key: cord-0834245-rj1sy0tk
authors: de Sousa, Adriano Roberto Vieira; do Carmo Silva, Lívia; de Curcio, Juliana Santana; da Silva, Hugo Delleon; Eduardo Anunciação, Carlos; Maria Salem Izacc, Silvia; Neto, Flavio Olimpio Sanches; de Paula Silveira Lacerda, Elisângela
title: “pySewage”: a hybrid approach to predict the number of SARS-CoV-2-infected people from wastewater in Brazil
date: 2022-05-06
journal: Environ Sci Pollut Res Int
DOI: 10.1007/s11356-022-20609-z
sha: bfb8362fdebad2c1d0a529ce6e44dc7bbb15ddd8
doc_id: 834245
cord_uid: rj1sy0tk

It is well known that the new coronavirus pandemic has global environmental, public health, and economic implications. In this sense, this study aims to monitor SARS-CoV-2 in the largest wastewater treatment plant of Goiânia, which processes wastewater from more than 700,000 inhabitants, and to correlate the molecular and clinical data collected. Influent and effluent samples were collected at Dr. Helio de Seixo Britto’s wastewater treatment plant from January to August 2021. Viral concentration was performed with polyethylene glycol before viral RNA extraction. Real-time qPCR (N1 and N2 gene assays) was performed to detect and quantify the viral RNA present in the samples. The results showed that 43.63% of the samples were positive. There is no significant difference between the detection of primers N1 (mean 3.23 log10 genome copies/L, std 0.23) and N2 (mean 2.95 log10 genome copies/L, std 0.29); also, there is no significant difference between the detection of influent and effluent samples. Our molecular data revealed a positive correlation with clinical data, and infection prevalence was higher than clinical data. In addition, we developed a user-friendly web application to predict the number of infected people based on the detection of viral load present in wastewater samples and may be applied as a public policy strategy for monitoring ongoing outbreaks. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11356-022-20609-z.

In December 2019, a cluster of patients with pneumonia symptoms was observed in Wuhan, Hubei, China. After further investigation, the outbreak was identified as coronavirus disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (Dong et al. 2020; Graham Carlos et al. 2020) . The rapid global spread of SARS-CoV-2 led the World Health Organization to classify COVID-19 as a pandemic in March 2020 (World Health Organization 2020). The current COVID-19 outbreak has become a serious threat to human health around the world (Liu et al. 2020) .

In the early days of the pandemic, nonpharmaceutical measures (NPIs) such as isolation through quarantine, social distancing, wearing face masks, and travel restrictions were implemented in many countries to contain the peak of COVID-19 infection (Coccia 2020a; Bo et al. 2021 ). These measures have had a positive impact on the natural environment, as many sources of pollution have been reduced or eliminated (Elsaid et al. 2021) , and transmission of COVID-19 within the local population has also decreased (Flaxman et al. 2020; Huang et al. 2021 ). However, some NPIs, such as prolonged lockdown, could have negative impacts on many aspects of life, including psychological, social, and economic consequences (Brooks et al. 2020; Coccia 2021a, b) .

On the other hand, the transmission dynamics and diffusion of SARS-CoV-2 appear to be a complex relationship between climate, environment, and social aspects (Coccia 2020a (Coccia , b, 2021a . Cities with high levels of air pollution combined with low production of renewable wind energy may have accelerated the spread of COVID-19, especially in people with concomitant diseases such as heart disease, chronic obstructive pulmonary disease, and lung cancer (Amoatey et al. 2020; Coccia 2020a Coccia , b, 2021a . Gross domestic product (GDP) per capita, health expenditures, population mobility, and social interactions are also factors that can contribute to the spread and mortality of COVID-19 (Bontempi et al. 2020; Coccia 2021d) .

SARS-CoV-2 is readily transmitted from human-to-human through contact with aerosols, droplets, and fomites from infected individuals (He et al. 2020; Meyerowitz et al. 2021 ). However, active viral replication in the gut and detection of SARS in intestinal samples have been reported, even when some patients did not have any gastrointestinal symptoms (Zuo et al. 2021) . Released SARS-CoV-2 viral particles are rapidly inactivated in gastrointestinal fluid (Larsen and Wigginton 2020) . There is evidence of cell tropism of SARS-CoV-2 in multiple organs, not only in the lung but also in the small intestine, pancreas, blood vessels, and other tissues . Moreover, SARS-CoV-2 was detected in stool samples over a longer time, namely after 5 weeks, whereas it was undetectable in respiratory swab samples (Wu et al. 2020b) , indicating possible contamination via the fecal-oral route (Heller et al. 2020; Guo et al. 2021) .

