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Using detrending to assess SARS-CoV-2 wastewater loads as a leading indicator of fluctuations in COVID-19 cases at fine temporal scales: Correlations across twenty sewersheds in North Carolina [1]

['Kelly Hoffman', 'Department Of Environmental Sciences', 'Engineering', 'University Of North Carolina At Chapel Hill', 'Chapel Hill', 'North Carolina', 'United States Of America', 'David Holcomb', 'Department Of Epidemiology', 'Stacie Reckling']

Date: 2023-11

Abstract Wastewater surveillance emerged during the COVID-19 pandemic as a novel strategy for tracking the burden of illness in communities. Previous work has shown that trends in wastewater SARS-CoV-2 viral loads correlate well with reported COVID-19 case trends over longer time periods (i.e., months). We used detrending time series to reveal shorter sub-trend patterns (i.e., weeks) to identify leads or lags in the temporal alignment of the wastewater/case relationship. Daily incident COVID-19 cases and twice-weekly wastewater SARS-CoV-2 viral loads measured at 20 North Carolina sewersheds in 2021 were detrended using smoothing ranges of ∞, 16, 8, 4 and 2 weeks, to produce detrended cases and wastewater viral loads at progressively finer time scales. For each sewershed and smoothing range, we calculated the Spearman correlation between the cases and the wastewater viral loads with offsets of -7 to +7 days. We identified a conclusive lead/lag relationship at 15 of 20 sewersheds, with detrended wastewater loads temporally leading detrended COVID-19 cases at 11 of these sites. For the 11 leading sites, the correlation between wastewater loads and cases was greatest for wastewater loads sampled at a median lead time of 6 days before the cases were reported. Distinct lead/lag relationships were the most pronounced after detrending with smoothing ranges of 4–8 weeks, suggesting that SARS-CoV-2 wastewater viral loads can track fluctuations in COVID-19 case incidence rates at fine time scales and may serve as a leading indicator in many settings. These results could help public health officials identify, and deploy timely responses in, areas where cases are increasing faster than the overall pandemic trend.

Citation: Hoffman K, Holcomb D, Reckling S, Clerkin T, Blackwood D, Beattie R, et al. (2023) Using detrending to assess SARS-CoV-2 wastewater loads as a leading indicator of fluctuations in COVID-19 cases at fine temporal scales: Correlations across twenty sewersheds in North Carolina. PLOS Water 2(10): e0000140. https://doi.org/10.1371/journal.pwat.0000140 Editor: Vikram Kapoor, University of Texas at San Antonio, UNITED STATES Received: May 30, 2023; Accepted: August 14, 2023; Published: October 18, 2023 Copyright: © 2023 Hoffman et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: All wastewater data and geocoded COVID-19 cases are available by date and wastewater treatment plant here - https://covid19.ncdhhs.gov/dashboard/data-behind-dashboards. Please scroll to the "wastewater monitoring" tab on the page. Then toggle to download the "viral gene copies per person" in one csv/excel file and the "new cases per 10,000 persons" in a csv/excel file. Funding: We gratefully acknowledge the funding support provided by the Centers for Disease Control and Prevention (CDC) National Wastewater Surveillance System (NWSS) through the Epidemiology and Laboratory Capacity (ELC) Cooperative Agreement (VG, AC, SR, SB). Additionally, we acknowledge the support and foresight of Dr. Jeff Warren and the North Carolina Policy Collaboratory for the project entitled "Tracking SARS-CoV-2 in the Wastewater Across a Range of North Carolina Municipalities" which allowed NC to emerge as a leader in wastewater surveillance (RN, MS, MM, LE, AH, JS, HM, LC, AF, FR). Support was also received from the National Institute for Occupational Health and Safety (T42OH008673 to MS, LE) and the NSF RAPID program for project number 2029866 (to MS, LE) entitled "RAPID: Identifying Geographic and Demographic Drivers of Rural Disease Transmission for Improved Modeling and Decision Making". The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist.

