key: cord-0907852-4h4u2rhz
authors: Sharkey, Mark E.; Kumar, Naresh; Mantero, Alejandro M.A.; Babler, Kristina M.; Boone, Melinda M.; Cardentey, Yoslayma; Cortizas, Elena M.; Grills, George S.; Herrin, James; Kemper, Jenny M.; Kenney, Richard; Kobetz, Erin; Laine, Jennifer; Lamar, Walter E.; Mason, Christopher E.; Quintero, Anda Z.; Reding, Brian D.; Roca, Matthew A.; Ryon, Krista; Schürer, Stephan C.; Shukla, Bhavarth; Solle, Natasha; Stevenson, Mario; Stone, Thomas; Tallon, John J.; Venkatapuram, Sreeharsha S.; Vidovic, Dusica; Williams, Sion L.; Young, Benjamin; Solo-Gabriele, Helena M.
title: Lessons learned from SARS-CoV-2 measurements in wastewater
date: 2021-07-21
journal: Sci Total Environ
DOI: 10.1016/j.scitotenv.2021.149177
sha: 129b549716a39640effffcaf1f53ab0450cc5bb2
doc_id: 907852
cord_uid: 4h4u2rhz

Standardized protocols for wastewater-based surveillance (WBS) for the RNA of SARS-CoV-2, the virus responsible for the current COVID-19 pandemic, are being developed and refined worldwide for early detection of disease outbreaks. We report here on lessons learned from establishing a WBS program for SARS-CoV-2 integrated with a human surveillance program for COVID-19. We have established WBS at three campuses of a university, including student residential dormitories and a hospital that treats COVID-19 patients. Lessons learned from this WBS program address the variability of water quality, new detection technologies, the range of detectable viral loads in wastewater, and the predictive value of integrating environmental and human surveillance data. Data from our WBS program indicated that water quality was statistically different between sewer sampling sites, with more variability observed in wastewater coming from individual buildings compared to clusters of buildings. A new detection technology was developed based upon the use of a novel polymerase called V2G. Detectable levels of SARS-CoV-2 in wastewater varied from 102 to 106 genomic copies (gc) per liter of raw wastewater (L). Integration of environmental and human surveillance data indicate that WBS detection of 100 gc/L of SARS-CoV-2 RNA in wastewater was associated with a positivity rate of 4% as detected by human surveillance in the wastewater catchment area, though confidence intervals were wide (β ~ 8.99 ∗ ln(100); 95% CI = 0.90–17.08; p < 0.05). Our data also suggest that early detection of COVID-19 surges based on correlations between viral load in wastewater and human disease incidence could benefit by increasing the wastewater sample collection frequency from weekly to daily. Coupling simpler and faster detection technology with more frequent sampling has the potential to improve the predictive potential of using WBS of SARS-CoV-2 for early detection of the onset of COVID-19.

The current COVID-19 pandemic has inspired novel methods for monitoring impending outbreaks and for tracking the spread of the disease among human populations. Although COVID-19 is spread primarily through direct person-to-person contact and airborne routes Although there are many advantages of WBS, its use and implementation need optimization.

This includes establishing relationships between WBS measures and the incidence of human illness, improvements in detection sensitivity and quantification, and better strategies for sample collection, concentration, and detection (Kitajima et al. 2020) . In this study we implemented a WBS program to evaluate the RNA of SARS-CoV-2 in wastewater. The objective here is to report on lessons learned from establishing a wastewater monitoring program that is integrated with human surveillance of COVID-19 and has the goal of informing decisions needed for timely containment of disease transmission. One important deviation of WBS from traditional wastewater systems design is that measurements are taken much closer to the origin of the J o u r n a l P r e -p r o o f Journal Pre-proof source of contamination. Traditionally sewage systems are designed for the conveyance of wastewater, to transport sewage from the buildings to the wastewater plant. The wastewater treatment plant is where the sewage is treated to improve its quality before discharge and thus the quality of wastewater typically focuses on evaluating the wastewater characteristics at the treatment plant. Outside of forensic-based illicit chemical tracking studies (Centazzo et al. 2019 , Bannwarth et al. 2019 , there are limited data that evaluate water quality characteristics of the sewage upstream from the wastewater plant. One new aspect of the WBS SARS-CoV-2 virus monitoring is that it has focused efforts on evaluating water quality upstream, within the building scale and within the cluster scale (which collects sewage from a group of buildings). Thus, we aim to describe lessons learned from: measuring wastewater quality characteristics closer to the point of initial discharge into the sewage system, concentrating samples for viruses, and detecting SARS-CoV-2 within the concentrates. The unique features of this work include the development of a new qPCR detection strategy for wastewater and the comparisons between SARS-CoV-2 levels in wastewater and corresponding clinical cases.

