key: cord-0873969-4dxw48wy authors: Hoar, C.; Chauvin, F.; Clare, A.; McGibbon, H.; Castro, E.; Patinella, S.; Katehis, D.; Dennehy, J. J.; Trujillo, M.; Smyth, D. S.; Silverman, A. I. title: Monitoring SARS-CoV-2 in wastewater during New York City's second wave of COVID-19: Sewershed-level trends and relationships to publicly available clinical testing data date: 2022-02-09 journal: nan DOI: 10.1101/2022.02.08.22270666 sha: f472a40e795f52afe7b9935532bdca02852f4e5e doc_id: 873969 cord_uid: 4dxw48wy New York City's ongoing wastewater monitoring program tracked trends in sewershed-level SARS-CoV-2 loads starting in the fall of 2020, just before the start of the City's second wave of the COVID-19 outbreak. During a five-month study period, from November 8, 2020 to April 11, 2021, viral loads in influent wastewater from each of New York City's 14 wastewater treatment plants were measured and compared to new laboratory-confirmed COVID-19 cases for the populations in each corresponding sewershed, estimated from publicly available clinical testing data. We found significant positive correlations between viral loads in wastewater and new COVID-19 cases. The strength of the correlations varied depending on the sewershed, with Spearman's rank correlation coefficients ranging between 0.38 and 0.81 (mean = 0.55). Based on a linear regression analysis of a combined data set for New York City, we found that a 1 log10 change in the SARS-CoV-2 viral load in wastewater corresponded to a 0.6 log10 change in the number of new laboratory-confirmed COVID-19 cases/day in a sewershed. An estimated minimum detectable case rate between 2 - 8 cases/day/100,000 people was associated with the method limit of detection in wastewater. This work offers a preliminary assessment of the relationship between wastewater monitoring data and clinical testing data in New York City. While routine monitoring and method optimization continue, information on the development of New York City's ongoing wastewater monitoring program may provide insights for similar wastewater-based epidemiology efforts in the future. a sustainable wastewater monitoring program designed for long-term, routine tracking of trends 93 in virus loads for multiple sewersheds serving a large urban population. 94 95 Methods 96 97 Sample collection and processing 98 24-h flow-weighted composite influent wastewater samples were collected from each of NYC's 99 14 WRRFs twice weekly beginning August 31, 2020. From January 31, 2021 to April 18, 2021 100 sampling was reduced to once weekly. Each composite sample consisted of eight grab samples 101 collected every three hours beginning at 7:00 AM on the sampling date. Samples were 102 transported on ice and stored at 4 °C until processing, which started within twelve hours after the 103 final grab sample was collected. For each sampling date, one of the 14 samples was analyzed in 104 duplicate and the remainder were analyzed as single samples; facilities were selected for 105 duplicate analysis on a rotating basis. A method blank containing Type I deionized water was 106 included with each set of samples to confirm the absence of contamination during sample 107 processing. Detailed descriptions of materials, methods, and data analysis are provided in the SI. 108 In brief, 40-mL aliquots of the 24-h composite samples were first pasteurized (60 ºC, 90 min), 109 and then centrifuged (5000 x g, 4 ºC, 10 min) to remove solids. The supernatant was filtered 110 (0.22 µm, cellulose acetate) and then subjected to virus concentration using polyethylene glycol 111 (PEG) precipitation (addition of 4.0 g PEG and 0.9 g NaCl followed by overnight incubation at 4 112 ºC, and centrifugation at 12,000 x g at 4 ºC for 120 min to pellet viruses). 12 The supernatant was 113 discarded and RNA was extracted from the concentrated PEG pellet using the Qiagen QiaAmp 114 Viral RNA Mini Kit with modifications (described in the SI). 115 116 A one-step RT-qPCR assay was used to quantify copies of the SARS-CoV-2 nucleocapsid (N) 118 gene, targeting the N1 region (CDC RUO Primers and Probes, Integrated DNA Technologies 13 ) 119 in triplicate reactions on a StepOnePlus Real-Time PCR System (Thermo Fisher Scientific). 120 Synthetic SARS-CoV-2 RNA covering > 99.9% of the viral genome (Twist Bioscience Control 121 1, GENBANK ID MT007544.1) served as both a positive control and standard used in a decimal 122 serial dilution for quantification of N1 gene copies. The limit of detection (LOD) and limit of quantification (LOQ) for the assay were estimated 125 from replicate standard curves as described by Forootan et al. 2017 14 and found to be 4,500 126 copies/L and 15,000 copies/L, respectively. Note that these LOD and LOQ values as well as 127 calculated sample concentrations are relative to the approximate concentration of the synthetic 128 RNA control reported by the manufacturer, as absolute quantification of the RNA control was 129 not feasible when sample analysis began. Note that quantification of the RNA control through 130 digital PCR is underway. N1 concentrations--including those of the LOD and LOQ--reported