Spatial aggregation methods for interpreting wastewater concentrations at jurisdictional scales: Insights from two SARS-CoV-2 monitoring programs

Spatial aggregation methods for interpreting wastewater concentrations at jurisdictional scales: Insights from two SARS-CoV-2 monitoring programs | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Spatial aggregation methods for interpreting wastewater concentrations at jurisdictional scales: Insights from two SARS-CoV-2 monitoring programs Elana M. G. Chan, Elisabeth Burnor, Alessandro Zulli, Chunye Lu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8205614/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Spatial aggregation of wastewater concentrations is necessary to summarize wastewater monitoring data across multiple wastewater treatment plants (WWTPs) because public health practitioners often enact public health action at broader spatial scales. We applied various approaches for spatially aggregating wastewater concentrations and evaluated how well aggregated wastewater metrics correlated with clinical disease metrics on the same spatial scale. We used wastewater SARS-CoV-2 RNA concentrations from two wastewater monitoring programs. One included 188 WWTPs across the USA and a single laboratory; the other included 78 WWTPs across California and two distinct laboratories. We spatially aggregated wastewater concentrations across WWTPs using the following approaches: median, unweighted average, and population-weighted average. We considered wastewater concentrations with and without normalization by pepper mild mottle virus RNA concentrations and with and without transformation using the wastewater viral activity level prior to aggregation. For the single laboratory program, we spatially aggregated wastewater concentrations to the state, Health and Human Services (HHS) region, and USA spatial scales. For the multi-laboratory program, we spatially aggregated wastewater concentrations to the county and state (California) spatial scales. We then assessed the correlation between spatially aggregated wastewater metrics and clinical COVID-19 test positivity on a weekly basis for the following spatial scales: California, HHS region, and USA. Wastewater metrics and test positivity were positively and significantly correlated using all spatial aggregation methods at each spatial scale considered, and no method was superior. Public health practitioners should adopt a spatial aggregation method that is suitable for the setup of a wastewater monitoring program. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1 Introduction First used in the 1940s to monitor poliovirus [ 1 – 3 ], wastewater monitoring is an approach to infectious disease surveillance that gained traction during the Coronavirus Disease 2019 (COVID-19) pandemic to monitor SARS-CoV-2, the causative virus of COVID-19 disease. Unlike case surveillance, which is subject to reporting delays and testing biases, wastewater concentrations of infectious disease biomarkers can be available within 24 hours of sample collection and represent an entire contributing population—regardless of individuals’ symptom presentation or access to healthcare. However, wastewater concentrations of pathogen quantity may vary between wastewater catchment areas (sewersheds), even if the viral contributions to a wastewater stream are equal. This variability may be due to sewershed factors, such as population size and non-human inputs (e.g., rainfall, industrial runoff), and differences in laboratory processing methods [ 4 , 5 ]. Due to these variations, it is not always clear if measurements made for different sewersheds are directly comparable to each other or if they can be aggregated across multiple sewersheds. Despite these challenges, there is a need to compare wastewater concentrations between wastewater treatment plants (WWTPs) and laboratory methods and to spatially aggregate wastewater concentrations across multiple WWTPs and wastewater monitoring programs to improve the value and interpretability of wastewater monitoring data. Spatial aggregation aims to combine data collected over more than one geographic unit (e.g., WWTP sewersheds) to a single spatial scale (e.g., state). Measured wastewater concentrations represent individual WWTP sewersheds, which often do not align with municipal boundaries (e.g., one large city may have multiple sewersheds within its city limits). Additionally, public health practitioners must understand what these data mean for broader spatial scales, such as the local county, state, regional, or national scales. Public health agencies and local health jurisdictions enact public health action, response, and policymaking on these broader spatial scales. Accurate summaries of wastewater monitoring data across multiple WWTPs are needed to effectively use the data to inform public health actions, both at local levels and at state and national levels [ 6 ]. Comparisons between WWTPs and spatial aggregation are complicated by differences in population and sewer network characteristics, sampling and analytical methods, and data processing approaches [ 4 , 5 , 7 – 15 ]. Several studies have assessed measurement variability arising from various site and methodological differences [ 4 , 5 ]. Studies investigating variability from data processing have focused on different approaches for handling non-detect concentrations [ 9 , 10 ], normalizing concentrations [ 11 , 12 ], temporally smoothing concentrations [ 13 , 14 ], and transforming concentrations [ 8 , 15 ]. There has been limited work focused on aggregation approaches as an important data processing step. Two different spatial aggregation methods are currently commonly used for wastewater concentrations: (1) population-weighted averages of non-transformed wastewater concentrations and (2) medians of wastewater concentrations that are transformed using the US Centers for Disease Control’s (CDC) National Wastewater Surveillance System (NWSS) team’s wastewater viral activity level method (WVAL). For a population-weighted average, wastewater concentrations are averaged across geographic units in an aggregate area, weighted by the population of individual geographic units (e.g., the service population of WWTP sewersheds in a state) [ 6 , 8 , 16 – 21 ]. Population weighting aims to adjust for differences in population size among the individual geographic units. Alternatively, the WVAL method involves first transforming wastewater concentration for individual sewersheds to a comparable scale and then spatially aggregating by calculating the median WVAL across geographic units [ 15 , 22 , 23 ]. To our knowledge, there have not been studies that systematically compare these spatial data aggregation approaches for interpreting wastewater monitoring data. An ideal spatial aggregation method for wastewater concentrations should correlate with case surveillance metrics of disease occurrence at the same spatial scales; work with an amalgamation of wastewater matrices, laboratory methods, reporting units, and normalization methods; and be simple to implement and interpret. Here, we spatially aggregated wastewater concentrations using multiple methods and evaluated how well spatially aggregated wastewater metrics correlated with test-positivity (a case surveillance metric) on the same spatial scale. We used wastewater concentrations collected using different sampling and analytical methods and with different data processing treatment to reflect the lack of standardization across wastewater monitoring programs. Previous work evaluating variability arising from other data processing steps focused on SARS-CoV-2 as a case study [ 9 , 11 – 15 ], so we also chose to focus on SARS-CoV-2 RNA wastewater concentrations for this analysis given the availability of longitudinal wastewater SARS-CoV-2 RNA datasets and publicly available COVID-19 case surveillance data. 2 Methods We applied 12 spatial aggregation methods to wastewater SARS-CoV-2 RNA monitoring data and evaluated each method using comparisons to clinical COVID-19 test positivity (Fig. 1 ). Data sources and data processing steps are described in detail below but, briefly, the methods differed in their normalization, transformation, and spatial aggregation approaches. The approaches to handle non-detect concentrations (i.e., wastewater concentrations below a laboratory method’s lower detection limit) and temporally smooth concentrations remained consistent across methods. Because we evaluated correlations between spatially aggregated wastewater metrics and test positivity on a weekly basis, we averaged wastewater metrics across all samples collected each week for each method prior to spatial aggregation. We considered several spatial scales to evaluate the various spatial aggregation methods, depending on the spatial coverage of the data sources described next. All calculations and analyses were conducted in R (version 4.5.0). Twelve unique spatial aggregation methods were applied to wastewater monitoring data. Methods differed with respect to normalization, transformation, and spatial aggregation approaches; approaches for non-detect handling, temporal smoothing, and temporal aggregation were consistent across methods. Only methods involving PMMoV-normalized wastewater SARS-CoV-2 RNA concentrations (n = 6) were applied to data from the California, multi-laboratory monitoring program. Methods involving both unnormalized and PMMoV-normalized wastewater SARS-CoV-2 RNA concentrations (n = 12) were applied to data from the national, single laboratory monitoring program. Spatially aggregated wastewater SARS-CoV-2 RNA metrics were then correlated with COVID-19 test positivity at corresponding spatial scales. Abbreviations: g/g = gene copies per gram, PMMoV = pepper mild mottle virus. 