A WBS monitoring program was established for a university with well-defined municipal water supply characteristics and an elaborate human surveillance monitoring program. The WBS monitoring program was implemented during the Fall 2020 academic semester (August to December). The WBS monitoring program was separated into three components: wastewater sampling, sample processing (e.g., concentration), and SARS-CoV-2 detection. Details of the study site and of the methods employed for each of these components are described below.

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The WBS study site was the University of Miami (UM), located in Miami-Dade County, Florida, USA. Tap water was the source of wastewater to the sampling sites as the site is characterized by dedicated sewers with no storm water inflows. The source of the tap water was groundwater treated using an enhanced lime softening process which increases the pH of the water (yearly average pH of treated water of 9.1) and decreases the alkalinity (and thus the buffering capacity) (average alkalinity of 51 mg/L as CaCO 3 ). In addition, the tap water was chlorinated using chloramines (combination of chlorine gas and ammonia) with the goal of maintaining a residual at the point of use of at least 0.2 mg/L. To confirm the interconnections of the sewer lines, building and sewer construction drawings were evaluated and sewer lines were dye tested.

UM is a private research university with a population of more than 34,000 students, faculty, and staff. The three largest campuses include: (1) the Coral Gables campus, which includes most of the teaching and dormitory facilities for the undergraduate student population; (2) During the Fall 2020 semester, this human surveillance program included screening of UM students who chose to live on campus and/or attend classes in person. Students who lived on campus were screened once every 10 days and students who lived off-campus and visited campus were screened once every 14 days. These requirements were initiated consistently for student residents effective August 16 and for non-resident students on September 10, 2021. A total of 55,186 tests were conducted on students during the Fall 2020 semester (August 16, 2020 through January 3, 2021). All testing for COVID-19 was conducted via mid-nasal swab followed by PCR-based diagnosis. Each week, a total of 3,800 students were screened. These data for resident students were disaggregated by residence hall. Using these data daily positive rate (# of cases per 1000 tested subjects) was computed for each residential hall. Data were interpreted to identify "hot spots" in the community and to establish potential mitigation measures (isolation and/or quarantine) for affected groups of individuals. This human 

Wastewater was collected at two sewage scales, one corresponding to individual buildings and the other corresponding to clusters of buildings. Individual buildings were serviced by manholes, while clusters of buildings were typically serviced by lift stations. There were no wastewater treatment plants on campus. Wastewater from campus is received by the city-wide sewage collection network which is then treated with effluent discharged to either deep well or ocean outfall. Regular sampling occurred weekly from the campus sewage collection network, on Wednesday mornings from 7:30 am to 10:30 am, starting on September 30, 2020, and continuing through the summer of 2021. The data in this report covers regular sampling through December 16, 2020 (a total of 12 sampling days), with 6 to 12 samples collected each sampling J o u r n a l P r e -p r o o f Journal Pre-proof day. Of note, human surveillance data corresponding to this time period extended through January 6, 2021. Early during the study, sampling focused on clusters on all three campuses of UM, and later focused on sample collection at the residential halls and the UMHT for the purpose of on-campus surveillance and establishing relationships between SARS-CoV-2 levels in wastewater and the proportion of building residents/patients with documented COVID-19.

All wastewater samples were collected as grab samples (corresponding to an instant in time) using a "bottle on chain approach." We recognize that sewage quality can vary throughout the day but due to budget limitations we chose to collect grab samples as opposed to composites which would have required the purchase and installation of autosamplers. To accommodate the variability, samples were collected at each target manhole or lift station at almost the same time each Wednesday for the purpose of eliminating, as best as possible, daily and weekly level variations.