Hence, epidemiological monitoring should be prioritized by surveillance services in the context of the pandemic, as it allows a better knowledge of the current epidemiological scenario (Peccia et al. 2020) . Concentrations of SARS-CoV-2 RNA in wastewater samples can be correlated with reported COVID-19 cases and predict the outcomes of community clinical trials, sometimes 6 to 14 days before the onset of symptoms (Peccia et al. 2020; Cervantes-Avilés et al. 2021; Kumar et al. 2021a; Barua et al. 2022) . One strategy that has been used is mass testing, but this strategy is costly and not feasible for low-income countries, even with existing adaptations such as the use of sample pools (Brault et al. 2021 ). Thus, cost-effective alternatives that can help with epidemiological monitoring and community surveillance are needed.

Wastewater-based epidemiology (WEB) is a populationwide potential tool for monitoring the chemical or microbiological profile of a community. This methodology allows assembly inferences about infected asymptomatic and symptomatic individuals by assessing the viral load in wastewater in a given population (Bivins et al. 2020; Hewitt et al. 2022) . Also, it allows the collection of data from people who lack access to health care and provides health authorities with realtime monitoring of outbreaks (Larsen and Wigginton 2020) . Furthermore, this approach has been supported by several studies on COVID-19 epidemiological control; Ahmed et al. (2021) estimated the average number of infected individuals in Australia to range from 171 to 1090, which was consistent with clinical observations. In Brazil, Claro et al. (2021) observed for 44 weeks at five different sites. The prevalence of infection ranged from 0.05 to 0.38%, showing a positive correlation with clinical observations. Other studies detected SARS-CoV-2 viral RNA in wastewater in India (Arora et al. 2020) , Germany (Westhaus et al. 2021) , the USA (Wu et al. 2021 ; Barua et al. 2022) , Croatia (Brnić et al. 2022) , and the United Arab Emirates (Hasan et al. 2021) .

Since the beginning of the pandemic, an avalanche of molecular data has been produced worldwide, making the development of data science and bioinformatics protocols essential (Mercatelli et al. 2021b) . Once new data is daily produced, computational tools may accelerate the monitoring of infected people around the world (Hufsky et al. 2021) , although several web tools have been developed for analysis regarding SARS-CoV-2 (Mercatelli et al. 2021a) .

Bioinformatics tools play an important role in monitoring SARS-CoV-2 and provide non-computational users with the possibility to analyze data and advanced knowledge related to COVID-19 (Hufsky et al. 2021) . This is probably part of the reason that although several studies have been conducted to monitor SARS-CoV-2, and estimate the number of infected people from wastewater samples, they are mostly used by a small group of researchers (Sanches-Neto et al. 2021) . Therefore, in addition to studies of SARS-CoV-2 monitoring by experimental techniques such as RT-qPCR and genomic sequencing, it is extremely important to develop web applications to automate the SARS-CoV-2 detection combined with COVID-19 monitoring (Pérez-Cataluña et al. 2022) . Moreover, the use of our web application can provide early prediction in wastewater, potentially identifying new variants for recurrent outbreaks (Peterson et al. 2022; Wurtzer et al. 2022) .

Nevertheless, to the best of our knowledge, there are no web applications regarding the monitoring of the number of people infected with SARS-CoV-2 from the viral load detected in wastewater. Thus, the focus of this work aimed to monitor the SARS-CoV-2 viral load in wastewater samples in a large community in Brazil-Midwest combined with the development of a user-friendly web application to predict the number of infected people.

A total of 55 wastewater samples were collected from January to August 2021 (30 weeks) at Dr Hélio Seixo de Britto's wastewater treatment plant (WWTP) located in Goiânia, Goiás, Brazil (latitude − 16.6799, longitude − 49.255 16° 40′ 48″ South, 49° 15′ 18″ West). A total of 200 mL of influent and effluent samples was collected between 2 and 3 pm (BRT -Brazilian time zone, − 03:00 UTC) in sterile bottles using the grab sample method, transported on wet ice to the testing laboratory within 15 min, and frozen at − 80 °C until the samples were processed for virus concentration. The WWTP serves about 57.51% of the population of Goiania (over 700,000 people) and its treatment consists of chemically enhanced primary treatment with a maximum capacity for sewage treatment of 2300L/s −1 T (Fig. 1 ).