in 131 the current version of this work may therefore be updated in future versions to reflect the 132 quantified concentration of the RT-qPCR standard. Nonetheless, while the approach described 133 herein limits direct comparison of N1 concentrations to those found in other studies, it does not 134 alter trends and comparisons across facilities examined within this study. In addition, we elected 135 to use a pooled standard curve to quantify samples on all plates to ameliorate variability in 136 standard preparation by different analysts from plate to plate. A description of the analysis used 137 to motivate this decision is presented in the SI ( Figure S1 ). The absence of contamination during 138 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted February 9, 2022. ; https://doi.org/10.1101/2022.02.08.22270666 doi: medRxiv preprint RT-qPCR preparation was confirmed through no template controls included on all RT-qPCR 139 plates. Only samples quantified above the LOQ were included in subsequent analysis. From 140 September 8, 2020 to June 8, 2021, samples were collected from each facility on 72 sampling 141 dates, with samples from only two dates associated with method blanks having N1 142 concentrations above the LOD; samples collected on these two dates were flagged as 143 contaminated and were not included in subsequent analysis. An attenuated bovine coronavirus (BCoV) (Calf-Guard® Bovine Rota-Coronavirus Vaccine, 146 Zoetis) was used as a process control. 15,16 BCoV was inoculated into samples after the 147 pasteurization step (details provided in the SI). A one-step RT-qPCR assay, adapted from 148 previously published assays, 15-17 targeting the transmembrane-protein gene of BCoV was used to 149 qualitatively assess BCoV recovery for each sample using an aliquot of the extracted RNA 150 (primers and probes purchased from Integrated DNA Technologies). Detection of BCoV was 151 used to confirm whether viruses were recovered in samples for which the N1 target was not 152 detected. Additional details regarding the RT-qPCR assays, standard curves, and QA/QC 153 procedures are provided in the SI. Data analysis 156 The concentration of the N1 RNA target in wastewater ( "" ) was determined for each sample in 157 units of N1 gene copies (GC)/L according to Equation 1 , where $ is the number of N1 GC 158 measured by RT-qPCR, &'(,* is the volume of RNA extracted from each sample (60 µL), 159 &'(,$ is the volume of template RNA added to the RT-qPCR reaction (5 µL), and * is the 160 volume of wastewater sample analyzed (0.04 L). The resulting "" was then normalized by the associated daily influent wastewater flow rate 165 (i.e., the flow rate in the same facility on the same day) to calculate the SARS-CoV-2 viral 166 loading rate ( "" ) in units of N1 GC/day (Equation 2). Given that 60% of the NYC sewer 167 system is a combined stormwater-sewer system, flow-based normalization was used to account 168 for differences in per capita water usage and variability in wastewater flow rates caused by non-169 domestic water inputs (e.g., rain events), which can affect measured virus concentrations. In 170 Equation 2, is the daily flow rate at the facility in millions of gallons per day (MGD), and 171 is the conversion factor required to convert from liters to million gallons (3.78541× 10 6 L/MG). Continuous measurements of flow rate were conducted at each facility using either magnetic 173 flow meters or flow measuring weirs (with uncertainty in measurements of ~ 5%). Average daily 174 flow rates had been measured at each facility prior to the establishment of the SARS-CoV-2 175 monitoring program, and thus required no additional analysis burden, making it a logistically 176 advantageous option for normalization of virus measurements. 177 178 Statistical analyses of relationships between SARS-CoV-2 loads in wastewater and laboratory-181 confirmed COVID-19 cases 182 Relationships between SARS-CoV-2 wastewater data in each sewershed and laboratory-183 confirmed COVID-19 cases for the associated sewershed population were evaluated through 184 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. is less likely to represent all infections in a population when the percentage of positive tests 222 exceeds approximately 10%; 19,20 we therefore excluded these data in an effort to best 223 approximate the incidence of SARS-CoV-2 infections. To assess whether trends in SARS-CoV-2 viral loading rates in wastewater preceded trends in 226 clinical testing data, correlations between the two data sets were also evaluated for each 227 sewershed with the clinical data shifted back in time with lags ranging from 0 to 21 days. For 228 this analysis, additional clinical data from April 12, 2021 to May 2, 2021 was included to 229 maintain a constant number of data pairs for each number of lag days applied. 