As part of sample collection, at each sampling site, a new unused 2-L bottle (HDPE) was lowered into the sewer. This 2-L sample was then split in the field into three containers: (1) a 0.5 L bottle which was sent to a commercial laboratory (Source Molecular, a LuminUltra Company, (SM)) for SARS-CoV-2 quantification; (2) a 1-L bottle which was sent to the UM SCCC laboratory for sample concentration; and (3) a 0.5 L plastic beaker which was used for basic water quality measurements in the field (temperature, pH, specific conductivity (SPC), dissolved oxygen (DO), and turbidity) using a precalibrated sonde (Xylem/YSI ProDSS) ( Figure   2 ). Details about field safety in terms of disinfection are provided in the supplemental text. 

Weekly samples collected in this study were concentrated via electronegative filtration by the SM laboratory and by the UM laboratory. Early during the study a subset of weekly samples were split and concentrated via ultracentrifugation. The paired samples which compared electronegative filtration and ultracentrifugation were sent to Weill Cornell Medicine (WCM) for RT-qPCR using laboratory-specific protocols described below. In addition, all weekly samples were processed for comparison between RT-qPCR (as analyzed by SM) and a new innovative technique called V2G-qPCR (as analyzed by the UM Center for AIDS Research (CFAR) laboratory), both of which used electronegative filtration for sample concentration and which utilized laboratory-specific protocols, also described below. V2G-qPCR was chosen as it has been useful in the past to detect and quantitate viral targets such as HIV-1 and Zika virus. The assay was modeled on previous work and developed early in the pandemic for the direct detection SARS-CoV-2 RNA in saliva. days of freezing. Three filters were prepared per sample at the UM laboratory. Each UM filter was folded and placed into its own tube (5 mL Eppendorf tube for V2G-qPCR analysis or a 2 mL sterile tube for all other analyses). Tubes contained 1.5 mL of DNA/RNA Shield (Zymo) forming a sewage concentrate that was stored at 4 ˚C until analysis.

Among the three UM filters, one was processed for rapid analysis by the innovative V2G-qPCR method developed at the UM CFAR. One of the other two UM SCCC filters was sent to WCM and the other was stored for later analyses. The RNA from the UM filter analyzed by V2G-J o u r n a l P r e -p r o o f Journal Pre-proof qPCR was extracted using a Zymo Quick-RNA Viral Kit using either 500 µL (first 10 sampling days) or 250 µL (last 2 sampling days) of the sewage concentrate to recover purified RNA (30 µL). The volume of concentrate was reduced from 500 to 250 µL to address inhibition, especially for highly turbid (>100 ntu) samples. Primers and reporter probe for the V2G assay bind to the same region as the nucleocapsid N3 target (CDC, 2020) but differ substantially. CoV-2 and B2M targets were analyzed for all sampling days. The OC43 target was analyzed for samples collected during the last 6 sampling days (starting November 11th) and the HIV-1 target was analyzed for samples collected during the last 4 sampling days (starting November 25th) to measure the degree to which PCR amplification was inhibited by wastewater components that co-purified with RNA. Early during the study, to minimize PCR inhibition by dilution effect, we doubled the amount of water to elute RNA from the purification columns.

Unexpectedly, increasing the volume of elution water led to a more pronounced inhibition of the J o u r n a l P r e -p r o o f PCR amplification reaction that was reduced by eluting with a smaller volume of water ( Figure   3a ). Ct values were determined in duplicate for twelve sample sites using final RNA elution volumes of 10 or 40 microliters. Comparison of the means of the two data sets showed an average shift of two cycles when the larger elution volume was used which was statistically significant (p<0.0001). Recovery of RNA was compared using different elution volumes and it was determined that 10 µl was sufficient to elute the RNA without losses (Figure 3b ). We hypothesize that the reason for this observation is that a larger volume flushes more inhibitory material through the column matrix. Using a minimal volume hydrates the silica such that the RNA is efficiently released, while inhibitory substances are retained on the silica bed. 

Wastewater results were imported into Excel (Microsoft Office 365 Pro Plus). Correlations between the RT-qPCR results and V2G-qPCR were evaluated using Pearson correlation coefficients (R 2 ) using base-10 logarithm-transformed values. Correlations were considered strong for R 2 greater than 0.5 (Shibata et al., 2004) and were considered significant for p values less than 0.05. Statistical differences between means of wastewater data were evaluated using single factor ANOVA. For sets of analyses showing statistical differences, student t-tests (assuming paired two samples for means, two tailed with alpha at 0.05) were used to evaluate differences between two specific data sets. Statistical differences between the variance of data sets were analyzed using the F-test with an alpha value of 0.05.

