March 17th, 2021 (41 weeks) for the SARS-CoV-2 RNA occurrence. Samples with Ct (Cycle threshold) less than 40 were considered positive and had their concentrations determined (genome copies/sample volume), according to Medema et al. (2020)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e and Wu et al. (2020)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eFigure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e shows the Ct values of the five sampling points for the entire monitoring period.\u003c/p\u003e\n\u003cp\u003eThe RT-qPCR N1 and N2 gene assays were analyzed for all wastewater samples. However, as shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, there was a higher SARS-CoV-2 occurrence for the N1 target. The SARS-CoV-2 RNA was detected in 40% (83/205) and 29% (60/205) of wastewater samples, for N1 and N2 gene assays, respectively. The differences among N1 and N2 assays on wastewater samples were also reported by other recent studies\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. This could be associated with the different analytical sensitivity between the RT-qPCR gene assays\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Furthermore, different PCR reactions may not be identically susceptible to the inhibitory effects of the evaluated matrix\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eAs in this study, Medema et al. (2020)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e verified a higher sensitivity for N1 gene assay, detecting SARS-CoV-2 RNA titers for a greater number of monitoring points even when clinical data indicated a low prevalence of 1 case per 100,000 inhabitants.\u003c/p\u003e\n\u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, the SARS-CoV-2 occurrence in the first weeks was less frequent. However, from November onwards, the SARS-CoV-2 RNA titers were detected in most of the wastewater samples. Coincidentally, at that same time, there were signs of the beginning of a \u0026ldquo;second wave\u0026rdquo; and/or recurring local outbreaks of COVID-19 in Brazil\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. In January, February, and March 2021, the SARS-CoV-2 RNA was detected in 91% (10/11), 100% (11/11), 82% (9/11), 64% (7/11), and 82% (9/11) of the wastewater samples from points 1, 2, 3, 4 and 5, respectively, clearly indicating the late spread of SARS-CoV-2 in the ABC Region.\u003c/p\u003e\n\u003cp\u003eFigure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e shows the Box Plot of the SARS-CoV-2 viral load (N1 assay) per sampling point for the entire monitoring period.\u003c/p\u003e\n\u003cp\u003eThere were no statistical differences among the mean SARS-CoV-2 RNA concentrations, as determined by the one-way analysis of variance (ANOVA), considering a significance level of 0.05. The mean concentrations were 5.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2, 5.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0, 5.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2, 5.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6, and 4.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9 log\u003csub\u003e10\u003c/sub\u003e genome copies.L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for points 1, 2, 3, 4 and 5, respectively.\u003c/p\u003e\n\u003cp\u003eThe average SARS-CoV-2 RNA titers in wastewater samples were equivalent to those detected by Randazzo et al. (2020)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e in Spain, about 5.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3 log\u003csub\u003e10\u003c/sub\u003e genome copies.L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for the N1 assay. Similarly, Wu et al. (2020)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e detected a viral load between 4 and 5 log\u003csub\u003e10\u003c/sub\u003e genome copies.L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, but in Massachusetts, USA.\u003c/p\u003e\n\u003cp\u003eThe maximum and minimum values of SARS-CoV-2 RNA found in this study were 7.1 and 2.7 log\u003csub\u003e10\u003c/sub\u003e genome copies.L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively, considering the entire data set. Other studies have also observed this wide range of concentrations. Wurtzer et al. (2020)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, for example, detected a viral load between 4 and 7 log\u003csub\u003e10\u003c/sub\u003e genome copies.L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, in France. Gonzales et al. (2020)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e detected values between 2 and 5 log\u003csub\u003e10\u003c/sub\u003e genome copies.L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, in Virginia, USA. According to Prado et al. (2021)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, different factors can influence the viral load determination, including the circumstances of the COVID-19 pandemic experienced in each region. Besides, we believe that the specificities of the sewage and the sewer network in each region also affect the experimental determinations. In Brazil, sewage, and surface run-off (rainwater and stormwater) are collected separately. However, there is a high rate of clandestine connections to the sewer network, which promotes the dilution of sewage during rainy events.