Viral concentration was performed according to Fongaro et al. (2021) , and 50 mL of influent and effluent were processed. The pH of the sample was adjusted to 3.5 using HCl 1 M (Sigma-Aldrich, MO, USA). Samples were then shaken at 4 °C for 30 min and centrifuged at 2474 g at 4 °C for 30 min. The supernatant was discarded and the pellet was resuspended with 25 mL of glycine-NaOH (0.25 M) buffer, pH 9.5, shaken for 30 min at 4 °C, and centrifuge at 2474 g at 4 °C for 60 min. The supernatant was homogenized with 4 mL polyethylene glycol 24% PEG 6000 (Sigma-Aldrich, MO, USA) by shaking for 120 min at 4 °C and then centrifuged for 180 min at 2474 g at 4 °C. The supernatant was discarded and the pellet formed was eluted with 1 mL of phosphate-buffered saline (PBS 1x-137 mM NaCl, 2.7 mM KCl, 10 mM Na2HPO4, and 1.8 mM KH2PO4, pH 7.0). Samples were stored at − 80 °C until RNA extraction. To evaluate the viral concentration protocol, samples were spiked with a double-stranded DNA control cloned into a plasmid containing the complete nucleocapsid gene sequence of the 2019-nCoV virus containing the two regions analyzed with the 2019-nCoV RUO N1 and N2 kit (IDT, Integrated and Technologies).

SARS-CoV-2 RNA was extracted from 100 μL of the samples using the MagMAX™ Viral/Pathogen Nucleic Acid Isolation Kit (Thermo Fisher Scientific, MA, USA) according to the manufacturer's protocol. The extracted nucleic acid was stored at − 80 °C until additional downstream analysis.

The different regions of the SARS-CoV-2 nucleocapsid gene were detected by RT-qPCR using two primers pairs-N1 and N2 (IDT, IA, USA) and according to the recommendations of the Centers for Disease Control and Prevention protocols (https:// www. fda. gov/ media/ 134922/ downl oad) and GoTaq® Probe 1-Step RT-qPCR System (Promega, WI, USA). The total reaction volume was 20 μL (10 μL GoTaq Probe qPCR Master Mix, 0.4 μL Go Script RT Mix for 1-Step RT-qPCR, 3.6 μL . The effluent reaches WWTP by gravity starting the preliminary treatment by removing coarse solids in the coarse grid (1) and by discharge from a lifting station (2). The raw sewage goes to a new mechanized cleaning in the grids (3), followed by sandpits with three sand traps (4). At the exit of the sand trap, the coagulant is added, and later in the parshall gutter (5), the flow measurement is measured by an ultrasonic meter, and then the anionic polymer is added. Subsequently, the effluent reaches the distribution box (6) and is dis-tributed to three primary decanters (7). The primary sludge present in the storage tank is repressed for the mechanical dehydration process, which occurs by centrifugation (8). The treated primary effluent is discharged directly into the river (9). The dry sludge is mechanically mixed with CaO, in order to stabilize it (pH 12) and improve its characteristics of manipulation. After this step, the sludge is transported to the landfill for the recovery of areas degraded (10). The influent and effluent samples were collected after the 3 and 7 steps, respectively nuclease-free water, 1.0 primer/probe, and 5.0 μL of RNA). Nuclease-Free Water for Molecular Biology (Sigma-Aldrich) was used as a negative control. The RT-qPCR was performed using the AriaMX Real-Time PCR System (Agilent, CA, USA). The amplification program consisted of one cycle at 45 °C for 15 min for reverse transcription, one cycle at 95 °C for 2 min for denaturation of reverse transcriptase and activation of DNA polymerase, followed by 45 cycles at 95 °C for 15 s, and 60 °C for 1 min for denaturation and amplification. We compared the amplification curves in the RT-qPCR reactions with both the commercial 2019-nCoV_N_Positive Control and the biological positive control using AriaMx software. All samples that presented Cq values lower than 40 were considered positive for SARS-CoV-2. This value was determined by the construction of a standard curve. We used a log 10 dilution series ranging from 10 5 to 1 copy/reaction. The straight equation generated through the standard curve was used to determine the number of copies of viral genetic material present in samples. All the reactions were performed in triplicate and nuclease-free water was used as a negative control. Viral load (viral gene copies/L of wastewater) was determined by Eq. (1):

(1)

= viral genomic copies μL eluted RNA • 40μL(total volume of eluted RNA) 50mL(initial volume of concentrated wastewater sample) • 20