2.1 SARS-CoV-2 wastewater monitoring We obtained wastewater concentrations of SARS-CoV-2 RNA from two distinct wastewater monitoring programs. For this analysis, we used concentrations between the first epidemiological week (i.e., Sunday–Saturday) of 2023 through the last epidemiological week of 2024 (1 January 2023 to 28 December 2024) from each program. All data used for this study are available through the Stanford Digital Repository ( https://purl.stanford.edu/tg451qc5869 ). The first program is a national wastewater monitoring program for the USA in which all wastewater concentrations are contributed by a single laboratory (hereafter referred to as the single laboratory program) [ 24 ]. During the analysis period, 188 WWTPs across 40 states and the District of Columbia had routine SARS-CoV-2 RNA monitoring (Fig. 2 a, Table S1 ). Wastewater samples were collected from WWTPs three times per week on average, and SARS-CoV-2 RNA and pepper mild mottle virus (PMMoV) RNA were measured in the wastewater solids of all samples in gene copies per gram (gc/g) using droplet digital reverse transcription polymerase chain reaction (ddRT-PCR) following environmental molecular biology best practices. PMMoV is a highly abundant virus in wastewater, originating in the human diet, and serves to correct for differences in viral recovery and the human fecal strength of wastewater [ 25 , 26 ]. A total of 50,708 wastewater concentrations of SARS-CoV-2 RNA and PMMoV RNA were reported during the analysis period. Detailed methods and data are available in a data descriptor by Boehm et al. [ 24 ] and through the Stanford Digital Repository ( https://purl.stanford.edu/hj801ns5929 ). The second program is a California statewide wastewater monitoring program in which wastewater concentrations are contributed by multiple laboratories (hereafter referred to as the multi-laboratory program) [ 27 ]. For this analysis, we used data contributed by two distinct laboratory methods. Although the program includes data from additional laboratories, we only used concentrations for which complete methodological details were documented and available to the author team. For the subset of data with complete methods available, 78 WWTPs across 40 counties had routine SARS-CoV-2 and PMMoV RNA monitoring during the analysis period (Fig. 2 b, Table S2 ). Some of these WWTPs were also represented in the single laboratory program described above. Wastewater samples were collected from WWTPs three times per week on average, and SARS-CoV-2 RNA and PMMoV RNA were measured by at least one of the two laboratories. The first laboratory is the same laboratory used by the national program and uses the methods described above, reporting concentrations in gc/g of wastewater solids. The second laboratory also uses ddRT-PCR to quantify viral RNA, although with different concentration and extraction methods than the first laboratory. Namely, concentrations are measured in the liquid fraction of wastewater and reported in gene copies per liter (gc/L). See the Supporting Information (SI) and Table S3 for complete methodological details for the second laboratory. We excluded concentrations with reported quality control issues (e.g., samples not stored at correct temperature). Refer to the SI for complete quality control procedures. A total of 18,224 and 6,801 wastewater concentrations of SARS-CoV-2 RNA and PMMoV RNA were reported by the first and second laboratory, respectively, during the analysis period. Data are available through the California Health and Human Services Open Data Portal ( https://data.chhs.ca.gov/dataset/wastewater-surveillance-data-california ). (a) National, single laboratory monitoring program. WWTPs are represented by black circles; states are colored by HHS region. (b) California, multi-laboratory monitoring program. Counties are colored by the number of participating wastewater treatment plants; gray counties did not have any participating WWTPs. Abbreviations: HHS = Health and Human Services, WWTP = wastewater treatment plant. Created in ArcGIS Pro (version 3.1.1) using state and county cartographic boundaries from the US Census Bureau [ 28 ]. 2.2 COVID-19 case surveillance We obtained COVID-19 case surveillance data from two sources. To match the time period of the SARS-CoV-2 RNA wastewater monitoring data, we used data between 1 January 2023 and 28 December 2024 from each source. The National Respiratory and Enteric Virus Surveillance System (NREVSS) is a laboratory-based sentinel surveillance system whereby participating laboratories voluntarily report to the CDC the number of nucleic acid amplification tests administered and the number of those tests that were positive for SARS-CoV-2 each epidemiological week (defined as Sunday to Saturday); results from antigen, antibody, and at-home tests are excluded [ 29 ]. We obtained weekly clinical COVID-19 test positivity for the USA and each Health and Human Services (HHS) region for this analysis ( https://data.cdc.gov/Laboratory-Surveillance/Percent-Positivity-of-COVID-19-Nucleic-Acid-Amplif/gvsb-yw6g/about_data ). NREVSS does not report test positivity for individual states [ 29 ]. For some weeks, multiple test positivity values were posted for a given spatial scale. We used the most recently posted test positivity value each week for this analysis. The California Department of Public Health (CDPH) reported total tests and positive tests for COVID-19 each day on its respiratory virus dashboard ( https://data.chhs.ca.gov/dataset/respiratory-virus-dashboard-metrics ) [ 30 ]. At the time of this analysis, these data, which are received by CDPH through electronic laboratory reporting of COVID-19 test results among residents of California, were only reported publicly at the state scale [ 30 ]. To match the reporting frequency of NREVSS and because testing data are prone to weekend bias [ 31 , 32 ], we determined weekly clinical COVID-19 test positivity for California by dividing the total positive tests by the total tests administered each epidemiological week (defined as Sunday to Saturday) to use for this analysis. 2.3 Wastewater data processing Prior to spatially aggregating wastewater concentrations, we considered different data processing approaches with respect to data normalization and transformation. We used the same data processing approach to handle non-detects, temporally smooth, and temporally aggregate wastewater concentrations for all spatial aggregation methods (Fig. 1 ). Details about each data processing step preceding spatial aggregation are described below. Non-detect concentrations . First, we used single imputation to handle non-detect concentrations, which comprised 1.6% (n = 799) of concentrations from the single laboratory program and 0.53% (n = 131) of concentrations from the multi-laboratory program. Handling non-detect concentrations aims to reduce bias in subsequent inferences. Although single imputation can amplify inference bias when the wastewater target is present in low concentrations with a high proportion of non-detects [ 9 , 10 ], SARS-CoV-2 was nearly always detected in the wastewater monitoring data during the study period used for this analysis. Any bias arising from the use of single imputation was likely negligible. Specifically, we set any non-detect concentrations to half the assay limit detection. The assay limit of detection was approximately 1,000 gc/g for the single laboratory program and varied for the multi-laboratory program (500 gc/g or 1,100 gc/g for solids concentrations and 1,000 gc/L for liquids concentrations) [ 24 , 27 ]. Normalization . Second, we normalized SARS-CoV-2 RNA wastewater concentrations by PMMoV RNA wastewater concentrations. As described previously, PMMoV is an indigenous wastewater virus of dietary origin. PMMoV-normalization aims to correct for differences in viral recovery and fluctuations in the human fecal strength of wastewater [ 25 , 26 ]. Process-based modeling also conceptually shows that PMMoV-normalized SARS-CoV-2 RNA wastewater concentrations should scale with disease incidence rate [ 33 ]. We considered concentrations from the single laboratory program both with and without normalization for reference, given that all concentrations were reported using consistent units (gc/g). We only considered concentrations from the multi-laboratory program with normalization, given that reported concentrations did not have consistent units across laboratories. For the multi-laboratory program, a subset of sewersheds were monitored by multiple laboratories during part or all of the study period, resulting in days where multiple SARS-CoV-2 RNA and PMMoV RNA concentrations were reported for a single WWTP. We calculated the arithmetic mean of all reported PMMoV-normalized SARS-CoV-2 RNA concentrations to obtain a single PMMoV-normalized SARS-CoV-2 RNA concentration per WWTP each day. Temporal smoothing . Third, we temporally smoothed wastewater concentrations for each WWTP using a simple moving average with truncation. Smoothing aims to reduce the effects of outlier concentrations. Specifically, we calculated the five-sample, centered, trimmed, moving average of wastewater concentrations for each WWTP as done previously [ 18 – 20 , 24 , 34 ]. Trimming refers to the removal of the highest and lowest value in the moving average window prior to calculating the average. To calculate moving average values at the start and end of a time series, we used a shrinking window (e.g., a three-sample window was used to calculate the moving average value for the first sample at a WWTP). Transformation . Fourth, we considered two types of wastewater monitoring metrics for data analysis: (1) wastewater concentrations and (2) WVAL values. Wastewater concentrations (either unnormalized or normalized by PMMoV) refer to concentration values without any further data transformation (Fig. 1 ). WVAL values refer to wastewater concentrations that are transformed to represent the number of standard deviations above a prespecified baseline; they are reported on a linear scale [ 35 ]. Transformation aims to allow for comparison of concentrations across WWTPs and laboratories—regardless of sampling, analytical, or normalization approaches. The WVAL metric was developed by the CDC’s NWSS program, and we calculated the WVAL metric using unnormalized, smoothed wastewater concentrations and PMMoV-normalized, smoothed wastewater concentrations (Fig. 1 ). See the SI for further details about the WVAL calculation steps as of March 2025 [ 35 ]. Temporal aggregation . Lastly, we temporally aggregated wastewater monitoring metrics on a weekly basis (Sunday–Saturday) at each WWTP for data analysis. To do so, we averaged wastewater metric values (either wastewater concentration or WVAL values) across all wastewater samples collected each week at each WWTP, similar to a method used by the CDC [ 22 ]. 