Introduction The first lab-confirmed COVID-19 case was reported in North Carolina (NC) on March 3, 2020, and over the next two and a half years, the number of reported positive cases statewide increased to more than three million [1,2]. However, the true burden of disease far exceeded this number due to underreporting, access to testing, unreported at-home tests, asymptomatic illness and other factors [3–5]. Testing was not uniformly distributed among populations for a number of structural and cultural reasons, including unequal availability and pervasive mistrust of public health recommendations by historically marginalized persons [6–8]. As a result, there is need for non-clinical means of tracking COVID-19 trends to augment case-based reporting. One promising approach is wastewater-based epidemiology (WBE), which measures substances shed in human feces and derived from a condition of interest, such as pathogen nucleic acids or pharmaceutical metabolites, by sampling sewage containing human fecal waste and byproducts of water usage [9]. WBE has been increasingly utilized to track COVID-19 infection trends at the community level by quantifying SARS-CoV-2 RNA in sewage. Twice-weekly testing of SARS-CoV-2 loads in wastewater can provide information on changes in COVID-19 burden in the sewershed population and can be used as a method to detect periods of increasing COVID-19 cases from far fewer samples than required for clinical case reporting since wastewater samples represent pooled samples of multiple individuals [10]. Unlike case-based surveillance, wastewater surveillance does not rely on individual healthcare-seeking behavior or access to testing, which are strongly impacted by well-documented societal inequities [11]. Additionally, SARS-CoV-2 is shed in the feces of both symptomatic and asymptomatic individuals, allowing the capture of data on a range of infected individuals [12–14] at varying stages of infection. Numerous studies have shown that when clinical testing coverage is high, wastewater SARS-CoV-2 loads and documented COVID-19 cases follow similar trends and are highly correlated [15–18]. Therefore, given the cost and human resource savings, WBE may provide an effective complement to case-based surveillance that addresses some of the limitations of traditional clinical surveillance approaches. However, the values typically measured in wastewater, such as viral genome copies per liter, are not directly interpretable in terms of familiar population health metrics, like the prevalence or incidence rate of infection in a defined population. To effectively inform public health response and mitigation strategies using WBE, it is necessary to relate wastewater-based measurements to interpretable population-level metrics. One critical aspect is the temporal relationship between SARS-CoV-2 wastewater loads measured at a wastewater treatment plant (WWTP) and reported COVID-19 cases in the corresponding sewershed served by the plant [5,19]. Past work has demonstrated that increases in SARS-CoV-2 wastewater loads may occur prior to a rise in lab-confirmed sewershed COVID-19 cases in a sewershed, allowing for WBE to be used as an early warning system [4,20–22]. Such leading signals in wastewater were reported during the earlier phases of the pandemic in some North Carolina sewersheds [10,23] as well as during more recent pandemic phases [24]. As the pandemic becomes endemic, trends lasting several months have been widely reported to anticipate trends in COVID-19 infections, as later indicated by population surveillance metrics [21,22,25,26]. However, the time alignment between trends in wastewater load and trends in cases can be difficult to determine since its small temporal lead or lag may be eclipsed by the longer time scale of trends. In this situation, kernel detrending can be used to remove these longer pandemic trends and reveal shorter-term fluctuations that may help identify leads or lags in the temporal alignment of the detrended wastewater and detrended case relationship [27–31]. While the correlation between wastewater-based measurements of pathogens of concern and clinical cases over longer time periods (i.e., months) is useful for informing longer-term public health response, much less is known about short-term sub-trends (i.e., weekly or even daily), which may be more relevant for ongoing, day-to-day public health decision making. Therefore, there is a need for research to better understand and anticipate changes in COVID-19 incidence on shorter time scales to inform timely, targeted, and cost-effective public health action, particularly at the local level. Detrending the wastewater and case data is done by modeling these longer-term trends and removing them to obtain detrended wastewater loads and detrended cases, also referred to as wastewater load residuals and case residuals, respectively. If wastewater load residuals can predict a fine-scale fluctuation in case residuals, then public health measures can be taken locally and for short durations in sewersheds where cases are anticipated to rise at levels greater than that of the baseline trend. This methodology may also be applicable for other pathogens beyond SARS-CoV-2 as wastewater surveillance expands to new targets in the future. Our work aims to contribute to previous studies by refining the time scale at which correlations between wastewater and cases are assessed. Accordingly, we investigate the temporal relationship (i.e., lead or lag) that maximizes correlation between detrended wastewater SARS-CoV-2 viral loads and detrended COVID-19 clinical cases at the finest time-scale possible for 20 sewersheds across North Carolina in 2021. Furthermore, to operationalize this approach, we propose and validate a set of reproducible criteria that can be easily deployed by public health agencies to support the application of WBE approaches beyond North Carolina.