As reported by Ahmed et al. (2021) , the number of infected people (NIP) was estimated as follows Eq. (2): where VGC is viral RNA genomic copies (per liter) of N1 or N2 gene targets detected in wastewater samples; FR is the flow rate of the wastewater treatment plant; α is the viral load shedding in the stool; β is the estimated daily production of stool per capita; and is the percentage of infected people shedding viral RNA in the stool. To calculate the flow rate (FR) of the wastewater treatment plant, data were collected and analyzed from health agencies, such as inflow rate, maximum daily inflow rate, and the mean inflow rate, to determine the average flow rate/day/L/s. Moreover, due to the wide variation in parameters such as viral load shedding rate (α), daily stool mass production per capita (β), and percentage of infected individuals shedding SARS-CoV-2 viral RNA in the stool ( ), it is difficult to interpret the infection prediction results. Thus, a Monte Carlo simulation was performed in Eq. 2 using the Python programming language. The viral load shedding rate in the stool (α) was modeled as a uniform distribution with a range of 0.8 to 7.5 log10 gc • g −1 • feces −1 (Ahmed et al. 2021) . Daily stool mass production per capita (β) was modeled as a normal distribution with a range of 75 to 520 g/capita/day in low-income countries, as reported by Rose et al. (2015) . The percentage 

In this study, the presence of SARS-CoV-2 RNA in sewage was analyzed using probes directed to the regions of SARS-COV-2 N (N1 and N2 gene assays), and the quantification Prado et al. (2020 Prado et al. ( , 2021 and Ahmed et al. (2021) presented 41.6% and 48.4% of positivity in their samples, respectively. We found a viral load ranging from 2.73 to 3.73 log10 for N1 gene assay, and 2.69 to 5.47 log10 for N2 gene assay. Wastewater epidemiology is a useful public health tool to understand the dissemination of COVID-19 in a community from SARS-CoV-2 RNA quantification (Orive et al. 2020 ). Table 1 shows some different results regarding the RNA concentration. This detection difference is consistent with what is reported in the literature (Guerrero-Latorre et al. 2020; Randazzo et al. 2020; Ahmed et al. 2021; Claro et al. 2021) , and it is suggested to be related to the analytical sensitivity of gene assays (Randazzo et al. 2020) . Additionally, PCR assays can be affected by potential inhibitors present in sewage (Kitajima et al. 2020) , especially those WWTP that receive clandestine sewage. The wastewater-based epidemiology could vary depending on laboratory resources, as there is no gold standard methodology leading to differences in viral load in the sample's positivity (Sherchan et al. 2021) , suggesting that a widely standardized methodology would be required (Kaya et al. 2022) . Kumar et al. (2021a, b) evaluated the efficacy of three gene assays: N-genes (nucleocapsid), S-genes (spike glycoprotein), and ORF-1ab genes (polyprotein) before and after the treatment. N genes showed to be more stable after the treatment when compared to S genes and ORF 1ab genes. It is due to these genes being protected in SARS-CoV-2 structures, and they are common genes among the family Coronaviridae (Kumar et al. 2021b ).

There is no significant difference between N1 and N2 gene assays in both influent (raw sewage) and final effluent (treated sewage) sites (considering p-value < 0.05). The occurrence of positive samples in treated wastewater suggests that the treatment employed by the WWTP is not able to completely remove the virus as reported by Randazzo et al. (2020) . Furthermore, discriminating between infectious and non-infectious viral particles in treated wastewater using RT-qPCR technics is promising for further outbreaks (Monteiro et al. 2022) . 

The number of infected people was estimated from wastewater data. Although there are limitations to the data, studies have suggested that wastewater parameters should be performed via Monte Carlo (Ahmed et al. 2021; Claro et al. 2021; Hasan et al. 2021 ). The estimation of the possible number of infected people was performed via Monte Carlos simulation with 50,000 random samples of each parameter of Eq. 1. According to Eq. 1, the number of infected people by SARS-CoV-2 in this study ranged from 2490 to 30,147 people, with a minimum and maximum prevalence of 0.35, and 4.29%, respectively (Table 2) . Ahmed et al. (2021) found a range of 122-1090 infected people. Wu et al. (2020a, b) have found a prevalence ranging from 0.1 to 5%, higher than the clinical cases (0.026), in MA, USA. Claro et al. (2021) have found a prevalence in the ABC region (point 4-São Paulo state) ranging from 0.1 to 4.39. There are some factors to be considered in estimating the prevalence of infected people. First, there is no consensus in the scientific community about the timeframe of SARS-CoV-2 shedding in the stool; there are no standardized protocols for wastewater-based epidemiology, especially in viral concentration step; and little is known about the loss of viral particles in sewage and experimental procedures (Wu et al. 2020a) . Although surveillance in wastewater is an important tool for monitoring pathogens that are circulating in the communities, further studies are needed to construct robust models of prevalence. Additionally, it is important to note that the accuracy and significance of the estimate performed depend on several parameters due to the large uncertainties and variability associated with the relevant SARS-CoV-2 data. The matrices of WWTP are complex compositions. Several kinds of chemicals (organic and inorganic) and biological compounds may affect the integrity of the viral RNA molecule. The WWTP analyzed in that study not only receive domestic sewage but also industrialized sewage. RNA extraction steps need to be carefully performed with attention to some compounds such as glove powder, salts, and detergent molecules that act as PCR inhibitors. Moreover, there is no standardized RNA extraction protocol from influent wastewater which presents a complex matrix of compounds (Michael-kordatou et al. 2020; Claro et al. 2021) .