2.4 Spatial aggregation of wastewater data To spatially aggregate weekly wastewater metrics (either concentrations or WVAL values) across multiple geographic units (e.g., WWTP sewersheds, counties, states), we considered three spatial aggregation approaches: (1) median, (2) unweighted average, and (3) population-weighted average. Median refers to calculating the median of the metric across geographic units (Eq. 1); unweighted average refers to calculating the arithmetic mean of the metric across geographic units (Eq. 2); and population-weighted average refers to calculating the weighted arithmetic mean of the metric across geographic units where the weights represent the population of each geographic unit (Eq. 3). Equation 1. \(\:Median\left(X\right)=X\left[\frac{n+1}{2}\right]if\:n\:is\:odd;\:\frac{X\left[\frac{n}{2}\right]+X[\frac{n}{2}+1]}{2}if\:n\:is\:even\) X: ordered list of wastewater metrics across all geographic units in the spatial aggregation area n: number of geographic units in the spatial aggregation area Equation 2. \(\:Unweighted\:average=\frac{\sum\:_{i=1}^{n}{x}_{i}}{n}\) x i : wastewater metric value of the i th geographic unit in the spatial aggregation area n: number of geographic units in the spatial aggregation area Equation 3. \(\:Population\:weighted\:average=\frac{\sum\:_{i=1}^{n}{p}_{i}{x}_{i}}{\sum\:_{i=1}^{n}{p}_{i}}\) p i : population of the i th geographic unit in the spatial aggregation area x i : wastewater metric value of the i th geographic unit in the spatial aggregation area n: number of geographic units in the spatial aggregation area Given we only considered both normalized and unnormalized wastewater concentrations for the single laboratory program, we evaluated 12 unique spatial aggregation methods for the single laboratory program dataset and 6 unique spatial aggregation methods for the multi-laboratory program dataset (Fig. 1 ). For the single laboratory program, we spatially aggregated weekly wastewater metrics across WWTPs to the state scale and then across states to the national and HHS region scales (Fig. 3 ). For the multi-laboratory program, we spatially aggregated weekly wastewater metrics across WWTPs to the county scale and then across counties to the California scale (Fig. 3 ). For data analysis, we only used aggregated wastewater metrics for California, each HHS region, and the USA spatial scales (Fig. 3 ). Wastewater metrics spatially aggregated for other states or counties were intermediary and not used for the data analysis described next. Table S1 and Table S2 list the sewershed population estimate of each WWTP in the single laboratory and multi-laboratory programs, respectively, and its associated state or county. We obtained county and state population estimates from the US Census Bureau [ 36 , 37 ]. Figure 2 a displays the HHS region of each state. For the national, single laboratory monitoring program, weekly wastewater metrics were spatially aggregated across WWTPs to the state scale and then across states to the national or HHS region scale. For the California, multi-laboratory monitoring program, weekly wastewater metrics were spatially aggregated across WWTPs to the county scale and then across counties to the California scale. The number in parentheses refers to the number of unique geographic units reporting wastewater concentrations within each spatial scale. Note that the District of Columbia is included as a unique state for the national, single laboratory monitoring program. Abbreviations: HHS = Health and Human Services, WWTP = wastewater treatment plant. 2.5 Data analysis We assessed the correlation between weekly COVID-19 test positivity and weekly SARS-CoV-2 wastewater metrics for each wastewater monitoring program dataset at various spatial scales. Time series data were not always normally distributed (Shapiro-Wilk test, p < 0.0001), so we used Kendall’s tau correlation test to assess the null hypothesis that weekly COVID-19 test positivity is not temporally correlated with weekly SARS-CoV-2 wastewater metric. Using wastewater monitoring data from the single laboratory program, we evaluated the correlation at the California, HHS region, and USA spatial scales (Table 1 ). For each spatial scale, we tested the correlation 12 times (once for each possible aggregation method). Using wastewater monitoring data from the multi-laboratory program, we evaluated the correlation at the California spatial scale only (Table 1 ). We tested the correlation 6 times (once for each possible aggregation method). To account for multiple testing at each spatial scale, we used an adjusted significance level of 0.05 divided by the total number of correlation tests conducted at the spatial scale (Table 1 ). At the USA and HHS region spatial scales, we conducted 12 correlation tests each (adjusted significance level: 0.05 / 12 = 0.004). At the California spatial scale, we conducted 18 total correlation tests (adjusted significance level: 0.05 / 18 = 0.003). We rejected the null hypothesis if the p value associated with Kendall’s tau estimate was less than the adjusted significance level. We used the cor.test function from the stats library [ 38 ] to conduct Kendall’s tau correlation tests and the KendallTauB function from the DescTools library [ 39 ] to determine the 95% confidence interval of Kendall’s tau estimates. Table 1 Kendall's Tau Correlation Tests Between Weekly Test Positivity and Weekly Wastewater Metric Spatial Scale Data Source: COVID-19 Test Positivity Data Source: SARS-CoV-2 Wastewater Monitoring Number of Aggregation Approaches a Adjusted Significance Level USA NREVSS Single laboratory program 12 0.05 / 12 = 0.004 HHS Regions NREVSS Single laboratory program 12 0.05 / 12 = 0.004 California CDPH Single laboratory program 12 0.05 / 18 = 0.003 California CDPH Multi-laboratory program 6 0.05 / 18 = 0.003 a The number of spatial aggregation approaches represents the number of Kendall’s tau correlation tests conducted. 3 Results Weekly COVID-19 test positivity and wastewater SARS-CoV-2 metrics using each spatial aggregation method are shown in Fig. 4a–e for the California spatial scale, Fig. S1–10 for each HHS region, and Fig. 6a–c for the USA spatial scale. Test positivity and wastewater metrics were positively, temporally, and significantly correlated for all aggregation approaches at all spatial scales considered (median tau: 0.56; range: 0.39–0.72; p < 0.0001) ( Table S4–6 ). Kendall’s tau correlation coefficients with 95% confidence interval error bars are shown in Fig. 4f–g for the California spatial scale, Fig. 5 for each HHS region, and Fig. 6d for the USA spatial scale. Overlapping 95% confidence intervals do not always indicate statistically insignificant differences [40], but herein we interpret overlapping 95% confidence intervals as suggesting differences are not substantial. Spatial aggregation methods using a PMMoV-normalized wastewater value generally resulted in a stronger correlation than the counterpart aggregation method using an unnormalized wastewater value, but differences were not substantial as suggested by overlapping 95% confidence intervals ( Fig. 4f–g , Fig. 5 , Fig. 6d ). Across all spatial scales using data from the single laboratory program (for which both PMMoV-normalized and unnormalized concentrations were considered), the median tau was 0.57 (range: 0.39–0.72) for methods using a PMMoV-normalized wastewater value versus 0.54 (range: 0.41–0.71) for methods using an unnormalized wastewater value. Similarly, any observed differences between wastewater metric type (wastewater concentrations versus WVAL values) and among spatial aggregation approaches (median versus unweighted average versus population-weighted average) were not substantial as suggested by highly overlapping 95% confidence intervals ( Fig. 4f–g , Fig. 5 , Fig. 6d ). Across all spatial scales, the median tau was 0.56 (range: 0.39–0.72) for methods using non-transformed wastewater concentrations versus 0.56 (range: 0.40–0.71) for methods using WVAL values. Additionally, the median tau was 0.56 (range: 0.42–0.68) for methods using a median, 0.56 (range: 0.39–0.69) for methods using an unweighted average, and 0.56 (range: 0.40–0.72) for methods using a population-weighted average across all spatial scales. At the California spatial scale, wastewater monitoring data from two distinct monitoring programs were considered. For the spatial aggregation methods that were considered for both monitoring programs, the correlations between test positivity and wastewater metrics were similar between programs, with only minor differences observed that were not substantial as suggested by overlapping 95% confidence intervals ( Fig. 4f–g , Table S4 ). It is hard to assess whether these minor differences in correlation strength are due to differences in laboratory methods or differences in population coverage as the multi-laboratory program includes more California WWTPs and higher population coverage than the single laboratory program. 4 Discussion Measured wastewater concentrations represent individual WWTP sewersheds. Aggregation of wastewater concentrations across WWTPs is essential for interpreting data at spatial scales that are relevant to public health jurisdictions as public health decisions are often enacted at these broader spatial scales. Here, we evaluated methods for spatially aggregating wastewater monitoring data using comparisons to clinical test positivity. The methodological approaches we considered included computing the median, unweighted average, or population-weighted average of wastewater concentrations across geographic units within a spatial scale. We additionally assessed differences in spatially aggregating non-transformed versus transformed wastewater metrics and using PMMoV-normalized versus unnormalized wastewater concentrations to determine such metrics. All spatial aggregation methods we considered were positively and temporally correlated with test positivity. An optimal spatial aggregation method should work with a mix of wastewater matrices, laboratory methods, reporting units, and normalization methods and be practical to implement and interpret. We did not observe substantial differences among the three methodological approaches (median, unweighted average, population-weighted average) or between the two wastewater metric types (wastewater concentrations versus WVAL values) in terms of correlation with test positivity. Nonetheless, some approaches are easier to interpret. Conceptually, population weighting makes epidemiological sense: geographic units with larger