Human surveillance data were processed by assuming that subjects testing positive for COVID-19 on a given day were infected many days before and were shedding the virus since Human surveillance data were available for student residents on campus, for student nonresidents who visit campus for hybrid courses, and for faculty/staff. The lag correlation analysis described above was performed using the student resident data given that this population was on campus full-time on a daily basis.

Overall, water quality results ( Figure S Correlations were evaluated between water quality and SARS-Cov-2 levels. All correlations were weak (R 2 <0.1) and insignificant. Although significant variations were observed in water quality parameters, these measures did not correlate with the SARS-CoV-2 RNA signal.

We also found the variations in bacteria levels to be surprising. At wastewater treatment plants the typical level of E. coli in sewage (at the community scale) is 10,000 colony forming units (CFU) per mL of wastewater (Roca et al. 2019 ). Some sewage samples at the cluster scale and building scale showed these levels but there was also sewage from clusters that were at levels that were much less, less than 10 CFU per mL. As a result, the fecal indicator bacteria analyses were switched to measurements of fecal coliform by membrane filtration to confirm whether in fact the levels of fecal bacteria were low. Again, with the fecal coliform plates we found that levels in the sewage were as expected for some sites, on the order of 10,000 per ml. But there was sewage from some buildings and clusters that were consistently low. This was confirmed with two different sets of agar plates and was observed repeatedly on a weekly basis. These clean plates were likely associated with low levels of feces in the wastewater at the time of sampling and the likely effects of chlorine residual from the tap water. This resulted in a change in our protocol where samples were processed with the same procedures used to collect drinking water. A reductant, sodium thiosulfate, was added to the sample collection bottles (0.1 g added per liter of wastewater) starting November 25th to reduce the chlorine residual. Even with the reductant added clean agar plates were still observed for some sites.

Ultracentrifugation using Centricon-70 devices and electronegative filtration performed similarly for the detection of SARS-CoV-2. Specifically, the mean Ct value for the ultracentrifugation concentrates (29.2) was higher than the mean Ct value for the electronegative filter concentrates (26.9) . The Ct mean values were not statistically different at 95% confidence In addition to showing comparable results between both ultracentrifugation and electronegative filtration concentration methods, we found that the supplies for electronegative filtration were more readily available. The nature of the electronegative filtration process also allowed for the preparation of multiple filters from the same sample without sacrificing concentration factors. As a result, sample concentration by ultracentrifugation was dropped early during the study and electronegative filtration was run for all samples.

The overall comparison between the UM CFAR V2G-qPCR method and the SM RT-qPCR method shows a strong correlation between the two methods with N1 RT-qPCR detection showing a slightly stronger correlation (R 2 = 0.75, p < 0.01) in comparison to N2 RT-qPCR (R 2 = 0.63, p < 0.01) (Figure 4 ). The correlations between N1 and N2 RT-qPCR detection (R 2 = 0.87, p < 0.01) were also, as expected, strong. Correlations between V2G-qPCR and RT-qPCR results were particularly good considering that two different laboratories were used for sample concentration and considering the different amplification processes, detection technologies, and target genes. On average the results from the N1 RT-qPCR method were about 10% higher than those of the V2G-qPCR method, whereas results from the N2 RT-qPCR method were about 6% lower than those of the V2G-qPCR method. The detection limits for both the V2G-qPCR and RT-qPCR technologies were similar, on the order of a few 100"s of gc/L. SM reports a limit of detection (LOD) of 3 copies per reaction and a limit of quantitation (LOQ) of 10 copies per reaction. Assuming a 95% extraction efficiency of 5 µL RNA input in the qPCR reaction, 70 J o u r n a l P r e -p r o o f Journal Pre-proof µL of total RNA extract, and 200 mL filtration volume, then the LOD is estimated as 220 gc/L and the LOQ is estimated as 740 gc/L. Similarly for V2G-qPCR the LOD is estimated at 1 copy per reaction through validation with standards, and the LOQ at 10 copies per reaction.

Assuming a 95% extraction efficiency of 5 µL RNA input in the qPCR reaction, 30 µL of total RNA extract, and 100 mL filtration volume, then the LOD is estimated as 70 gc/L and the LOQ is estimated as 700 gc/L.