\u003c/p\u003e\n\u003cp\u003eTherefore, the viral load measured in the wastewater samples from the ABC Region was consistent with those found in other studies around the world.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEnvironmental surveillance.\u003c/strong\u003e Supplementary Fig.\u0026nbsp;1 shows the spread of the SARS-CoV-2 in the ABC region for different dates, considering the viral load measured (N1 assay) in the wastewater samples.\u003c/p\u003e\n\u003cp\u003eAs observed in Supplementary Fig.\u0026nbsp;1, the SARS-CoV-2 RNA concentration from all sampling sites increased gradually over time, indicating the spread of COVID-19 infection in the ABC Region. At the beginning of monitoring (June 2020), the amount of SARS-CoV-2 RNA titers in wastewater samples were much less expressive than those found in the last weeks. As previously shown, this behavior was also observed for the Ct results (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe presentation of monitoring results through heat maps (Supplementary Fig.\u0026nbsp;1) can be especially useful for health agencies since it allows the spatial analysis of the pandemic situation.\u003c/p\u003e\n\u003cp\u003eEpidemiological/clinical data on COVID-19 in the ABC Region was obtained from the publicly available repository of the Federal University of ABC, \u0026ldquo;Onde tem coronavirus?\u0026rdquo; project (available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ondetemcoronavirus.ufabc.edu.br/\u003c/span\u003e\u003c/span\u003e). Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e shows the new cases during the monitoring period normalized by each city\u0026rsquo;s population. The cumulative prevalence of COVID-19 (in percentage), considering all municipalities of the ABC Region, was also plotted.\u003c/p\u003e\n\u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, although there is a wide variability of data, an upward trend of COVID-19 cases in the ABC Region can be observed, especially from November onwards. As previously discussed, the SARS-CoV-2 occurrences in wastewater samples showed the same behavior (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eFigure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e shows the SARS-CoV-2 viral load (N1 assay) in the five sampling points throughout the monitoring period.\u003c/p\u003e\n\u003cp\u003eDespite the relatively large variance, an upward trend in SARS-CoV-2 viral load (red arrow) was observed for the five monitoring points (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e), following the continuous increase of reported clinical cases (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Therefore, there is a correlation between the amount of SARS-CoV-2 in wastewater and the hospitalization data, as observed by other authors\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. However, few studies have reported long-term monitoring (over several months) of SARS-CoV-2 in wastewater\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. A large dataset allows for more robust statistical analysis and, therefore a more reliable model for WBE\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eFigure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e shows the SARS-CoV-2 amount (N1 assay) for samples of Point 1 (WWTP ABC) in relation to the number of new COVID-19 cases of ABC Region. The temporal delay between the SARS-CoV-2 RNA peaks (for wastewater samples) and clinical data are indicated with black arrows. Point 1 was chosen for this comparative analysis, since it receives most of the wastewater generated in the ABC region and, therefore is the most representative monitoring point.