populations represent more people in the aggregate area, and it is reasonable to give these geographic units greater weight than geographic units representing smaller populations when attempting to summarize overall disease activity in the broader region. The WVAL metric can better account for differences in wastewater matrices, laboratory methods, reporting units, and normalization methods because, prior to aggregation, wastewater concentrations are standardized by converting measured concentrations to laboratory- and site-specific standard deviations above a laboratory- and site-specific baseline. Delatolla et al. [15] implemented the WVAL framework using SARS-CoV-2 RNA wastewater concentrations collected across Ontario, Canada and demonstrated its use for interpreting regional trends in disease occurrence. However, the WVAL metric may be challenging to interpret in retrospective analyses because the baseline reference date used for the WVAL calculation differs among WWTPs. For example, if one WWTP stops monitoring in May, its baseline would be calculated using the past 12 months of data from the preceding January 1. After July 1, its baseline would not be recalculated whereas the baselines of all other WWTPs would be recalculated using the past 12 months of data from July 1. The WVAL method has several other limitations, including its highly variable performance at the individual WWTP level, making it difficult to compare standardized WVAL values between WWTPs [8,41]. WWTPs also cannot be included in analyses using the WVAL until they have at least 6 weeks (8 weeks as of August 2025) of concentration data available [22,35]. In contrast, the use of non-transformed wastewater concentrations allows WWTPs to be included in analyses as soon as monitoring begins and avoids imposing potentially misleading transformations to the data. Nevertheless, non-transformed wastewater concentrations are only suitable for aggregation when reporting units are consistent across WWTPs. Standardizing wastewater monitoring protocols across WWTPs and jurisdictions would allow for easier spatial aggregation of wastewater monitoring data—and improved data reliability [42]. However, laboratory standardization may be challenging in practice, as each laboratory will likely have different resources, equipment, and assay validation procedures. Even with standardized laboratory procedures across WWTPs, wastewater concentrations may still vary between sites due to environmental matrix effects. Although the difference was not substantial, using PMMoV-normalized versus unnormalized wastewater concentrations of SARS-CoV-2 RNA generally improved the correlation between wastewater metrics and COVID-19 test positivity. This finding aligns conceptually with a mass balance model relating PMMoV-normalized SARS-CoV-2 RNA concentrations in wastewater solids to the population fraction shedding SARS-CoV-2 RNA in stool [33]. Other studies that evaluated PMMoV normalization—all of which quantified viral RNA in the liquid fraction of wastewater—concluded that PMMoV normalization only sometimes improves correlation strength with clinical disease metrics [11,43–47]. Presently, some wastewater monitoring programs do not measure PMMoV RNA concentrations and instead normalize wastewater concentrations by flow rate and population (“flowpop” normalization). Prior to August 2025, the CDC’s WVAL methodology preferentially used flowpop-normalized concentrations over PMMoV-normalized concentrations when both normalization approaches were available [35]. The methodology has since been updated to use only unnormalized concentrations [22]. The multi-laboratory program dataset includes some WWTPs with flow rate measurements, so we conducted a supplementary analysis using wastewater metrics calculated using flowpop-normalized values ( Fig. S11 , Table S7 ). Only 29 WWTPs from the multi-laboratory program reported flow rate measurements for this supplementary analysis (compared to 78 WWTPs reporting PMMoV RNA concentrations for the main analysis), but Kendall’s tau estimates calculated using flowpop-normalized wastewater values were similar to estimates calculated using PMMoV-normalized wastewater values. Regardless, a standard normalization approach is still needed to account for dilution effects, such as from rainfall or other changes in flow rate, and to improve comparisons across WWTPs. At the California state spatial scale, we did not observe differences in correlations between wastewater aggregates and test positivity using wastewater concentrations from the single laboratory versus multi-laboratory monitoring programs. Future studies could assess the correlations tested herein at finer spatial scales, such as California Regional Public Health Office regions [48] and California counties, although the aggregation approaches appear to be robust to differences in wastewater monitoring protocols at the state spatial scale. This finding aligns with previous work examining inter-laboratory variability. Chik et al. [49] provided wastewater samples spiked with SARS-CoV-2 surrogates to eight laboratories in Canada. Concentrations measured using reverse transcription quantitative polymerase chain reaction (RT-qPCR) were consistently on the same order of magnitude across laboratories [49]. Moreover, Pecson et al. [50] evaluated the sensitivity and reproducibility of 36 quantification methods, including both RT-qPCR and RT-dPCR platforms, and observed overall reproducible results with slight differences across methodological variations. Thus, spatial aggregation of wastewater concentrations is still feasible, even when concentrations are generated using different protocols, so long as concentrations are reported using the same units. Herein, we only evaluated spatial aggregation methods using correlations with test positivity. However, test positivity data do not necessarily represent a gold standard metric for disease occurrence. Testing data are biased towards those with symptomatic disease and access to healthcare, and test positivity may become further biased by changes in testing volume over time and across different geographic regions. Other use cases for wastewater monitoring data include characterizing trends and relative levels in disease activity [8,20]. To report trends and relative levels at jurisdictional scales, Chan and Boehm [20] demonstrate how trends and levels may be calculated at the WWTP sewershed scale and then spatially aggregated based on frequency (i.e., the most frequent level or trend among all WWTPs in a state would be the level or trend representing that state). Population weighting could further be incorporated into this approach if desired by weighting counts of levels or trends by population prior to determining the most frequent level or trend among all geographic units in the aggregate area. This study used data from a multi-laboratory sampling program in California to assess the performance of spatial aggregation methods across different laboratories and methods. However, only laboratories sampling within this program that provided detailed laboratory method documentation to the author team were included. The exclusion of multiple laboratories that did not provide method documentation limited our ability to assess spatial aggregation across many laboratories. Future studies assessing all available laboratory data in California may provide further insight into the ideal spatial aggregation method for a complex, multi-laboratory program. In order for these studies to be completed, wastewater monitoring data should be accompanied by complete methodological protocols that follow minimum reporting guidelines [51,52] We only conducted this analysis using SARS-CoV-2 wastewater monitoring data and COVID-19 case surveillance data. These findings may be generalizable to other commonly detected pathogens that are at least seasonally abundant in wastewater, but further studies should be done to repeat this analysis for those pathogens. Meanwhile, a limitation of all the spatial aggregation approaches is that some sewersheds may intersect multiple jurisdictions (namely counties). Herein, we associated a sewershed with the jurisdiction its WWTP is physically located in for spatial aggregation purposes. Consequently, if a sewershed intersects a jurisdiction—but the WWTP of the sewershed is not physically present in or confined to that jurisdiction—the jurisdiction will not be represented in aggregate wastewater metrics. More sophisticated spatial aggregation approaches could account for the proportion of each jurisdiction that is serviced by a sewershed using geoprocessing tools. Another limitation associated with any spatial aggregation is that differences within the aggregate area may become masked. Zhu et al. [17] showed that geographic units with high wastewater viral load were masked in the spatially aggregated area which may negatively impact risk perception and public health response. Although spatial data aggregation is useful for data communication and interpretation at jurisdictional scales, paying attention to pathogen occurrence patterns at small spatial scales (i.e., WWTP sewersheds) is still important to recognize localized patterns of disease activity and mobilize appropriate public health response [8]. Abbreviations CDPH California Department of Public Health HHS Health and Human Services NREVSS National Respiratory and Enteric Virus Surveillance System Declarations Supporting Material SpatialAggregationMethods_SI.pdf Funding statement This work was funded in part by a gift from the Sergey Brin Family Foundation to ABB. This study was supported in part by the CDC Epidemiology and Laboratory Capacity for Infectious Diseases Cooperative Agreement (Grant number 6NU50CK000539-04-04). The funders played no role in study design, data collection, analysis and interpretation of data, or the writing of this manuscript. Ethics statement This study was reviewed by the Stanford University Human & Animal Research Compliance Office and determined not to involve human subjects. The study is exempt from oversight. Clinical trial number Not applicable. Consent to publish declaration Not applicable. Consent to participate declaration Not applicable. Author contribution declaration EMGC: Conceptualization, Methodology, Software, Validation, Formal Analysis, Investigation, Writing - Original Draft, Writing - Review & Editing, Visualization, Project Administration EB: Conceptualization, Methodology, Investigation, Resources, Data Curation, Writing - Original Draft, Writing - Review & Editing AZ: Conceptualization, Methodology, Writing - Review & Editing CL: Methodology, Validation, Investigation, Data Curation, Writing – Review & Editing ATY: Conceptualization, Writing - Review & Editing, Supervision, Project Administration, Funding Acquisition ABB: Conceptualization, Validation, Resources, Data Curation, Writing - Review & Editing, Supervision, Project Administration, Funding Acquisition Competing interests statement The authors have no competing interests to disclose. Data availability statement Data and R code used for this study are publicly available through the Stanford Digital Repository: https://purl.stanford.edu/tg451qc5869. Acknowledgements We thank the participating wastewater treatment plant staff for collecting samples for this project and CDPH Drinking Water and Radiation Lab (DWRL) wastewater team for processing and testing samples. References Paul JR, Trask JD, Gard S. II. Poliomyelitic Virus in Urban Sewage. J Exp Med. 1940 Jun 1;71(6):765–77. Paul JR, Trask JD. The Virus of Poliomyelitis in Stools and Sewage. J Am Med Assoc. 