230 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted February 9, 2022. ; https://doi.org/10.1101/2022.02.08.22270666 doi: medRxiv preprint Simple linear regressions were performed using log10-transformed SARS-CoV-2 viral loading 232 rates (N1 GC/day) and log10-transformed 7-day averages of new COVID-19 cases (new COVID-233 19 cases/day) for each individual sewershed as well as for the combined data set. The combined 234 data set was assessed with and without the testing rate filter described above. Linear regressions 235 were used to estimate the equivalent number of cases/day/100,000 people associated with the 236 method LOD ( 789 ), equal to 4,500 N1 GC/L. This estimate was calculated for each facility 237 using individual, sewershed-specific linear regressions and using the linear regression for the 238 combined data set. First, the LOD was converted to a SARS-CoV-2 viral loading rate in 239 wastewater ( "",789 ) for each sewershed in units of N1 GC/day using Equation 3, where :;< 240 is the average of daily flow rates at the facility over the study period (Table S. As described above, quantification of the RT-qPCR standard for the N1 target is underway. Future updates to the N1 standard concentration will change the reported method LOD, in units 256 of N1 GC/L. However, because all sample concentrations will also be adjusted to reflect the 257 updated standard concentration, we anticipate that the resulting relationships between the 258 wastewater data and the clinical data (including the associated 789 ) should remain similar to 259 what is reported herein. Statistical analyses were performed using R, and figures were created using GraphPad Prism. 21,22 262 263 264 Results and Discussion 265 266 Methodological considerations for SARS-CoV-2 quantification in wastewater 267 The public health emergency caused by the emergence of COVID-19 required the expedited 268 development of NYC DEP's SARS-CoV-2 wastewater monitoring program. As such, several 269 methodological choices for virus quantification were considered, and the ultimate standard 270 operating procedure (SOP) described herein was developed reflecting NYC DEP's program 271 goals of monitoring trends in SARS-CoV-2 viral loads in wastewater, accounting for equipment 272 availability, existing expertise of personnel, and considerations of material procurement. Selections were also made to minimize analyst-based variability. For example, commercially-274 available kits for RNA extraction were considered over alternatives that may be more sensitive to 275 analyst skill and consistency. Data analysis and internally-developed QA/QC guidelines were 276 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted February 9, 2022. ; https://doi.org/10.1101/2022.02.08.22270666 doi: medRxiv preprint established in line with programmatic goals. Additional methodological considerations, such as 277 the inclusion of a filtration step in sample preparation, are discussed in the SI. Long-term routine monitoring to assess virus trends through quantification with RT-qPCR 280 requires reliable comparison of data originating from different RT-qPCR plates prepared by 281 different analysts, which presents several challenges. First, in the absence of a formally 282 quantified standard for the N1 RNA target, this program relied on the use of a synthetic RNA 283 control. An approximate concentration of this RNA control was provided by the manufacturer, 284 but was found to differ between lots purchased at different times. In addition, standard curves for 285 routine RT-qPCR assays were prepared by different analysts on different days, with separate 286 serial dilutions of standards performed for each individual RT-qPCR plate. To account for any 287 resulting variability caused by these aspects of the RT-qPCR quantification method, we 288 quantified the concentration of each RNA control lot relative to the original lot used and applied RT-qPCR-based quantification using a standard curve highlight the benefits of alternative 291 methods, such as digital PCR for absolute RNA quantification, which eliminates the need for a 292 standard curve and may offer more sensitive detection for environmental samples. 23 Nonetheless, 293 the methodology employed in this work allowed us to compare relative viral loads and 294 confidently assess of trends of SARS-CoV-2 in wastewater over time. population-normalized SARS-CoV-2 viral loads for each facility during this period ranged from 302 1.6 × 10 L to 6.8 × 10 L N1 GC/day/population, with many of these values occurring around the 303 time when a peak in COVID-19 cases was observed (January 2021). Note that in September of 304 2020, prior to the increase in COVID-19 cases associated with NYC's second wave of the 305 outbreak, N1 concentrations in wastewater remained below the LOQ in several sewersheds. Visual inspection of trends in SARS-CoV-2 quantities in wastewater and new laboratory-308 confirmed COVID-19 cases indicates an association between the wastewater and clinical data. 309 The strength of this association varied across sewersheds, as reflected in results from statistical 310 analysis presented in the next section. Additionally, most sewersheds exhibited peaks for both 311 data sets in January 2021 ( Figure 1 ), with two notable exceptions being Oakwood Beach and 312 Port Richmond, discussed below. Sewersheds with lower incidence rates of COVID-19 (e.g., 313 Red Hook WRRF) generally had lower per capita SARS-CoV-2 viral loads in wastewater than 314 those with higher incidence rates of COVID-19 (e.g., Hunts Point WRRF). 