\u003c/p\u003e\n\u003cp\u003eAs observed in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e, there is a correlation between the SARS-CoV-2 amount variation in wastewater and the clinical data on COVID-19 with the former preceding the latter by approximately 14 days (two weeks). These results are consistent since the transmission of SARS-CoV-2 generally precedes the notification of a positive test by 2 to 3 weeks. This time interval corresponds to an incubation period between 2 and 14 days followed by clinical testing about a week after symptoms onset\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eOther studies have observed the same behavior\u003csup\u003e7,25,27\u0026minus;30\u003c/sup\u003e. Saguti et al. (2021)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, for example, verified peaks of SARS-CoV-2 in wastewater samples from a WWTP in Sweden occurring 3 to 4 weeks before clinical notification. Ahmed et al. (2021)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, detected SARS-CoV-2 in wastewater samples from three WWTPs in Australia up to 3 weeks before the first reported clinical case. Peccia et al. (2020)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, observed a shorter delay of about a week when analyzing samples of primary sludge from a WWTP in USA.\u003c/p\u003e\n\u003cp\u003eIn Florianopolis (Santa Catarina, Brazil), the viral genome of SARS-CoV-2 was detected in wastewater samples in November 2019, before the first case in the Americas was reported. The SARS-CoV-2 occurrence was confirmed by genome sequencing analysis. The mean concentration was 5.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12 log\u003csub\u003e10\u003c/sub\u003e genome copies.L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, while the maximum and minimum values were 6.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 and 5.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 log\u003csub\u003e10\u003c/sub\u003e genome copies.L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThese results indicate that wastewater surveillance can be used successfully as an early warning system for monitoring COVID-19. This methodology allows verifying the increase or reduction in the number of active cases about 2 weeks in advance. In addition, since the monitoring can be regionalized by sewer sub-basins, control actions by health agencies can be directed to the infection and transmission clusters.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrevalence estimate.\u003c/strong\u003e The COVID-19 prevalence for each sampling site, shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, was estimated using the SARS-CoV-2 RNA titers data (N1 assay) and other parameters (methods: equations 1 and 2). The positive results (for SARS-CoV-2 occurrence) of all monitoring weeks were considered. The predicted prevalence of COVID-19, resulting from the Monte-Carlo simulation, was summarized in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e as mean and 95% confidence interval (CI) (lower and upper).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003ePredicted prevalence of COVID-19 for each sampling point.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eSampling point\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eMean\u003c/p\u003e\n\u003cp\u003e(%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e95% Confidence Interval\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eLower (%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eUpper (%)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePoint 1:\u003c/p\u003e\n\u003cp\u003eWWTP ABC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.10\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePoint 2:\u003c/p\u003e\n\u003cp\u003eVila Vilma\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.16\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePoint 3:\u003c/p\u003e\n\u003cp\u003eCalif\u0026oacute;rnia Paulista\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.61\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePoint 4:\u003c/p\u003e\n\u003cp\u003eParque Indai\u0026aacute;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.39\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePoint 5:\u003c/p\u003e\n\u003cp\u003eWTTP Parque Andreense\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.55\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe average prevalence (in percentage) in the ABC Region for the same period (June 9th, 2020 - March 17th, 2021) was 0.016\u0026thinsp;\u0026plusmn;\u0026thinsp;0.005%. As shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, the predicted values were much higher (about 10 times) than the observed COVID-19 prevalence (considering epidemiological/clinical data).\u003c/p\u003e\n\u003cp\u003eWu et al. (2020)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, also estimated prevalence values (0.1\u0026ndash;5%) higher than those reported (about 0.026%) in Massachusetts, USA.