When comparing results for individual sites, the sites that detected consistently below detection limits (C3, C4, and C5) showed below detection limit values for both the V2G-qPCR and RT-qPCR method ( Figure 5 ). For the remaining sites, the N1 RT-qPCR results and the N2 RT-qPCR results were statistically not different as expected given that these were processed from the same concentrate and at the same laboratory. Of particular interest is that for all sites, V2G-qPCR and N2 RT-qPCR results were also statistically not different (p>0.05), which is particularly promising given that different laboratories and methods were used for quantification of SARS-CoV-2. The only statistical differences observed between V2G-qPCR and N1 RT-qPCR were for a subset of the sites: for C1 (p=0.049) and C2 (p=0.01). For all other sites, V2G-qpcr and N1 RT-qPCR results were not statistically different.

To enable rapid turn-around time, V2G-qPCR results were used for UM SARS-CoV-2 wastewater surveillance. Analysis results were reported to the university administration leadership within 24 hours of sample collection, to inform decision-making on human health surveillance and disease prevalence mitigation measures. The main advantage of the V2G-qPCR method is that is it simpler than the traditional RT-qPCR methods in that it does not require a separate cDNA synthesis step which reduces assay time and cost. Due to genetic modifications, V2G polymerase activity is robust when using crude cell lysates (Chovancova et al. 2017 ) and when amplifying RNA from unprocessed biological fluids, such as saliva and J o u r n a l P r e -p r o o f Journal Pre-proof urine. Similar, robust amplification efficiency has been observed in this study when using RNA purified from wastewater concentrates with optimal column elution volumes. The standard turnaround time from receipt of the sewage concentrates to SARS-CoV-2 quantitation was 2.5 hours. The reduced time for analysis is facilitated by a 50-minute cycling time for V2G-qPCR in comparison to a 1.5 to 2 hour cycling time for most RT-qPCR assays. Although results were routinely available within 24 hours of sample collection, a 12-hour turn-around time was possible and was achieved for V2G-qPCR when needed.

A wide range of SARS-CoV-2 virus concentrations were observed in wastewater ( Figure 6 ). SARS-CoV-2 levels in wastewater for some clusters (C3, C4, and C5) were consistently below detection. These clusters had warmer water and one cluster (C3) also had high salinity.

However, wastewater from cluster C2 was consistently at high levels with all factors consistent with water conducive to higher SARS-CoV-2 RNA levels (it had a lower water temperature, near neutral pH, low salinity, and moderate turbidity). On average, C2 viral levels were in the 10 3 to 10 4 gc/L range. Wastewater virus concentrations from individual buildings was also variable.

For example, wastewater from building B4 and B5 were generally below detection limits for SARS-CoV-2 RNA. Wastewater from some of the other buildings (e.g., B1 and B7) showed very high viral load levels, as high as 10 6 gc/L. As seen in Figure 5 , for virus concentration levels measured using the V2G-qPCR method, clusters C3, C4, and C5 had statistically lower SARS-CoV-2 levels relative to clusters C1 and For levels measured using RT-qPCR, clusters C3, C4, and C5 had statistically lower SARS-CoV-2 levels relative to all other sites. Based upon N1 gene quantification, the means of C1, B4, and B5 were statistically less than C2, B2, B3, and B7 (p<0.01). Results were similar based upon N2 gene quantification with the means of C1, B4, and B5 statistically less than C2, B2, and B3 (p<0.01) with the exception of B7 which was not statistically different due to the higher variation of the N2 values for this site. The variance of B7 was not statistically different from other sites except for C1 (p<0.01) and B4 (p=0.03).

Over time (Figure 6 ), results show that the variability of the wastewater from clusters was more gradual. Wastewater from individual buildings had higher variability, and generally gave a strong positive or negative signal. For example, the wastewater from building B4 had a very interesting trend. The wastewater for this building started at below detection limits, went up to the 10 4 gc/L, then one week later dropped to 10 3 gc/L, and then the week after fell to below detection limits again. Apparently a source of SARS-CoV-2 was present in the November 11 th time frame in building B4 releasing SARS-CoV-2 RNA into the sewer. The wastewater from building B5 showed similar trends with values going above and below detection limits. The wastewater from buildings B2 and B3 were more constant in the 10 3 to 10 4 gc/L range. In addition, wastewater from building B1 and B7 varied from 10 3 to 10 6 gc/L during this time frame. (Table S-1) . This association was not significant from 1 through 5th day lag and was also significant on the 6th day lag. There were differences in the association of timelagged COVID-19 positivity rates with the virus RNA in the wastewater samples when disaggregating by C1, C2, and B2. The strongest associations were observed in C2 followed by B2.