315 316 SARS-CoV-2 viral loads in the Coney Island WRRF influent in September 2020 and October 317 2020 displayed a high degree of variability, with some measured virus loads that were greater 318 than those in all other sewersheds during that period, despite a consistent processing method 319 applied for all samples and confirmed COVID-19 case rates that were consistently low across 320 NYC ( Figure 1 ). While there were relatively low rates of clinical testing in New York City in 321 September 2020 and COVID-19 clusters emerged in some neighborhoods served by the Coney 322 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. To account for variability in wastewater flow rates and minimize the effect of (1), viral loads 349 calculated using measured wastewater flow rates (Equation 2) were used for analysis instead of 350 N1 concentrations. Preliminary tests with an RT-qPCR inhibition control assay during method 351 optimization were used to assess the impact of factor (2) and indicated minimal inhibition (data 352 not shown). Regular assessment of inhibition with additional control assays was not feasible 353 during routine monitoring due to resource constraints. In addition, dilution of RNA, a strategy 354 used to reduce PCR inhibition, was avoided in order to maintain consistency in sample 355 processing, given that viral concentrations in samples collected during periods of low COVID-19 356 case rates were susceptible to dilution below the limits of quantification or detection. While not 357 included in this work, assessment of viral recovery and wastewater matrix effects should be 358 considered for future research aiming to characterize uncertainty in WBE data. Although beyond 359 the scope of this work, identifying and characterizing external factors related to (3) and (4) is the 360 focus of ongoing SARS-CoV-2 WBE research efforts. Considering these uncertainties and 361 variabilities in wastewater data, which likely increase with scale, 32 we did not attempt to quantify 362 the number of SARS-CoV-2 infections in each sewershed based on wastewater data, but instead 363 explored the relationship between viral quantities in wastewater and publicly available clinical 364 data to assess trends and associations, and examine differences between sewersheds. 365 366 As mentioned above, SARS-CoV-2 viral loads in wastewater from the Port Richmond and 367 Oakwood Beach WRRFs (both located in the borough of Staten Island) did not capture the peak 368 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. and wastewater data for these sewersheds, it does not explain the lower-than-expected SARS-380 CoV-2 viral loads measured in Staten Island wastewater in January 2020. A more likely 381 explanation could stem from the composition or operation of the wastewater system in the 382 borough. For example, a portion of the Staten Island population is not served by the sewer 383 system and instead uses septic systems. As such, a segment of this population does not contribute 384 to the sewer system, and viruses excreted by these residents would not have been present in the 385 influent wastewater at the Oakwood Beach and Port Richmond WRRFs. Nonetheless, given that 386 the population served by septic systems on Staten Island is thought to be smaller than those 387 served by the sewer system, it is unlikely that this hypothesis can entirely explain the 388 discrepancy between measured SARS-CoV-2 viral load and new COVID-19 cases. In addition, 389 much of Staten Island uses separated rather than combined stormwater-sewer systems, which 390 could potentially impact the wastewater matrix and influence viral recovery during concentration 391 and quantification steps in sample analysis. Because of these discrepancies, the Staten Island 392 sewersheds were excluded from analysis of the combined data set and the estimation of 393 minimum COVID-19 case rates associated with the LOD. By early June 2021, city-wide weekly averages of the percentage of positive COVID-19 clinical 396 tests declined below l%, and over 50% of NYC residents had received at least one dose of a 397 COVID-19 vaccine. 18,33 To minimize the potential impact of mass vaccination on the evaluation 398 of relationships between case rates and SARS-CoV-2 concentrations in wastewater presented in 399 this work, we chose to conduct the statistical analyses described in the following section for a 400 period ending in early April, shortly after New York State extended vaccination availability to 401 individuals of 16 years and older. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted February 9, 2022. ; https://doi.org/10.1101/2022.02.08.22270666 doi: medRxiv preprint COVID-19 case rates (cases/day/100,000, as opposed to cases/day) yielded similar results (Table 415 S.3). The correlation coefficient for the combined data set (⍴ = 0.82) was higher than for any of 416 the individual sewersheds (Figure 3 .a). Minimal differences were observed in the magnitudes of the Spearman's rank correlation 419 coefficients between clinical COVID-19 case data and SARS-CoV-2 viral loads in wastewater 420 for the data sets with and without lag times applied ( Figure S.4) . Furthermore, correlations for 421 several sewersheds--including the Wards Island WRRF--were strongest without a time lag 422 between the two data sets. Previous studies, applying a variety of assessment methods, have 423 suggested lag times between clinical testing and wastewater data ranging on the order of days to 424 weeks, while others have indicated that the SARS-CoV-2 concentration in wastewater is not a 425 leading indicator of COVID-19 diagnosis. 9 Inconsistent findings for lag times may be attributed 426 to whether clinical data are presented by the date of specimen collection or the date that results 427 are reported, as well as the adequacy of COVID-19 testing rates, which vary in different regions 428 and shift across time. Clinical data collected during periods with low testing rates are less likely 429 to capture all infections in a region, and individuals may be more likely to be tested after 430 symptom onset, at a time when viral shedding in feces may have already begun. These 431 conditions can result in a lag behind wastewater monitoring data, which provides viral load 432 information independent from clinical testing rates. Data for this work was collected during a 433 time when testing rates were significantly higher than those during the first wave of the 434 pandemic in NYC, and weekly median turnaround times for test results were 1 to 2 days. 18 435 Furthermore, we could not confidently rule out that the small improvements in correlations 436 observed when applying a lag time for some sewersheds was an artifact of variability in the 437 measured wastewater data. A rigorous assessment of lag time would also need to account for 438 contributions of previous as well as newly infected individuals to viral loads in wastewater, 439 which was beyond the scope of this work. For these reasons, we considered data without a time 440 lag for subsequent comparisons and linear regression analysis. Because the nonparametric Spearman's rank correlation was used for this analysis, results 443 suggest that there is, at minimum, a monotonic, direct relationship between SARS-CoV-2 444 quantified in wastewater and clinically confirmed COVID-19 cases. Linear relationships 445 between the two log10-transformed datasets were assessed through analysis of linear regressions, 446 with the best fit found for the Wards Island WRRF (R 2 = 0.65) and some of the poorest fits found 447 for the sewersheds in Staten Island (Figure 2 flow rate (Spearman, p > 0.05), which was expected given that N1 concentrations were 458 normalized by flow rate. Nonetheless, the linear regression found using the combined data set 459 had a strong fit (R 2 = 0.70) relative to the fits of regressions for the individual sewersheds. 460 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. indicates that trends in daily hospitalizations generally reflect trends in case rates for sewersheds 479 within each borough ( Figure S.3) . The limitations of clinical testing are in fact a major driver for 480 the application of WBE, which aims to provide community-level information free from clinical 481 testing bias. 37-39 Continued population-level monitoring from wastewater data could become 482 increasingly useful in areas where clinical testing rates decline or resources for clinical testing 483 are limited. Linear regressions for the combined data set are presented in Figure 3 with data collected on 486 dates with over 10% positive COVID-19 testing rates removed. Removing data associated with 487 potentially inadequate testing from the combined data set did not significantly change the 488 regression (Analysis of Covariance, p > 0.05) compared to the full data set without filtering 489 ( Figure S .5). After the peak in COVID-19 cases in NYC in January 2021, there was a decline in 490 cases across all sewersheds. To assess whether the relationship between SARS-CoV-2 loads in 491 wastewater and new clinical COVID-19 cases was significantly different during the period of 492 declining cases from that during the period when cases were increasing, we compared separate 493 linear regressions for the data associated with the rise in case rates (data prior to January 2021) 494 and the decline in case rates (data after January 2021). No significant differences were found 495 between the slopes of the linear regression lines determined using the full combined data set and 496 the data separated based on time period. The slope of the linear regression line for the full combined data set was found to be 0.6, 499 indicating that a 1 log10 change in the number of N1 GC/day corresponded to a 0.6 log10 change 500 in the number of new laboratory-confirmed COVID-19 cases/day in a sewershed. Metrics such 501 as these are derived from relative changes in viral load, and therefore do not require absolute 502 quantification of viral concentrations in wastewater, allowing for