SARS-CoV-2 viral load detected in wastewater was correlated with clinical cases (Fig. 2) . The clinical cases (confirmed cases, deaths, and intense care unit-ICU hospitalizations) were measured as a simple moving average (SMA) of 14 days. The SARS-CoV-2 viral load has a peak of RNA detection in the upper respiratory tract within the first week after symptom onset. In stool samples, it can be detected, with high viral loads, after 2 and up to 4 weeks from the onset of symptoms (Cevik et al. 2021; Guo et al. 2021) .

In the 25th epidemiological week ( Fig. 2A) , corresponding to the period between July 20 and 26, we observed a lower peak in clinical data (confirmed cases) with a high detected viral load. We suggest that the mass vaccination programs in Brazil may have led to a decrease in COVID-19 confirmed cases once there is evidence that vaccination reduces SARS-CoV-2 shedding rates (Levine-tiefenbrun et al. 2021; Li et al. 2022) . From a public-health perspective, Coccia (2022) states that an effective policy to maximize vaccination reduces COVID-19 pandemic threats, which is consistent with our findings.

To perform WBE detections, it is necessary to have expertise in molecular biology, in handling suitable laboratory equipment, and it is also time-consuming. In this context, the development of a web application becomes advantageous in analyzing large amounts of real-time data of infected people. We propose the use of an application tool based on python language-pySewage (Fig. 3) . It targets health authorities and the academic community in dealing with large amounts of data, like those produced by WBE surveillance. In addition, pySewage can be utilized as a decentralized monitoring tool to assist health authorities with the spatial distribution and prevalence of infections (Mota et al. 2021) . It can be accessed from www. pysew age. com. br. To the best of our knowledge, this is the first free-web application that estimates the number of people infected by SARS-CoV-2. Figure 3 shows a screenshot of the pySewage user view.

On the main page of the web application, the user can choose between two options: (i) Home and (ii) Simulation. If "Home" is chosen, a description of the pySewage application will be displayed. If the option "Simulation" is chosen, a page will be displayed for the user to estimate the prevalence of infection using Monte Carlo simulation due to the uncertainty of the variables (see Eq. 2). On the Simulation interface, the user can choose either "automatic or manual simulation." For both cases, the user must add genomic copies per liter of sewage (excel format), and wastewater flow rate input (liters per second per day). However, the user can change some variables such as α, β, and (see Eq. 2) when manual mode is selected.

This study detected SARS-CoV-2 in the biggest wastewater treatment plant in a large city in the Midwest region of Brazil from January to August 2021. SARS-CoV-2 was detected in 43.63% of the samples. A correlation was observed between the clinical data and the viral load detected in this study. The main limitations of this work were the lack of viral concentration standardization in the scientific literature and the manifold factors associated with the uncertainty of the prevalence prediction variables. However, the viral concentration was performed with polyethylene glycol and extracted with commercial RNA kits. Moreover, to estimate the number of infected people, we performed a Monte Carlo simulation with 95% confidence intervals in the simulated variables to deal with uncertainty. To disseminate our results, we developed a user-friendly web application for the automatic prediction of the number of infected people. Therefore, the use of the web application combined with the WBE methodology for the detection of SARS-CoV-2 becomes an essential tool for epidemiological management and may be applied as a public policy strategy for monitoring ongoing outbreaks. Several factors regarding the viral pathogens outbreaks are related to complex interactions between humans and the environment. Additionally, the environmental climate changes affect not only human chronic diseases but also infectious diseases. Thus, to further disseminate the use of our results, the future perspectives of this investigation should include arboviruses monitoring, such as DENV, CHIKV, and ZIKV, once tropical countries are endemic to these viruses.

The online version contains supplementary material available at https:// doi. org/ 10. 1007/ s11356-022-20609-z.

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We are grateful to Dr. Gislaine Fongaro for the experimental support of the viral concentration protocol and SANEAGO wastewater treatment plant for the collaboration.