We report here on lessons learned from establishing a wastewater monitoring program to supplement human surveillance of COVID-19. From this study we observed that SARS-CoV-2 RNA concentrations in individual buildings were more variable than in clusters of buildings.

Concentrations at building and cluster scales varied by orders-of-magnitude from detection limits of 10 1 through 10 6 gc/L allowing for the log transformation of the data to observe trends.

In terms of overall quality of the wastewater, we observed that basic physical-chemical parameters were influenced by the water source but water quality parameters were not correlated with SARS-CoV-2 levels. Additionally, the potable water source that services the building is chlorinated for disinfection, the residual of which is believed to reduce bacteria in the wastewater to levels that were lower than expected.

J o u r n a l P r e -p r o o f comparable. Electronegative filtration was facilitated by the ease of obtaining supplies and provided for efficient sample splitting allowing for multiple filters to be prepared and shared among laboratories without sacrificing processing volume.

We also learned that new innovative technologies, such as V2G-qPCR, can simplify the detection and quantification of SARS-CoV-2 in wastewater. The approach was initially meant to serve as an interesting comparator for the more mainstream RT-qPCR approaches but developed into a reliable and consistent assay for quantifying SARS-CoV-2 RNA purified from wastewater. Since RNA extracted from wastewater is amplified directly with V2G-qPCR, the cDNA synthesis step is bypassed, reducing both assay time and cost. Using this new tool, it was possible to have results from the start of sampling to the end of detection within 12 hours, which is fast compared to standard techniques, and therefore can be useful for an early detection system for COVID-19.

Comparison of wastewater results against human surveillance data suggests that SARS-CoV-2 measures in wastewater can provide an early warning of impending COVID-19 outbreaks.

Results showed that high concentration of SARS-CoV-2 RNA in wastewater samples on a given day indicates undetected COVID-19 cases. These undetected cases will likely be observed 4 days after the observed increase in wastewater RNA levels. To fine tune the optimum lag time, wastewater sampling at a higher frequency (daily as opposed to weekly) would facilitate one-toone matching of COVID-19 positivity rates with measurements of SARS-CoV-2 levels in wastewater. It is possible that with daily sampling, the early warning period could be longer than 4 days allowing more time to identify positive subjects and thereby possibly reducing disease transmission.

This ongoing study required the integration of field sampling information, concentration information, and detection results from multiple laboratories that use different technologies and processing protocols. Although detailed metadata were recorded at each step of sample processing, virus quantification, and data analysis, the specific metadata parameters and descriptions were not standardized prior to the start of the project. A considerable effort was therefore spent to manually combine and harmonize the various sources of information to report the results of this study. To continue and expand SARS-CoV-2 wastewater surveillance studies and to integrate these results with those of other studies to detect SARS-CoV-2 or other wastewater datasets requires a systematic approach to metadata standardization and data harmonization. We anticipate that sample processing and analysis methods which integrate metadata standardization and data harmonization such as those utilized in high throughput clinical labs can be tailored for use as part of intense wastewater sampling programs used to gauge the health of a community.

Overall, this study showed that the challenges associated with tracking disease outbreaks associated with the COVID-19 pandemic can be met through a multi-pronged approach that integrates comprehensive human surveillance of the disease with environmental surveillance of the virus. In the case of COVID-19, the RNA of the etiologic agent of disease, SARS-CoV-2, was found to unexpectedly be excreted in urine and feces of both symptomatic and asymptomatic people. Although COVID-19 is a respiratory disease, and it would be expected in respiratory fluids, it also has been found in wastewater, allowing for an alternative approach to detecting early onset of outbreaks by measuring markers of the pathogen in wastewater. Future work should focus on expanding techniques and protocols for environmental monitoring of infectious agents for the purpose of tracking disease outbreaks.

J o u r n a l P r e -p r o o f The cumulative model included positivity rates between 7 day and a given day before/after the wastewater sampling day. In the daily-lag specific model, positivity rate was computed for a given day before/after the wastewater sampling day. Error bars correspond to 95% confidence limits on positivity rate.

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