comparison to other studies and 503 alleviating challenges related to absolute quantification of standard curves. However, this metric 504 comparing SARS-CoV-2 loads and daily new COVID-19 cases has not been consistently 505 reported in studies monitoring SARS-CoV-2 in influent wastewater. Harmonizing data analysis 506 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted February 9, 2022. ; https://doi.org/10.1101/2022.02.08.22270666 doi: medRxiv preprint strategies to include such a metric would improve efforts to compare results across different 507 locations. The slope of 0.6 observed herein is greater than that reported previously by Wolfe et 508 al. (slope = 0.24), who compared SARS-CoV-2 concentrations measured in primary wastewater 509 settled solids and COVID-19 incidence in seven publicly owned treatment works located across 510 the United States, including one of the NYC facilities described in this work. 35 In addition to 511 analyzing a different type of sample for SARS-CoV-2 concentrations (i.e., primary settled solids 512 versus influent wastewater), the analysis used by Wolfe et al. (2021) differed from that herein in 513 that they normalized measured SARS-CoV-2 concentrations in wastewater solids by 514 concentrations of pepper mild mottle virus (PMMoV). The differences in the slopes may be due 515 to either of these factors, to variations in the relationship between SARS-CoV-2 wastewater 516 loads and COVID-19 cases in different regions, or to a difference in the overall sensitivity of the 517 methodology applied by Wolfe et al. At present, limitations regarding the accuracy of COVID-19 clinical testing data and 520 uncertainties related to SARS-CoV-2 measurements in wastewater--including SARS-CoV-2 521 shedding rates and RNA stability in different sewersheds--preclude development and validation 522 of a universal, quantitative model to predict disease incidence based on viral RNA concentrations 523 in wastewater. Ongoing research continues to expand our understanding of critical model 524 parameters and factors contributing to uncertainty, owing particularly to SARS-CoV-2 525 monitoring work completed at smaller scales (e.g., building-level), 40 from which information 526 about the contributing population can be obtained more easily than from larger sewersheds. An 527 attempt to quantify COVID-19 case rates in NYC's sewersheds based on wastewater data at this 528 time would be inaccurate, and is not currently recommended for application in the realm of 529 public health. 41 However, based on our analysis and others, there is utility in using wastewater 530 data to monitor trends in COVID-19 incidence. 531 532 Estimated case rates associated with method LOD 533 The utility of SARS-CoV-2 wastewater data depends on whether virions are present in 534 wastewater at detectable concentrations (i.e., above the LOD and LOQ). It is therefore useful to 535 approximate the minimum number of contributing COVID-19 cases per day required for 536 detection of the SARS-CoV-2 N1 gene target in wastewater using the methodology described 537 here. When estimated using individual, sewershed-specific linear regressions (Figure 2 ), the 538 minimum new COVID-19 case rate that corresponds to the method LOD varied for each 539 sewershed, ranging between 2 and 8 cases/day/100,000 people (Table S .4). Minimum detectable 540 case rates were also estimated for each sewershed using the linear regression from the combined 541 data set and the average daily influent flow rates for each WRRF during the study period. These 542 estimates fell within the same range as those derived from sewershed-specific linear regressions 543 (Table S .4). The minimum detectable case rate estimates presented here should be taken as order-of-546 magnitude approximations rather than absolute quantities, especially considering the varying 547 strength of the linear relationships between data for certain sewersheds (e.g., data sets for Coney 548 Island, Bowery Bay, Oakwood Beach, and Port Richmond WRRFs had Pearson correlation 549 coefficients below 0.5). Furthermore, these findings hold only for the specific SARS-CoV-2 550 quantification methodology applied herein, and may not be transferable to locations with 551 different per capita wastewater flow rates, even if testing rates and case rates are similar to those 552 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted February 9, 2022. ; https://doi.org/10.1101/2022.02.08.22270666 doi: medRxiv preprint described here. The estimates may also be limited by the assumption that the dominant source of 553 the SARS-CoV-2 viral load in the wastewater is from recent cases as opposed to prolonged fecal 554 shedding, which is consistent with assumptions made in previous studies. 35,42 Furthermore, 555 variability in virus shedding rates were not considered for the simple linear models in our study. 