Materials and methods Ongoing wastewater-based epidemiology in North Carolina In collaboration with University of North Carolina (UNC) system researchers, the North Carolina Department of Health and Human Services (NCDHHS) was one of eight state health departments initially funded by the Centers for Disease Control and Prevention (CDC) to participate in the National Wastewater Surveillance System (NWSS). The NCDHHS NC Wastewater Monitoring Network is a multi-disciplinary collaboration between epidemiologists, laboratory scientists, water reclamation managers, environmental engineers, and public health officials with promising applications for genomic, large-scale pathogen monitoring, as well as COVID-19. The development of this state surveillance network benefited from a collaboration funded by the North Carolina State Legislature among North Carolina universities at the start of the pandemic in 2020. This group of experts created the NC Wastewater Pathogen Research Network to develop sampling techniques, laboratory capabilities, and analysis of SARS-CoV-2 in wastewater [32]. The NC Wastewater Pathogen Research Network, in collaboration with NCDHHS, established a strong foundation for WBE, and founding contributors continue to be essential partners in the NC Wastewater Monitoring Network using a framework of innovative research to inform public health surveillance and action in North Carolina. As part of the NC Wastewater Monitoring Network data collection in 2021, wastewater samples were collected twice per week by WWTP staff and shipped to the UNC-Chapel Hill Institute of Marine Sciences (IMS, Morehead City, NC) for laboratory analysis. Samples were analyzed for SARS-CoV-2 by reverse-transcription droplet digital polymerase chain reaction (RT-ddPCR) following a standardized protocol [33], for which additional details are provided in the Supporting Information [34]. Sewer network spatial data (e.g., gravity mains, force mains, manholes, pump stations) obtained from North Carolina wastewater utilities and local geographic information systems departments were used to delineate a sewershed polygon using ArcGIS Pro 2.8 (ESRI, Redlands, CA). COVID-19 clinical cases reported to NCDHHS were geocoded in ArcMap 10.7.1 (ESRI) and matched to the sewershed within which they resided using a custom composite geocoder built from state and county address data. Lastly, wastewater sample results and recorded clinical cases in the sewershed were submitted to NCDHHS and uploaded weekly the CDC NWSS analytics platform for epidemiologic trend analysis. COVID-19 cases were given a date based on the following hierarchy: date of symptom onset, date of specimen collection, and date of result. Daily incidence rates per 100,000 estimated sewershed population were calculated. Wastewater sample results were normalized to flow within each municipal utility to represent a 24-hour viral load. These analyzed data are posted publicly on the CDC COVID-19 Data Tracker and the NCDHHS COVID Dashboard (https://covid19.ncdhhs.gov/dashboard/wastewater-monitoring). No permits were required for this work. Wastewater treatment plants participate voluntarily in the NC Wastewater Monitoring Network. Relating wastewater loads and COVID-19 incidence During a ten-month study period from January 2021 through October 2021, we compared SARS-CoV-2 viral loads in influent wastewater collected at the 20 WWTPs in the NC Wastewater Monitoring Network with COVID-19 incidence in the corresponding sewersheds. Nine sites were sampled for the entire duration of the study period, two sites were sampled beginning in January and ending before October 2021, and nine sites were added in the summer and sampled from June 2021 through October 2021 (Table 1). We retrieved calculated wastewater viral loads and clinical COVID-19 incidence rates in the sewershed for each of the 20 sites from the CDC NWSS analytics platform. Twice-weekly wastewater loads were provided as the sample-specific geometric mean of measured N1 and N2 target copy numbers per liter (L) of wastewater [35], normalized by multiplying by the average daily flow and dividing by the estimated sewershed population. Half the target-specific limit of detection (LOD) was substituted for the concentration when a target was not detected in the sample (N1 LOD = 1170 copies/L, N2 LOD = 330 copies/L; see Supporting Information). The resulting population-normalized viral loads, with units of SARS-CoV-2 N gene copies (GC) per person per day (pppd), were log 10 -transformed for all analyses, which were conducted in R version 4.1.2 [36]. Code for all analyses in R Markdown format is freely available in a permanent online repository at https://osf.io/gzfb5/ [37]; as all data analyzed were publicly available, this research did not involve human subjects. PPT PowerPoint slide