1941 Feb 8;116(6):493–8. Trask JD, Paul JR, Technical Assistance of John T. Riordan. Periodic Examination of Sewage for the Virus of Poliomyelitis. J Exp Med. 1942 Jan 1;75(1):1–6. 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1","display":"","copyAsset":false,"role":"figure","size":221395,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverview of wastewater monitoring data spatial aggregation methods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTwelve unique spatial aggregation methods were applied to wastewater monitoring data. Methods differed with respect to normalization, transformation, and spatial aggregation approaches; approaches for non-detect handling, temporal smoothing, and temporal aggregation were consistent across methods. Only methods involving PMMoV-normalized wastewater SARS-CoV-2 RNA concentrations (n = 6) were applied to data from the California, multi-laboratory monitoring program. Methods involving both unnormalized and PMMoV-normalized wastewater SARS-CoV-2 RNA concentrations (n = 12) were applied to data from the national, single laboratory monitoring program. Spatially aggregated wastewater SARS-CoV-2 RNA metrics were then correlated with COVID-19 test positivity at corresponding spatial scales. Abbreviations: g/g = gene copies per gram, PMMoV = pepper mild mottle virus.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8205614/v1/2efa149e91608f33097f6120.png"},{"id":97370300,"identity":"e0ae3877-6a18-459f-8e39-e850611b5203","added_by":"auto","created_at":"2025-12-03 16:27:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":197822,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMap of SARS-CoV-2 wastewater monitoring programs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(a) National, single laboratory monitoring program. WWTPs are represented by black circles; states are colored by HHS region. (b) California, multi-laboratory monitoring program. Counties are colored by the number of participating wastewater treatment plants; gray counties did not have any participating WWTPs. Abbreviations: HHS = Health and Human Services, WWTP = wastewater treatment plant. Created in ArcGIS Pro (version 3.1.1) using state and county cartographic boundaries from the US Census Bureau [28].\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8205614/v1/ca00b06b6796b59625c251e6.png"},{"id":97315187,"identity":"7e7ce851-adb9-4cb5-9e60-e373396e43f9","added_by":"auto","created_at":"2025-12-03 06:33:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":45202,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpatial aggregation steps for wastewater monitoring data from each program\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor the national, single laboratory monitoring program, weekly wastewater metrics were spatially aggregated across WWTPs to the state scale and then across states to the national or HHS region scale. For the California, multi-laboratory monitoring program, weekly wastewater metrics were spatially aggregated across WWTPs to the county scale and then across counties to the California scale. The number in parentheses refers to the number of unique geographic units reporting wastewater concentrations within each spatial scale. Note that the District of Columbia is included as a unique state for the national, single laboratory monitoring program. Abbreviations: HHS = Health and Human Services, WWTP = wastewater treatment plant.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8205614/v1/31a80ea5589ad92f0dc87c95.png"},{"id":97315191,"identity":"a8c942e3-198d-4deb-94ba-9fa50a90cb2b","added_by":"auto","created_at":"2025-12-03 06:33:30","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":149408,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWeekly temporal correlation between COVID-19 test positivity and wastewater SARS-CoV-2 metric, California spatial scale\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(a) California COVID-19 test volume (left y-axis; gray bars) and test positivity (right y-axis; red line) from CDPH. (b) State aggregated wastewater SARS-CoV-2 RNA concentrations (left y-axis: normalized by PMMoV; right y-axis: unnormalized), and (c) state aggregated wastewater viral activity levels (calculated using both PMMoV-normalized and unnormalized concentrations) using wastewater monitoring data from the single laboratory program. (d) State aggregated wastewater SARS-CoV-2 RNA concentrations (PMMoV-normalized concentrations only), and (e) state aggregated wastewater viral activity levels (calculated using PMMoV-normalized concentrations only) using wastewater monitoring data from the multi-laboratory program. (f) Kendall’s tau correlation between COVID-19 test positivity and each wastewater metric in b–c (all p \u0026lt; 0.0001); error bars represent 95% confidence interval of the tau estimate. (g) Kendall’s tau correlation between COVID-19 test positivity and each wastewater metric in d–e (all p \u0026lt; 0.0001); error bars represent 95% confidence interval of the tau estimate. Solid lines represent wastewater metrics determined using PMMoV-normalized concentrations; dashed lines represent wastewater metrics determined using unnormalized concentrations. Lines and bars are colored by spatial aggregation method (purple: median, pink: unweighted average, orange: population-weighted average). Abbreviations: CDPH = California Department of Public Health, gc/g = gene copies per gram, PMMoV = pepper mild mottle virus.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8205614/v1/3991423c79c8405e8007ecdc.png"},{"id":97369488,"identity":"477c8e22-138b-473d-a97f-6bd705114ced","added_by":"auto","created_at":"2025-12-03 16:25:01","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":91440,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWeekly temporal correlation between COVID-19 test positivity and wastewater SARS-CoV-2 metric, HHS region spatial scale\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKendall’s tau correlation between COVID-19 test positivity from NREVSS and each wastewater metric from the single laboratory monitoring program (all p \u0026lt; 0.0001); error bars represent 95% confidence interval of the tau estimate. Solid lines represent wastewater metrics determined using PMMoV-normalized concentrations; dashed lines represent wastewater metrics determined using unnormalized concentrations. Bars are colored by spatial aggregation method (purple: median, pink: unweighted average, orange: population-weighted average). Abbreviations: HHS = Health and Human Services, NREVSS = National Respiratory and Enteric Virus Surveillance System, PMMoV = pepper mild mottle virus.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8205614/v1/21aa1fcd8792b8771a74be80.png"},{"id":97315193,"identity":"7c24ab9d-8e7f-45c4-9d0e-a9928b401f8b","added_by":"auto","created_at":"2025-12-03 06:33:30","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":104370,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWeekly temporal correlation between COVID-19 test positivity and wastewater SARS-CoV-2 metric, USA spatial scale\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(a) National COVID-19 test volume (left y-axis; gray bars) and test positivity (right y-axis; red line) from NREVSS. (b) Nationally aggregated wastewater SARS-CoV-2 RNA concentrations (left y-axis: normalized by PMMoV; right y-axis: unnormalized), and (c) nationally aggregated wastewater viral activity levels (calculated using PMMoV-normalized and unnormalized concentrations) using wastewater monitoring data from the single laboratory program. (d) Kendall’s tau correlation between COVID-19 test positivity and each wastewater metric (all p \u0026lt; 0.0001); error bars represent 95% confidence interval of the tau estimate. Solid lines represent wastewater metrics determined using PMMoV-normalized concentrations; dashed lines represent wastewater metrics determined using unnormalized concentrations. Lines and bars are colored by spatial aggregation method (purple: median, pink: unweighted average, orange: population-weighted average). Abbreviations: gc/g = gene copies per gram, NREVSS = National Respiratory and Enteric Virus Surveillance System, PMMoV = pepper mild mottle virus.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8205614/v1/4c94dbaada32d1dbacaa563c.png"},{"id":103395903,"identity":"55f6aa82-d308-4abd-9fa0-fbfdcd728b54","added_by":"auto","created_at":"2026-02-25 08:43:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1657734,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8205614/v1/ce8c0ea4-2bdc-463a-91c7-d8b755254d7b.pdf"},{"id":97315219,"identity":"69f88ff7-abe4-42fb-83b1-8785a1699a47","added_by":"auto","created_at":"2025-12-03 06:33:30","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":6109109,"visible":true,"origin":"","legend":"","description":"","filename":"SpatialAggregationMethodsSI.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8205614/v1/865d9f82b5c03bb3f844ac17.