\u003c/p\u003e"},{"header":"Discussion","content":" \u003cp\u003eIn this study, a long-term monitoring of SARS-CoV-2 occurrence in wastewater samples was performed. Five sampling sites in the ABC Region (S\u0026atilde;o Paulo, Brazil) were evaluated. The viral RNA concentration ranged from 2.7 to 7.1 log\u003csub\u003e10\u003c/sub\u003e genome copies.L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, with peaks being detected in the last weeks of monitoring. We observed a positive correlation between the viral load in wastewater samples and the epidemiological/clinical data, with the former preceding the latter by approximately two weeks.\u003c/p\u003e \u003cp\u003eThe long-term monitoring (41 weeks) allowed to assess different moments of the pandemic in the studied area, such as the increase in cases of COVID-19 in the first months of 2021, the worst period of the pandemic in the ABC Region.\u003c/p\u003e \u003cp\u003eAlthough the WBE approach emerges as a promising and attractive tool for epidemiological surveillance, there are still some challenges to be considered. Some factors that influence the prevalence estimation model.\u003c/p\u003e \u003cp\u003eThe municipal wastewater is a complex matrix with different chemical (organic and inorganic) and biological compounds. In general, it provides a lot of information about the habits and characteristics of a population. However, some of its constituents may contribute to the degradation of SARS-CoV-2 RNA\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Bivins et al. (2020)\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, observed a time for 90% reduction (T\u003csub\u003e90\u003c/sub\u003e) of viable SARS-CoV-2 in untreated wastewater about 26.2 days at 20\u0026deg;C. Ahmed et al. (2020)\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, verified a T\u003csub\u003e90\u003c/sub\u003e of 20.4 and 12.6 days at 15 and 25\u0026deg;C, respectively. As expected, the decay rate increases as the temperature also increase.\u003c/p\u003e \u003cp\u003eAlthough the wastewater residence times in the sewer network are not so long, the effect of degradation must be considered, especially in warm climate regions\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Furthermore, sampling sites that represent large areas and basins, such as the WWTP points, are more susceptible to the degradation effect of the viral genome during wastewater travel.\u003c/p\u003e \u003cp\u003eThere are discrepancies regarding viral shedding by humans. Some studies have indicated that only 60\u0026ndash;70% of infected individuals shed SARS-CoV-2 RNA titers\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. On the other hand, other studies have already proved that asymptomatic and undiagnosed carriers also shedding SARS-CoV-2 RNA titers\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. There are also discrepancies in the viral load excreted by an infected individual. W\u0026ouml;lfel et al. (2020)\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e observed a shedding rate between 2.56 and 7.67 log\u003csub\u003e10\u003c/sub\u003e genome copies.g feces\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. The shedding rates reported by Kitajima et al. (2020)\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e range from 5.79 to 8.11 log\u003csub\u003e10\u003c/sub\u003e genome copies.g feces\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. There is still a great variation in the mass of feces eliminated by each infected individual, as previously discussed.\u003c/p\u003e \u003cp\u003eAnother important aspect is the time that an infected individual remains to eliminate the SARS-CoV-2 RNA titers. Zheng et al. (2020)\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e observed that the SARS-CoV-2 duration in feces samples is significantly longer than in serum and respiratory samples. The average duration of the virus in feces samples was 22 days (interquartile range from 17 to 31 days), while in respiratory and serum samples were 18 and 16 days, respectively. Thus, an infected individual remains to eliminate the virus even when it is no longer considered an active case.\u003c/p\u003e \u003cp\u003eIn addition to the above-mentioned factors, there are still limitations and uncertainty in the sampling strategies, and in the methods of concentration, extraction, and quantification of SARS-CoV-2 RNA\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTherefore, an absolute comparison between the observed COVID-19 prevalence and the SARS-CoV-2 RNA concentrations in wastewater is significantly complex. On other hand, as also attested by Medema et al. (2020)\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, the epidemiological surveillance based on relative changes in SARS-CoV-2 RNA titers from wastewater samples of sewer pipes and WWTP over time can be used as an additional monitoring tool capable of providing an early warning of SARS-CoV-2 circulation. The WBE is especially useful for health agencies and public decision-makers and should be incorporated in the country\u0026rsquo;s epidemiological surveillance and public policy development. This research is a step towards the improvement of WBE to track COVID-19.