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TIFF original image Download: Table 1. Characteristics of NC wastewater monitoring network sites. https://doi.org/10.1371/journal.pwat.0000140.t001 Exponential kernel smoothing is a technique used in space/time geostatistics to estimate spatial and temporal trends of environmental and health processes at a variety of spatial and temporal scales [27–31]. Here, we used exponential kernel smoothing to estimate trends in wastewater viral loads and COVID-19 incidence rates at different temporal scales. For each observed response, a smoothed estimate was obtained as the average of all observations weighted by an exponentially decaying function of the temporal distance from the estimation time point. The rate of exponential decay was determined by a smoothing range parameter, corresponding to the temporal duration below which variations in the response are smoothed out of the mean trend to retain only those variations of greater duration than the smoothing range. For a response y(t) observed at time t, the smoothed estimate was obtained as the mean trend m y (t;T) with smoothing range of duration T: (1) where y(t j ), j = 1,…N, are the observations at observation times t j and the exponential kernel smoothing weights k j are given by (2) Scaling the exponential decay function by -3 ensured that the influence of observations with temporal distance equal to the smoothing range T was diminished by ~95%, with the estimation point itself receiving the highest weight. As T increased, observations further away in time were allowed greater influence on the mean trend, increasing the extent of smoothing until converging to a constant value at the arithmetic mean of all the data for T of infinite duration. As the mean trend m y (t;T) only retained variations in the response of greater duration than the smoothing range T, we detrended the observed responses by subtracting the mean trend estimated at time t to obtain the residual response: (3) which captured the fluctuations around the trend at temporal scales shorter than the smoothing range T (including any measurement error). In short, we decomposed the signal y i (t) into a time trend that captured variation of time scales greater than T and a detrended signal that captured fluctuations of time scale shorter than T, corresponding to the shorter-term variations around pandemic trends that are of particular relevance to timely public health action. To examine the time scales at which wastewater signals may lead (i.e., precede) or lag (i.e., follow) clinical cases at North Carolina Wastewater Monitoring Network sites, we evaluated the cross-correlation between detrended wastewater viral loads, denoted , and detrended COVID-19 incidence rates across various detrending kernel smoothing ranges for observations from January—October, 2021. The cross-correlation between two time series was determined as the set of correlations between pairs of observations for different temporal offsets τ, given by (4) for which τ < 0 indicated the detrended wastewater load signal leads the detrended signal obtained from COVID-19 incidence rates; conversely, τ > 0 indicated the signal from detrended wastewater loads lags that of detrended COVID-19 incidence. We examined detrended wastewater loads and detrended COVID-19 incidence rates with detrending smoothing ranges of T = ∞, 16, 8, 4 and 2 weeks separately for each site. Because subtracting a constant does not affect correlation estimates, using the T = ∞ detrended residuals was equivalent to performing the analysis without detrending. As we anticipated nonlinear associations, we estimated Spearman’s rank correlations (ρ), which are nonparametric, to assess the monotonic relationships between the two surveillance systems for temporal offsets ranging from τ = -7 to τ = +7 days. The optimal combination of detrending smoothing range and temporal offset to characterize the lead/lag relationship between wastewater and incidence over relevant time scales was identified for each site by applying a reproducible set of criteria. For each detrending smoothing range T, starting from T = ∞ down to T = 2, we: Identified the span of consecutive lead/lag values τ for which r(τ;T) was a statistically significant positive correlation. Accepted τ if (a) it was less than 7 days (identifiable), (b) it lasted at least 2 days (persistent), and (c) it contained the maximum r(τ;T) value (predictive). Otherwise, it was rejected and deemed inconclusive. Finally, the optimal smoothing range was obtained by choosing the shortest detrending smoothing range T that successfully identified a conclusive lead or lag. Detecting fluctuations over a shorter duration is ideal because these can be addressed with more timely public health measures. We selected criteria that favor identifiability, persistence, and predictivity; however, this framework may easily be extended to additional or alternative criteria as required by the specific application.