556 The relationships found are also limited by the accuracy of clinical testing data, as discussed 557 above. As COVID-19 cases declined in NYC in the spring and early summer of 2021, the estimated 560 minimum detectable COVID-19 case rates were reached in most sewersheds by May and June 561 2021. As such, we expected that SARS-CoV-2 viral loads in wastewater would have decreased 562 to below the LOQ and LOD at this time. However, viral RNA was still detectable in influent 563 wastewater collected from all sewersheds in mid June 2021 (Figure 4) . While this discrepancy 564 may be explained by the limitations described above, it may also be due to decreasing COVID-565 19 testing rates, which could result in reduced diagnosis of individuals with asymptomatic 566 infections, who are less likely to seek out COVID-19 tests. The estimated minimum numbers of COVID-19 cases required before SARS-CoV-2 can be 580 detected in wastewater from NYC sewersheds are associated with considerable disease incidence 581 that may be captured if some degree of clinical testing continues. Nonetheless, these estimates 582 could aid public health agencies in understanding what COVID-19 incidence to expect if SARS-583 CoV-2 loads measured in wastewater influent cross the threshold from being below the detection 584 limit to being detected. Improvements to analytical methods that lower the LOD 46-48 would 585 expand the utility of WBE in indicating low levels of disease incidence. 586 587 Conclusion 588 589 Critical choices made at the beginning of the development of NYC's SARS-CoV-2 wastewater 590 monitoring program proved beneficial for the long-term wastewater monitoring goals for NYC, 591 and highlight strategies that may be useful for agencies interested in implementing wastewater 592 monitoring programs for emerging pathogens. First, collaborating parties--including academic 593 partners and NYC DEP personnel--worked together to develop a monitoring program centered 594 around NYC DEP's priorities. Second, sample analysis was conducted in a NYC DEP 595 microbiology laboratory, which allowed the program to take advantage of existing equipment, 596 expertise, protocols, and resources related to wastewater analysis, as well as existing wastewater 597 sampling and transport protocols and infrastructure. Doing so expedited the initiation of the 598 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. and expertise in different WBE programs may foster the continued use of many different 621 methods rather than the adoption of one universal method. Additionally, poorly characterized 622 variability in WBE data stands in the way of the critical goal of relating viral loads in wastewater 623 to disease dynamics. Clear characterization of uncertainties related to analytical methodologies 624 would therefore facilitate interpretation of wastewater data by public health agencies. 51 625 Nonetheless, results from NYC's monitoring program show that relative trends in SARS-CoV-2 626 loads in wastewater can be evaluated and associated with trends in clinical testing data, and 627 therefore can potentially contribute to situational awareness of disease incidence in large urban 628 sewersheds. All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted February 9, 2022. ; https://doi.org/10.1101/2022.02.08.22270666 doi: medRxiv preprint There are no conflicts of interest to declare. 632 633 Note that the N1 concentrations reported in the following figures may be updated in future 660 versions of this work to reflect the quantified concentration of the RT-qPCR standard, which is 661 currently being quantified. These updates should not change observed trends reported here, as 662 described in the main text. 663 664 Figure 1 . Summary of SARS-CoV-2 wastewater data for New York City's 14 sewersheds. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted February 9, 2022. cases/day for (a) the combined data set, (b) data from the combined data set associated with 690 a rise in cases, and (c) data from the combined data set associated with a decline in cases. Data associated with potentially inadequate testing (i.e., over 10% positive tests) are not included 692 in this analysis. Linear regressions (solid lines) and associated 95% confidence intervals (dashed 693 lines) are shown along with goodness of fit R 2 values and Spearman's rank correlation 694 coefficients ( ) between N1 GC/day and new COVID-19 cases/day. All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted February 9, 2022. ; https://doi.org/10.1101/2022.02.08.22270666 doi: medRxiv preprint SARS-CoV-2 wastewater data and COVID-19 case data from The date on which the case rate first fell below the estimated minimum detectable 699 case rate (based on the sewershed-level linear regression) is indicated with a solid vertical line 700 for each sewershed. Shaded regions indicate the time period during which case rates were below 701 the estimated minimum detectable case rate. Primary (left) y-axis Error bars indicate standard deviations 703 from triplicate RT-qPCR reactions as well as standard deviations of duplicate samples Secondary (right) y-axis, red line: 7-day average of new COVID-19 707 cases/day/100,000 people in the previous 7 days. Estimated minimum detectable case rates (new 708 cases/day/100,000) needed to detect SARS-CoV-2 in wastewater, based on linear