\u003c/p\u003e "},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eSampling sites and samples collection.\u003c/strong\u003e Untreated wastewater from five points of the ABC Region, S\u0026atilde;o Paulo, Brazil, was collected and analyzed for 41 weeks, between June 9th, 2020 and March 17th, 2021, for the SARS-CoV-2 RNA occurrence. All monitored points are shown in Supplementary Fig.\u0026nbsp;2.\u003c/p\u003e\n\u003cp\u003eAdditional information on sampling points is shown in Supplementary Table\u0026nbsp;1.\u003c/p\u003e\n\u003cp\u003eThe sampling points represent the different income levels in the region. Besides, there are also differences in the level of urbanization, access to basic sanitation, and the flow of wastewater generated.\u003c/p\u003e\n\u003cp\u003eIn the WWTPs (points 1 and 5), 24-hour composite sampling of 1000 mL (proportional to the hourly flow rate) was performed using a refrigerated Hach automatic sampler (model AWRS AS950), with a storage temperature of 4\u0026deg;C. In the sewer pipes (points 2, 3, and 4), 4-hour semi-composite sampling (proportional to the hourly flow rate) was carried out using the same automatic sampler. These sampling strategies allow a greater representation of the wastewater characteristics. The sampling frequency was weekly at all monitoring sites.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWastewater concentration and RNA extraction.\u003c/strong\u003e Before the concentration step, wastewater samples were pasteurized at 60\u0026deg;C for 90 minutes to inactivate the virus, according to Wu et al. (2020)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Viral particles were concentrated by the precipitation method, as described by Wu et al. (2020)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Briefly, 40 mL of composite samples were centrifuged at 8000xg for 120 min at 4\u0026deg;C and the pellet was resuspended in 0,4 mL of 1x PBS (pH 7.2). For sample cleaning, 1 mL of acid phenol was added and mixed strongly (Cabral et al. 2020)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. An aqueous phase was then formed by centrifugation at 12.000xg for 10 min and transferred to a microtube containing 0.3 mL of lysis buffer. RNA extraction was performed using the PureLink\u0026trade; Viral RNA/DNA Mini Kit (Thermo Fisher Scientific), according to the manufacturer\u0026rsquo;s protocol. The concentration of RNA was measured in the Nanodrop Lite (Thermo Fisher Scientific). The enveloped bovine respiratory syncytial virus (BRSV \u0026ndash; Inforce\u0026trade; 3, Zoetis, US) was used for evaluated concentration methods recovery capacity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eViral detection and quantification.\u003c/strong\u003e The presence of RNA SARS-CoV-2 was determined using the 2019-nCoV TaqMan RT-PCR Kit (Norgen, Cat. TM67120) based on the assays and protocols developed by the Centers for Disease Control and Prevention (CDC). The RT-qPCR was carried out following the manufacturer\u0026rsquo;s instructions, recommended standards, and positive controls in a Rotor-Gene Q (Qiagen) instrument. Each RNA extract was analyzed in duplicate. A calibration curve was performed using the 2019-nCoV_N_Positive Control (Norgen, Cat. PC67102). Reactions were performed in a final volume of 20 \u0026micro;L, using 5 \u0026micro;L of the extracted RNA, 10 \u0026micro;L of 2x One-Step RT-PCR Master Mix, 1.5 \u0026micro;L of Primer\u0026amp;Probe Mix, and 3.5 \u0026micro;L of RNAse free water. A series of positive (four 10-fold serial dilutions) and negative controls (extraction and PCR) were included for each RT-qPCR run by N1 and N2 genome target. The cycling program followed: cycle 1\u0026ndash;50\u0026deg;C for 30 minutes; cycle 2\u0026ndash;95\u0026deg;C for 3 minutes; cycle 3\u0026ndash;45x95\u0026deg;C for 3 seconds and 55\u0026deg;C for 30 seconds (acquiring fluorescence in the green filter, as the probes contain FAM fluorescence). Cycle threshold (Ct) values were used to calculate GC/L in the original sample. Ct values lower than 40 were considered positive for SARS-CoV-2, as proposed previously\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. BRSV RT- qPCR reactions were performed according to previous protocols described by Rajal et al. (2007)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e and Boxus et al. (2005)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrevalence estimation.\u003c/strong\u003e The infected population in the ABC Region was estimated using the SARS-CoV-2 viral load measured in wastewater and other parameters, according to the following Eq.