Discussion Wastewater surveillance emerged during the pandemic as a potential leading indicator of COVID-19 infection trends in the community. Although previous research analyzed the overall correlation between SARS-CoV-2 wastewater loads and clinical cases, this analysis used kernel detrending to characterize short-term relationships and identify sub-trends. By detrending wastewater viral loads and cases in the sewershed using various kernel smoothing ranges, we were able to characterize lead/lag relationships at 15 of the 20 North Carolina sewersheds assessed using a set of reproducible criteria, reducing the proportion of inconclusive results from 90% without detrending to 25% using the optimal detrending smoothing range. Furthermore, we found that detrended wastewater loads temporally led detrended cases at almost three times as many sewersheds (N = 11) as sewersheds where detrended wastewater loads lagged detrended cases (N = 4), further highlighting the utility of wastewater as a leading indicator of COVID-19 cases in North Carolina. The optimal detrending kernel smoothing range that removed long-scale pandemic trends while retaining sufficient temporal correlation to identify lead/lag relationships was in the range of 4 to 8 weeks at 12 of the 15 sites with conclusive relationships. Because detrending with a given smoothing range retains only the variation in the observations at time scales shorter than the corresponding timeframe, this finding suggests that this approach is ideal for identifying the leading or lagging nature of wastewater and case trends in most sewersheds experiencing a sustained period of increasing SARS-CoV-2 infection rates lasting at least 4 to 8 weeks. A sustained 4 to 8 weeks increase in COVID-19 incidence corresponding to the emergence of the Delta variant (B.1.617.2) in late July 2021 was observed in wastewater loads at 19 of the 20 study sites, further supporting the wider relevance of this range during the study period. However, due to onboarding schedules, some sewersheds were only sampled for half of the study period, and the shorter sampling history appeared related to inconclusive results at these sites. A strength of our study is that we performed a lead/lag analysis across a wide-range of WWTP systems, including both rural and urban municipal systems serving sewershed populations ranging from under 4,000 to 550,000 people [16,24,38–40]. Although we identified a leading relationship in the majority of North Carolina sewersheds, those within the same county or in adjacent counties did not always exhibit the same lead/lag relationship nor have the same optimal detrending smoothing range (Fig 4). For example, we found that detrended wastewater loads led detrended cases at Charlotte 1 and Charlotte 3 but lagged detrended cases at Charlotte 2 (Fig 4, S2 Table). Wastewater led cases in both the Wilmington sewershed and the sewershed encompassing surrounding areas of New Hanover County, but the optimal detrending smoothing range was 8 weeks for the city and 16 weeks in the county, which covers a larger land area but serves fewer people (Table 1). Differences in the temporal relationship or optimal smoothing range at each sewershed could be due to conditions at a given site: virus loads measured in wastewater can be impacted by sewer network infrastructure age, sewer residence time, or weather [39,41,42], and clinical surveillance is subject to underreporting due to testing access, home test usage, or fluctuations in populations from tourists and commuters [43]. To minimize the potential impact of testing behavior on the evaluation of relationships between SARS-CoV-2 loads and COVID-19 cases presented in this work, we chose to perform the analysis for a period ending prior to November 2021, when clinical testing penetration was still relatively high and home testing was not yet widely used in North Carolina communities. Given that site-specific conditions can influence wastewater results, public health agencies leading wastewater surveillance programs in their jurisdictions may want to validate their wastewater data against other foundational COVID-19 metrics to determine how wastewater surveillance fits into their larger surveillance strategies. For states or jurisdictions less familiar with wastewater data, a lead/lag analysis between wastewater loads and reported cases would be a useful method to help understand the temporal relationship between wastewater-based pathogen and other decision-making metrics. Our method can be employed by public health agencies participating in CDC NWSS across the United States by using an R Markdown document that applies set criteria to identify the leading or lagging relationships between wastewater and reported cases [37]. As counts of reported cases become less reliable over time due to an increase in non-reportable results from at-home-testing kits, as well as an overall reduction in PCR-based, reportable, COVID-19 clinical testing, this method can be adapted to utilize surveillance metrics besides cases, including hospitalizations, emergency department visits (syndromic surveillance data), or mortality [17]. Results from our analysis characterizing the shortest time ranges at which wastewater loads are associated with cases have been formative in elevating wastewater as a reliable metric for tracking trends in North Carolina, not only to anticipate the start of long-term