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Spatial aggregation methods for interpreting wastewater concentrations at jurisdictional scales: Insights from two SARS-CoV-2 monitoring programs","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eFirst used in the 1940s to monitor poliovirus [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], wastewater monitoring is an approach to infectious disease surveillance that gained traction during the Coronavirus Disease 2019 (COVID-19) pandemic to monitor SARS-CoV-2, the causative virus of COVID-19 disease. Unlike case surveillance, which is subject to reporting delays and testing biases, wastewater concentrations of infectious disease biomarkers can be available within 24 hours of sample collection and represent an entire contributing population\u0026mdash;regardless of individuals\u0026rsquo; symptom presentation or access to healthcare. However, wastewater concentrations of pathogen quantity may vary between wastewater catchment areas (sewersheds), even if the viral contributions to a wastewater stream are equal. This variability may be due to sewershed factors, such as population size and non-human inputs (e.g., rainfall, industrial runoff), and differences in laboratory processing methods [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Due to these variations, it is not always clear if measurements made for different sewersheds are directly comparable to each other or if they can be aggregated across multiple sewersheds. Despite these challenges, there is a need to compare wastewater concentrations between wastewater treatment plants (WWTPs) and laboratory methods and to spatially aggregate wastewater concentrations across multiple WWTPs and wastewater monitoring programs to improve the value and interpretability of wastewater monitoring data.\u003c/p\u003e\u003cp\u003eSpatial aggregation aims to combine data collected over more than one geographic unit (e.g., WWTP sewersheds) to a single spatial scale (e.g., state). Measured wastewater concentrations represent individual WWTP sewersheds, which often do not align with municipal boundaries (e.g., one large city may have multiple sewersheds within its city limits). Additionally, public health practitioners must understand what these data mean for broader spatial scales, such as the local county, state, regional, or national scales. Public health agencies and local health jurisdictions enact public health action, response, and policymaking on these broader spatial scales. Accurate summaries of wastewater monitoring data across multiple WWTPs are needed to effectively use the data to inform public health actions, both at local levels and at state and national levels [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eComparisons between WWTPs and spatial aggregation are complicated by differences in population and sewer network characteristics, sampling and analytical methods, and data processing approaches [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan additionalcitationids=\"CR8 CR9 CR10 CR11 CR12 CR13 CR14\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Several studies have assessed measurement variability arising from various site and methodological differences [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Studies investigating variability from data processing have focused on different approaches for handling non-detect concentrations [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], normalizing concentrations [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], temporally smoothing concentrations [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], and transforming concentrations [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. There has been limited work focused on aggregation approaches as an important data processing step.\u003c/p\u003e\u003cp\u003eTwo different spatial aggregation methods are currently commonly used for wastewater concentrations: (1) population-weighted averages of non-transformed wastewater concentrations and (2) medians of wastewater concentrations that are transformed using the US Centers for Disease Control\u0026rsquo;s (CDC) National Wastewater Surveillance System (NWSS) team\u0026rsquo;s wastewater viral activity level method (WVAL). For a population-weighted average, wastewater concentrations are averaged across geographic units in an aggregate area, weighted by the population of individual geographic units (e.g., the service population of WWTP sewersheds in a state) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan additionalcitationids=\"CR17 CR18 CR19 CR20\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Population weighting aims to adjust for differences in population size among the individual geographic units. Alternatively, the WVAL method involves first transforming wastewater concentration for individual sewersheds to a comparable scale and then spatially aggregating by calculating the median WVAL across geographic units [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. To our knowledge, there have not been studies that systematically compare these spatial data aggregation approaches for interpreting wastewater monitoring data.\u003c/p\u003e\u003cp\u003eAn ideal spatial aggregation method for wastewater concentrations should correlate with case surveillance metrics of disease occurrence at the same spatial scales; work with an amalgamation of wastewater matrices, laboratory methods, reporting units, and normalization methods; and be simple to implement and interpret. Here, we spatially aggregated wastewater concentrations using multiple methods and evaluated how well spatially aggregated wastewater metrics correlated with test-positivity (a case surveillance metric) on the same spatial scale. We used wastewater concentrations collected using different sampling and analytical methods and with different data processing treatment to reflect the lack of standardization across wastewater monitoring programs. Previous work evaluating variability arising from other data processing steps focused on SARS-CoV-2 as a case study [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan additionalcitationids=\"CR12 CR13 CR14\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], so we also chose to focus on SARS-CoV-2 RNA wastewater concentrations for this analysis given the availability of longitudinal wastewater SARS-CoV-2 RNA datasets and publicly available COVID-19 case surveillance data.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cp\u003eWe applied 12 spatial aggregation methods to wastewater SARS-CoV-2 RNA monitoring data and evaluated each method using comparisons to clinical COVID-19 test positivity (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Data sources and data processing steps are described in detail below but, briefly, the methods differed in their normalization, transformation, and spatial aggregation approaches. The approaches to handle non-detect concentrations (i.e., wastewater concentrations below a laboratory method\u0026rsquo;s lower detection limit) and temporally smooth concentrations remained consistent across methods. Because we evaluated correlations between spatially aggregated wastewater metrics and test positivity on a weekly basis, we averaged wastewater metrics across all samples collected each week for each method prior to spatial aggregation. We considered several spatial scales to evaluate the various spatial aggregation methods, depending on the spatial coverage of the data sources described next. All calculations and analyses were conducted in R (version 4.5.0).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTwelve unique spatial aggregation methods were applied to wastewater monitoring data. Methods differed with respect to normalization, transformation, and spatial aggregation approaches; approaches for non-detect handling, temporal smoothing, and temporal aggregation were consistent across methods. Only methods involving PMMoV-normalized wastewater SARS-CoV-2 RNA concentrations (n\u0026thinsp;=\u0026thinsp;6) were applied to data from the California, multi-laboratory monitoring program. Methods involving both unnormalized and PMMoV-normalized wastewater SARS-CoV-2 RNA concentrations (n\u0026thinsp;=\u0026thinsp;12) were applied to data from the national, single laboratory monitoring program. Spatially aggregated wastewater SARS-CoV-2 RNA metrics were then correlated with COVID-19 test positivity at corresponding spatial scales. Abbreviations: g/g\u0026thinsp;=\u0026thinsp;gene copies per gram, PMMoV\u0026thinsp;=\u0026thinsp;pepper mild mottle virus.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 SARS-CoV-2 wastewater monitoring\u003c/h2\u003e\u003cp\u003eWe obtained wastewater concentrations of SARS-CoV-2 RNA from two distinct wastewater monitoring programs. For this analysis, we used concentrations between the first epidemiological week (i.e., Sunday\u0026ndash;Saturday) of 2023 through the last epidemiological week of 2024 (1 January 2023 to 28 December 2024) from each program. All data used for this study are available through the Stanford Digital Repository (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://purl.stanford.edu/tg451qc5869\u003c/span\u003e\u003cspan address=\"https://purl.stanford.edu/tg451qc5869\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe first program is a national wastewater monitoring program for the USA in which all wastewater concentrations are contributed by a single laboratory (hereafter referred to as the single laboratory program) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. During the analysis period, 188 WWTPs across 40 states and the District of Columbia had routine SARS-CoV-2 RNA monitoring (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea,\u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). Wastewater samples were collected from WWTPs three times per week on average, and SARS-CoV-2 RNA and pepper mild mottle virus (PMMoV) RNA were measured in the wastewater solids of all samples in gene copies per gram (gc/g) using droplet digital reverse transcription polymerase chain reaction (ddRT-PCR) following environmental molecular biology best practices. PMMoV is a highly abundant virus in wastewater, originating in the human diet, and serves to correct for differences in viral recovery and the human fecal strength of wastewater [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. A total of 50,708 wastewater concentrations of SARS-CoV-2 RNA and PMMoV RNA were reported during the analysis period. Detailed methods and data are available in a data descriptor by Boehm et al. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] and through the Stanford Digital Repository (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://purl.stanford.edu/hj801ns5929\u003c/span\u003e\u003cspan address=\"https://purl.stanford.edu/hj801ns5929\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe second program is a California statewide wastewater monitoring program in which wastewater concentrations are contributed by multiple laboratories (hereafter referred to as the multi-laboratory program) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. For this analysis, we used data contributed by two distinct laboratory methods. Although the program includes data from additional laboratories, we only used concentrations for which complete methodological details were documented and available to the author team. For the subset of data with complete methods available, 78 WWTPs across 40 counties had routine SARS-CoV-2 and PMMoV RNA monitoring during the analysis period (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb, \u003cb\u003eTable S2\u003c/b\u003e). Some of these WWTPs were also represented in the single laboratory program described above. Wastewater samples were collected from WWTPs three times per week on average, and SARS-CoV-2 RNA and PMMoV RNA were measured by at least one of the two laboratories. The first laboratory is the same laboratory used by the national program and uses the methods described above, reporting concentrations in gc/g of wastewater solids. The second laboratory also uses ddRT-PCR to quantify viral RNA, although with different concentration and extraction methods than the first laboratory. Namely, concentrations are measured in the liquid fraction of wastewater and reported in gene copies per liter (gc/L). See the Supporting Information (SI) and \u003cb\u003eTable S3\u003c/b\u003e for complete methodological details for the second laboratory. We excluded concentrations with reported quality control issues (e.g., samples not stored at correct temperature). Refer to the SI for complete quality control procedures. A total of 18,224 and 6,801 wastewater concentrations of SARS-CoV-2 RNA and PMMoV RNA were reported by the first and second laboratory, respectively, during the analysis period. Data are available through the California Health and Human Services Open Data Portal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://data.chhs.ca.gov/dataset/wastewater-surveillance-data-california\u003c/span\u003e\u003cspan address=\"https://data.chhs.ca.gov/dataset/wastewater-surveillance-data-california\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e(a) National, single laboratory monitoring program. WWTPs are represented by black circles; states are colored by HHS region. (b) California, multi-laboratory monitoring program. Counties are colored by the number of participating wastewater treatment plants; gray counties did not have any participating WWTPs. Abbreviations: HHS\u0026thinsp;=\u0026thinsp;Health and Human Services, WWTP\u0026thinsp;=\u0026thinsp;wastewater treatment plant. Created in ArcGIS Pro (version 3.1.1) using state and county cartographic boundaries from the US Census Bureau [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 COVID-19 case surveillance\u003c/h2\u003e\u003cp\u003eWe obtained COVID-19 case surveillance data from two sources. To match the time period of the SARS-CoV-2 RNA wastewater monitoring data, we used data between 1 January 2023 and 28 December 2024 from each source.\u003c/p\u003e\u003cp\u003eThe National Respiratory and Enteric Virus Surveillance System (NREVSS) is a laboratory-based sentinel surveillance system whereby participating laboratories voluntarily report to the CDC the number of nucleic acid amplification tests administered and the number of those tests that were positive for SARS-CoV-2 each epidemiological week (defined as Sunday to Saturday); results from antigen, antibody, and at-home tests are excluded [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. We obtained weekly clinical COVID-19 test positivity for the USA and each Health and Human Services (HHS) region for this analysis (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://data.cdc.gov/Laboratory-Surveillance/Percent-Positivity-of-COVID-19-Nucleic-Acid-Amplif/gvsb-yw6g/about_data\u003c/span\u003e\u003cspan address=\"https://data.cdc.gov/Laboratory-Surveillance/Percent-Positivity-of-COVID-19-Nucleic-Acid-Amplif/gvsb-yw6g/about_data\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). NREVSS does not report test positivity for individual states [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. For some weeks, multiple test positivity values were posted for a given spatial scale. We used the most recently posted test positivity value each week for this analysis.\u003c/p\u003e\u003cp\u003eThe California Department of Public Health (CDPH) reported total tests and positive tests for COVID-19 each day on its respiratory virus dashboard (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://data.chhs.ca.gov/dataset/respiratory-virus-dashboard-metrics\u003c/span\u003e\u003cspan address=\"https://data.chhs.ca.gov/dataset/respiratory-virus-dashboard-metrics\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. At the time of this analysis, these data, which are received by CDPH through electronic laboratory reporting of COVID-19 test results among residents of California, were only reported publicly at the state scale [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. To match the reporting frequency of NREVSS and because testing data are prone to weekend bias [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], we determined weekly clinical COVID-19 test positivity for California by dividing the total positive tests by the total tests administered each epidemiological week (defined as Sunday to Saturday) to use for this analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Wastewater data processing\u003c/h2\u003e\u003cp\u003ePrior to spatially aggregating wastewater concentrations, we considered different data processing approaches with respect to data normalization and transformation. We used the same data processing approach to handle non-detects, temporally smooth, and temporally aggregate wastewater concentrations for all spatial aggregation methods (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Details about each data processing step preceding spatial aggregation are described below.\u003c/p\u003e\u003cp\u003e\u003cb\u003eNon-detect concentrations\u003c/b\u003e. First, we used single imputation to handle non-detect concentrations, which comprised 1.6% (n\u0026thinsp;=\u0026thinsp;799) of concentrations from the single laboratory program and 0.53% (n\u0026thinsp;=\u0026thinsp;131) of concentrations from the multi-laboratory program. Handling non-detect concentrations aims to reduce bias in subsequent inferences. Although single imputation can amplify inference bias when the wastewater target is present in low concentrations with a high proportion of non-detects [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], SARS-CoV-2 was nearly always detected in the wastewater monitoring data during the study period used for this analysis. Any bias arising from the use of single imputation was likely negligible. Specifically, we set any non-detect concentrations to half the assay limit detection. The assay limit of detection was approximately 1,000 gc/g for the single laboratory program and varied for the multi-laboratory program (500 gc/g or 1,100 gc/g for solids concentrations and 1,000 gc/L for liquids concentrations) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eNormalization\u003c/b\u003e. Second, we normalized SARS-CoV-2 RNA wastewater concentrations by PMMoV RNA wastewater concentrations. As described previously, PMMoV is an indigenous wastewater virus of dietary origin. PMMoV-normalization aims to correct for differences in viral recovery and fluctuations in the human fecal strength of wastewater [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Process-based modeling also conceptually shows that PMMoV-normalized SARS-CoV-2 RNA wastewater concentrations should scale with disease incidence rate [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. We considered concentrations from the single laboratory program both with and without normalization for reference, given that all concentrations were reported using consistent units (gc/g). We only considered concentrations from the multi-laboratory program with normalization, given that reported concentrations did not have consistent units across laboratories. For the multi-laboratory program, a subset of sewersheds were monitored by multiple laboratories during part or all of the study period, resulting in days where multiple SARS-CoV-2 RNA and PMMoV RNA concentrations were reported for a single WWTP. We calculated the arithmetic mean of all reported PMMoV-normalized SARS-CoV-2 RNA concentrations to obtain a single PMMoV-normalized SARS-CoV-2 RNA concentration per WWTP each day.\u003c/p\u003e\u003cp\u003e\u003cb\u003eTemporal smoothing\u003c/b\u003e. Third, we temporally smoothed wastewater concentrations for each WWTP using a simple moving average with truncation. Smoothing aims to reduce the effects of outlier concentrations. Specifically, we calculated the five-sample, centered, trimmed, moving average of wastewater concentrations for each WWTP as done previously [\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Trimming refers to the removal of the highest and lowest value in the moving average window prior to calculating the average. To calculate moving average values at the start and end of a time series, we used a shrinking window (e.g., a three-sample window was used to calculate the moving average value for the first sample at a WWTP).