\u0026nbsp;9\u003csup\u003e,23\u003c/sup\u003e:\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/58677_ec8811c6b4185256/58677_custom_files/img1616596369.png\"\u003e\u003c/p\u003e\n\u003cp\u003eWhere:\u003c/p\u003e\n\u003cp\u003eC\u003csub\u003eRNA\u003c/sub\u003e = SARS-CoV-2 RNA concentration measured in wastewater samples (genome copies.L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e).\u003c/p\u003e\n\u003cp\u003eF\u0026thinsp;=\u0026thinsp;Wastewater volumetric flow rate (L.d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e).\u003c/p\u003e\n\u003cp\u003e\u0026alpha;\u0026thinsp;=\u0026thinsp;Fecal load (g.person\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e).\u003c/p\u003e\n\u003cp\u003e\u0026beta;\u0026thinsp;=\u0026thinsp;SARS-CoV-2 shedding rate by an infected individual (genome copies.g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e).\u003c/p\u003e\n\u003cp\u003eThe SARS-CoV-2 RNA titers in wastewater (C\u003csub\u003eRNA\u003c/sub\u003e) were measured for the five monitoring points in the ABC Region, as previously described.\u003c/p\u003e\n\u003cp\u003eIn the WWTPs (points 1 and 5), the wastewater volumetric flow rate (F) was measured in loco, while for the sewer pipes points (points 2, 3, and 4), it was estimated from the per capita wastewater generation (160 L.person\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and the contributing population (Supplementary Table\u0026nbsp;1).\u003c/p\u003e\n\u003cp\u003eThe daily feces mass (\u0026alpha;) produced by humans from low-income countries usually ranges from 75 to 520 grams per person (with an average value of 149\u0026thinsp;\u0026plusmn;\u0026thinsp;95 g.person\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), according to Rose et al. (2015)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe SARS-CoV-2 shedding rate by an infected individual (\u0026beta;) usually ranges from 6.3x10\u003csup\u003e5\u003c/sup\u003e to 1.3x10\u003csup\u003e8\u003c/sup\u003e genome copies.g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, according to Kitajima et al. (2020)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e and Gholipour et al. (2021)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThus, the COVID-19 average prevalence was estimated for each sampling site considering the contributing population (Supplementary Table\u0026nbsp;1), according to the following equation:\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/58677_ec8811c6b4185256/58677_custom_files/img1616596416.png\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis.\u003c/strong\u003e The one-way analysis of variance (ANOVA) was used to determine whether there were differences among the mean SARS-CoV-2 RNA concentrations of each sampling site, considering a significance level of 0.05.\u003c/p\u003e\n\u003cp\u003eThe statistical Monte-Carlo approach was incorporated into the calculation of the prevalence estimate (Equations 1 and 2), since parameters such as Fecal load (\u0026alpha;) and SARS-CoV-2 shedding rate (\u0026beta;) have large variation, which makes it difficult to interpret the results of the infected population (N) and, consequently, the predicted prevalence. Monte-Carlo simulation with 10,000 random samplings from each parameter/input of Eq.\u0026nbsp;1 was implemented. The parameters values and their respective types of statistical distribution were shown in Supplementary Table\u0026nbsp;2. The Monte-Carlo simulation summary was reported as mean and 95% confidence interval (CI) for each sampling point.\u003c/p\u003e\n\u003cp\u003eStatistical analysis was performed using Origin Pro, while the Monte-Carlo simulation was implemented in Microsoft Excel.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to acknowledge the financial support from the following Brazilian institutions: Brazilian National Council of Scientific and Technological Development (CNPq) in partnership with Ministry of Science, Technology, Innovations and Communications (MCTIC), and Ministry of Health (MS), Secretariat of Science, Technology, Innovation and Strategic Inputs \u0026ndash; Decit/SCTIE 07/2020 (Research to cope with COVID-19, its consequences and other severe acute respiratory syndromes\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u0026ndash; Process Number 402432/2020-7).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIeda Carolina Mantovani Claro\u003c/strong\u003e: Conceptualization, Methodology, Formal analysis, Investigation, Writing.