cycles (such as the start of elevated rates in winter), but also for short duration fluctuations within any given long-term cycle. The leading nature of wastewater-based COVID-19 findings at most sites provides the foundation and rationale for including wastewater loads as an early warning metric alongside reported cases, emergency department visits, and hospitalizations, which are highlighted on statewide data surveillance dashboards such as the NCDHHS COVID-19 dashboard (https://covid19.ncdhhs.gov/dashboard/wastewater-monitoring). In under two years, COVID-19 wastewater surveillance in the United States expanded from 8 pilot state health agencies participating in the CDC National Wastewater Surveillance System in 2020 to 50 states, 5 cities, 3 territories, and 7 tribes participating in 2023 [44]. Similarly, the global portal expanded to cover 72 countries, reporting for 4,648 sites, indicating widespread use of wastewater surveillance data [45]. With the explosive growth in both the academic literature on, and implementation of, wastewater surveillance programs globally, public health professionals developed a wide range of approaches to utilizing wastewater data for decision making. Our method shows how detrended wastewater loads can predict finer scale fluctuations in detrended cases, which can allow public health officials to respond more locally and timely when COVID-19 burden, or other disease burden as wastewater surveillance expands to new targets, is increasing at levels greater than the baseline trend. Examples of mitigation strategies that can be deployed at local levels and for short durations, while being complementary to long lasting statewide measures, may include the following: (a) officials could quickly alert local hospitals about a potential increase in cases above the statewide trend and provide recommendations to community leaders to implement short-duration restrictions, such as limiting indoor gatherings and reducing business capacity [46]; (b) jurisdictions could mobilize pop-up testing and take steps to increase vaccination in the community [47]; (c) increasing public health communications regarding masking, handwashing, vaccination, and social distancing to help contain the spread of the virus; and d) interacting with local public health officials and hospital administrators to indicate periods of higher ICU bed, PPE, and medical staffing needs. This has already been observed during a large sport fishing tournament that took place in a small coastal North Carolina sewershed where NCDHHS notified local health department and city officials of an increase in wastewater viral load. In response to this increase, local health department and city officials reinforced recommended mitigation strategies outlined in the Governor’s Executive order to the event leadership, like additional hand-washing stations and frequent disinfection of high touch surfaces (Nina Oliver, Carteret County Health Director, personal communication, June 21–22 2021 & February 6, 2023). Local notices were also used to encourage the surrounding community to take precautions through vaccinations, masking, social distancing, and frequent handwashing [48]. Immediately following the event, county and city officials met routinely to review wastewater, as well as other COVID-19 metrics, and to ensure levels were decreasing (Nina Oliver, personal communication, February 6, 2023). Additional program evaluation is needed to understand the efficacy of these public health actions; evaluation is ongoing in NC. As public health officials and the scientific community continue to rely on wastewater surveillance both for large-scale pandemic decision-making and localized action as described here, there is a growing need for increasing equitable access to wastewater services, particularly in cases of municipal underbounding, and for investing in substantial infrastructure improvements. This is especially important in jurisdictions like North Carolina, where half of households rely on private septic and package treatment plants [49]. In some cases, racial disparities in access to and disproportionate exclusion from municipal water and sewer service have been documented [49–51]. In other areas, distance, lack of gradient, and groundwater height play a role in decisions to use centralized versus decentralized waste treatment systems. For wastewater to continue to be useful for disease tracking and public health decision-making beyond COVID-19, additional resources are needed to achieve equitable access to centralized wastewater treatment where it is desired and environmentally relevant. In other rural areas where this is not the case, we need to improve our technical capabilities to characterize decentralized waste systems.

Acknowledgments We gratefully acknowledge Jane Hoppin, and other UNC system researchers who were involved building the 2020 NC Wastewater Pathogen Research Network (NC WW Path); these partners conducted integral research to inform and launch the state surveillance system (NC Wastewater Monitoring Network) in January 2021. We also wish to thank Nina Oliver, the Carteret County Health Director, as well as local health departments, county and city officials, and wastewater utilities participating in wastewater monitoring across NC. We appreciate your tireless work to protect the public’s health.

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