regressions 709 derived from sewershed-level data and the combined data set No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity Implementation of environmental 716 surveillance for SARS-CoV-2 virus to support public health decisions: Opportunities and 717 challenges Measurement of SARS-CoV-2 RNA in wastewater tracks community infection 721 dynamics Targeted wastewater 723 surveillance of SARS-CoV-2 on a University Campus for COVID-19 outbreak detection and 724 mitigation SARS-CoV-2 titers in wastewater 729 foreshadow dynamics and clinical presentation of new COVID-19 cases, medRxiv Omer, 733 SARS-CoV-2 RNA concentrations in primary municipal sewage sludge as a leading indicator 734 of COVID-19 outbreak dynamics, medRxiv The myriad ways sewage surveillance is helping fight COVID around the world World Health Organization, Status of environmental surveillance for SARS-CoV-2 virus: 738 Scientific Brief Wastewater 741 monitoring outperforms case numbers as a tool to track COVID-19 incidence dynamics when 742 test positivity rates are high Making waves: Defining the lead time of 744 wastewater-based epidemiology for COVID-19 COVID-19 Outbreak Protocol for safe, affordable, and reproducible isolation and quantitation of 752 SARS-CoV-2 RNA from wastewater US 755 CDC Real-Time Reverse Transcription PCR Panel for Detection of Severe Acute Respiratory 756 Syndrome Coronavirus 2 Methods to 758 determine limit of detection and limit of quantification in quantitative real-time PCR (qPCR One-Step RT-ddPCR for Detection of SARS-CoV-2, Bovine Coronavirus, and 761 PMMoV RNA in RNA Derived from Wastewater or Primary Settled Solids, protocols.io Evaluation of Sampling, Analysis, and Normalization Methods for SARS-CoV-765 2 Concentrations in Wastewater to Assess COVID-19 Burdens in Wisconsin Communities Detection of bovine coronavirus using a TaqMan-769 based real-time RT-PCR assay World Health Organization, COVID-19 -virtual press conference Which States Are Doing Enough Testing? This Benchmark Helps Settle The 776 R: A language and environment for statistical computing. R Foundation for 780 Statistical Computing Redesigning SARS-CoV-2 784 clinical RT-qPCR assays for wastewater RT-ddPCR, medRxiv Press Notice About COVID-19 788 Areas of Concern: Tuesday Detecting COVID-19 Clusters at High Spatiotemporal Resolution Uncertainties in estimating SARS-CoV-2 795 prevalence by wastewater-based epidemiology Shedding of 799 SARS-CoV-2 in feces and urine and its potential role in person-to-person transmission and the 800 environment-based spread of COVID-19 At what times during 805 infection is SARS-CoV-2 detectable and no longer detectable using RT-PCR-based tests? A 806 systematic review of individual participant data SARS-CoV-2 viral load dynamics, duration of viral shedding, and infectiousness: a 809 systematic review and meta-analysis Persistence of SARS-CoV-2 in Water and Wastewater Tracking COVID-19 with wastewater COVID-19 Vaccination Reporting Correlation 818 of SARS-CoV-2 RNA in wastewater with COVID-19 disease burden in sewersheds Scaling of SARS-CoV-2 RNA in 823 Settled Solids from Multiple Wastewater Treatment Plants to Compare Incidence Rates of 824 Laboratory-Confirmed COVID-19 in Their Sewersheds Exploring surveillance data biases when estimating the reproduction number: with 828 insights into subpopulation transmission of COVID-19 in England Letter to the Editor: Wastewater-Based 831 Epidemiology Can Overcome Representativeness and Stigma Issues Related to COVID-19 Wastewater-based epidemiology-surveillance 834 and early detection of waterborne pathogens with a focus on SARS-CoV-2, Cryptosporidium 835 and Giardia Future perspectives of wastewater-based epidemiology: 837 Monitoring infectious disease spread and resistance to the community level Enumerating asymptomatic COVID-19 cases and 841 estimating SARS-CoV-2 fecal shedding rates via wastewater-based epidemiology, Science of 842 The Total Environment Early-pandemic wastewater 846 surveillance of SARS-CoV-2 in Southern Nevada: Methodology, occurrence, and 847 incidence/prevalence considerations Detection of SARS-CoV-2 in Fecal Samples From Patients With Asymptomatic 850 and Mild COVID-19 in Korea Evaluation 853 of SARS-CoV-2 viral RNA in fecal samples Asymptomatic SARS-CoV-2 855 infected case with viral detection positive in stool but negative in nasopharyngeal samples 856 lasts for 42 days Comparison of virus concentration 860 methods for the RT-qPCR-based recovery of murine hepatitis virus, a surrogate for SARS-861 CoV-2 from untreated wastewater A 864 comparison of SARS-CoV-2 wastewater concentration methods for environmental 865 surveillance 867 Comparing analytical methods to detect SARS-CoV-2 in wastewater, Science of The Total 868 Environment -2 I. Consortium, Reproducibility and sensitivity of 36 methods to 872 quantify the SARS-CoV-2 genetic signal in raw wastewater: findings from an interlaboratory 873 methods evaluation in the Analytical methodologies for the detection of SARS-CoV-2 in wastewater: 878 Protocols and future perspectives 882 SARS-CoV-2 Wastewater Surveillance for Public Health Action