\u003cstrong\u003e\u0026nbsp;Aline Diniz Cabral:\u003c/strong\u003e Conceptualization, Methodology, Formal analysis, Investigation, Writing. \u003cstrong\u003eMatheus Ribeiro Augusto:\u003c/strong\u003e Methodology, Formal analysis, Investigation, Writing. \u003cstrong\u003eAdriana Feliciano Alves Duran:\u003c/strong\u003e Methodology, Formal analysis, Investigation, Writing. \u003cstrong\u003eMelissa Cristina Pereira Graciosa:\u003c/strong\u003e Resources, Writing. \u003cstrong\u003eFernando Luiz Affonso Fonseca:\u003c/strong\u003e Resources, Writing. \u003cstrong\u003eMarcia Aparecida Speranca:\u003c/strong\u003e Resources, Writing. \u003cstrong\u003eRodrigo de Freitas Bueno:\u0026nbsp;\u003c/strong\u003eResources, Supervision, Project administration, Funding acquisition, Conceptualization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests Statements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWHO. 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Technol.\u003c/em\u003e \u003cb\u003e45\u003c/b\u003e, 1827\u0026ndash;1879 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGholipour, S. \u003cem\u003eet al.\u003c/em\u003e COVID-19 infection risk from exposure to aerosols of wastewater treatment plants. \u003cem\u003eChemosphere\u003c/em\u003e \u003cb\u003e273\u003c/b\u003e, 129701 (2021).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"COVID-19, Wastewater-based epidemiology, SARS-CoV-2, Environmental surveillance, Sewage, Coronavirus","lastPublishedDoi":"10.21203/rs.3.rs-358281/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-358281/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSARS-CoV-2, the novel Coronavirus, was first detected in Wuhan, China, in December 2019, and has since spread rapidly, causing millions of deaths worldwide. As in most countries of the world, in Brazil, the consequences of the COVID-19 pandemic have been catastrophic. The increasing of deaths and the decrease of available beds in the hospitals, especially in 2021, have disturbed the health authorities. Several studies have reported the fecal shedding of SARS-CoV-2 RNA titers from infected symptomatic and asymptomatic individuals. Therefore, the quantification of SARS-CoV-2 in wastewater can be used to track the virus spread in a population via Wastewater-based Epidemiology (WBE). In this study, samples of untreated wastewater were collected weekly between June 9th, 2020 and March 17th, 2021 (41 weeks) at five sampling sites in the ABC Region, S\u0026atilde;o Paulo, Brazil. This long-term monitoring was performed to evaluate the SARS-CoV-2 occurrence in the sewerage system. SARS-CoV-2 RNA titers were detected throughout the period. The viral RNA concentration ranged from 2.7 to 7.1 log\u003csub\u003e10\u003c/sub\u003e genome copies.L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, with peaks in the last weeks of monitoring. Furthermore, we observed a positive correlation between the viral load in wastewater samples and the epidemiological/clinical data, with the former preceding the latter by approximately two weeks. The COVID-19 prevalence for each sampling site was estimated using the viral load observed in wastewater and other parameters, via Monte-Carlo simulation. The mean predicted prevalence ranged 0.05 to 0.38%, slightly higher than reported (0.016\u0026thinsp;\u0026plusmn;\u0026thinsp;0.005%) in the ABC Region for the same period. These results highlight the viability of the WBE approach for COVID-19 infection monitoring in the largest urban agglomeration in South America. Environmental surveillance can be especially useful for health agencies and public decision-makers in predicting SARS-CoV-2 outbreaks, as well as in local tracing of infection clusters.\u003c/p\u003e","manuscriptTitle":"Long-Term Monitoring of SARS-COV-2 RNA in Wastewater in Brazil: A More Responsive and Economical Approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-03-24 14:46:13","doi":"10.21203/rs.3.rs-358281/v1","editorialEvents":[{"type":"communityComments","content":1}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4e6c425d-33eb-43ea-b91b-be489c12fdbf","owner":[],"postedDate":"March 24th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":3191965,"name":"Health Economics \u0026 Outcomes Research"},{"id":3191966,"name":"Infectious Diseases"}],"tags":[],"updatedAt":"2021-11-24T09:45:28+00:00","versionOfRecord":{"articleIdentity":"rs-358281","link":"https://doi.org/10.1016/j.watres.2021.117534","journal":{"identity":"water-research","isVorOnly":true,"title":"Water Research"},"publishedOn":"2021-09-01 09:45:28","publishedOnDateReadable":"September 1st, 2021"},"versionCreatedAt":"2021-03-24 14:46:13","video":"","vorDoi":"10.1016/j.watres.2021.117534","vorDoiUrl":"https://doi.org/10.1016/j.watres.2021.117534","workflowStages":[]},"version":"v1","identity":"rs-358281","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-358281","identity":"rs-358281","version":["v1"]},"buildId":"ehx78VzkSd0WSzXnipQa-","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}