\u003c/p\u003e\u003cp\u003e\u003cb\u003eTransformation\u003c/b\u003e. Fourth, we considered two types of wastewater monitoring metrics for data analysis: (1) wastewater concentrations and (2) WVAL values. Wastewater concentrations (either unnormalized or normalized by PMMoV) refer to concentration values without any further data transformation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). WVAL values refer to wastewater concentrations that are transformed to represent the number of standard deviations above a prespecified baseline; they are reported on a linear scale [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Transformation aims to allow for comparison of concentrations across WWTPs and laboratories\u0026mdash;regardless of sampling, analytical, or normalization approaches. The WVAL metric was developed by the CDC\u0026rsquo;s NWSS program, and we calculated the WVAL metric using unnormalized, smoothed wastewater concentrations and PMMoV-normalized, smoothed wastewater concentrations (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). See the SI for further details about the WVAL calculation steps as of March 2025 [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eTemporal aggregation\u003c/b\u003e. Lastly, we temporally aggregated wastewater monitoring metrics on a weekly basis (Sunday\u0026ndash;Saturday) at each WWTP for data analysis. To do so, we averaged wastewater metric values (either wastewater concentration or WVAL values) across all wastewater samples collected each week at each WWTP, similar to a method used by the CDC [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Spatial aggregation of wastewater data\u003c/h2\u003e\u003cp\u003eTo spatially aggregate weekly wastewater metrics (either concentrations or WVAL values) across multiple geographic units (e.g., WWTP sewersheds, counties, states), we considered three spatial aggregation approaches: (1) median, (2) unweighted average, and (3) population-weighted average. Median refers to calculating the median of the metric across geographic units (Eq.\u0026nbsp;1); unweighted average refers to calculating the arithmetic mean of the metric across geographic units (Eq.\u0026nbsp;2); and population-weighted average refers to calculating the weighted arithmetic mean of the metric across geographic units where the weights represent the population of each geographic unit (Eq.\u0026nbsp;3).\u003c/p\u003e\u003cp\u003e\u003cb\u003eEquation 1.\u003c/b\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Median\\left(X\\right)=X\\left[\\frac{n+1}{2}\\right]if\\:n\\:is\\:odd;\\:\\frac{X\\left[\\frac{n}{2}\\right]+X[\\frac{n}{2}+1]}{2}if\\:n\\:is\\:even\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003cp\u003eX: ordered list of wastewater metrics across all geographic units in the spatial aggregation area\u003c/p\u003e\u003cp\u003en: number of geographic units in the spatial aggregation area\u003c/p\u003e\u003cp\u003e\u003cb\u003eEquation 2.\u003c/b\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Unweighted\\:average=\\frac{\\sum\\:_{i=1}^{n}{x}_{i}}{n}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003cp\u003ex\u003csub\u003ei\u003c/sub\u003e: wastewater metric value of the i\u003csup\u003eth\u003c/sup\u003e geographic unit in the spatial aggregation area\u003c/p\u003e\u003cp\u003en: number of geographic units in the spatial aggregation area\u003c/p\u003e\u003cp\u003e\u003cb\u003eEquation 3.\u003c/b\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Population\\:weighted\\:average=\\frac{\\sum\\:_{i=1}^{n}{p}_{i}{x}_{i}}{\\sum\\:_{i=1}^{n}{p}_{i}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003cp\u003ep\u003csub\u003ei\u003c/sub\u003e: population of the i\u003csup\u003eth\u003c/sup\u003e geographic unit in the spatial aggregation area\u003c/p\u003e\u003cp\u003ex\u003csub\u003ei\u003c/sub\u003e: wastewater metric value of the i\u003csup\u003eth\u003c/sup\u003e geographic unit in the spatial aggregation area\u003c/p\u003e\u003cp\u003en: number of geographic units in the spatial aggregation area\u003c/p\u003e\u003cp\u003eGiven we only considered both normalized and unnormalized wastewater concentrations for the single laboratory program, we evaluated 12 unique spatial aggregation methods for the single laboratory program dataset and 6 unique spatial aggregation methods for the multi-laboratory program dataset (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). For the single laboratory program, we spatially aggregated weekly wastewater metrics across WWTPs to the state scale and then across states to the national and HHS region scales (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). For the multi-laboratory program, we spatially aggregated weekly wastewater metrics across WWTPs to the county scale and then across counties to the California scale (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). For data analysis, we only used aggregated wastewater metrics for California, each HHS region, and the USA spatial scales (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Wastewater metrics spatially aggregated for other states or counties were intermediary and not used for the data analysis described next. \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e and \u003cb\u003eTable S2\u003c/b\u003e list the sewershed population estimate of each WWTP in the single laboratory and multi-laboratory programs, respectively, and its associated state or county. We obtained county and state population estimates from the US Census Bureau [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea displays the HHS region of each state.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFor the national, single laboratory monitoring program, weekly wastewater metrics were spatially aggregated across WWTPs to the state scale and then across states to the national or HHS region scale. For the California, multi-laboratory monitoring program, weekly wastewater metrics were spatially aggregated across WWTPs to the county scale and then across counties to the California scale. The number in parentheses refers to the number of unique geographic units reporting wastewater concentrations within each spatial scale. Note that the District of Columbia is included as a unique state for the national, single laboratory monitoring program. Abbreviations: HHS\u0026thinsp;=\u0026thinsp;Health and Human Services, WWTP\u0026thinsp;=\u0026thinsp;wastewater treatment plant.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Data analysis\u003c/h2\u003e\u003cp\u003eWe assessed the correlation between weekly COVID-19 test positivity and weekly SARS-CoV-2 wastewater metrics for each wastewater monitoring program dataset at various spatial scales. Time series data were not always normally distributed (Shapiro-Wilk test, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), so we used Kendall\u0026rsquo;s tau correlation test to assess the null hypothesis that weekly COVID-19 test positivity is not temporally correlated with weekly SARS-CoV-2 wastewater metric.\u003c/p\u003e\u003cp\u003eUsing wastewater monitoring data from the single laboratory program, we evaluated the correlation at the California, HHS region, and USA spatial scales (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). For each spatial scale, we tested the correlation 12 times (once for each possible aggregation method). Using wastewater monitoring data from the multi-laboratory program, we evaluated the correlation at the California spatial scale only (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). We tested the correlation 6 times (once for each possible aggregation method).\u003c/p\u003e\u003cp\u003eTo account for multiple testing at each spatial scale, we used an adjusted significance level of 0.05 divided by the total number of correlation tests conducted at the spatial scale (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). At the USA and HHS region spatial scales, we conducted 12 correlation tests each (adjusted significance level: 0.05 / 12\u0026thinsp;=\u0026thinsp;0.004). At the California spatial scale, we conducted 18 total correlation tests (adjusted significance level: 0.05 / 18\u0026thinsp;=\u0026thinsp;0.003). We rejected the null hypothesis if the p value associated with Kendall\u0026rsquo;s tau estimate was less than the adjusted significance level. We used the cor.test function from the stats library [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] to conduct Kendall\u0026rsquo;s tau correlation tests and the KendallTauB function from the DescTools library [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] to determine the 95% confidence interval of Kendall\u0026rsquo;s tau estimates.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eKendall's Tau Correlation Tests Between Weekly Test Positivity and Weekly Wastewater Metric\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpatial Scale\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eData Source: COVID-19 Test Positivity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eData Source: SARS-CoV-2 Wastewater Monitoring\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNumber of Aggregation Approaches\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAdjusted Significance Level\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUSA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNREVSS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSingle laboratory program\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.05 / 12\u0026thinsp;=\u0026thinsp;0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHHS Regions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNREVSS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSingle laboratory program\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.05 / 12\u0026thinsp;=\u0026thinsp;0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCalifornia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCDPH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSingle laboratory program\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.05 / 18\u0026thinsp;=\u0026thinsp;0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCalifornia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCDPH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMulti-laboratory program\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.05 / 18\u0026thinsp;=\u0026thinsp;0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e The number of spatial aggregation approaches represents the number of Kendall\u0026rsquo;s tau correlation tests conducted.\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Results","content":"\u003cp\u003eWeekly COVID-19 test positivity and wastewater SARS-CoV-2 metrics using each spatial aggregation method are shown in \u003cstrong\u003eFig. 4a\u0026ndash;e\u003c/strong\u003e for the California spatial scale, \u003cstrong\u003eFig. S1\u0026ndash;10\u003c/strong\u003e for each HHS region, and \u003cstrong\u003eFig. 6a\u0026ndash;c\u003c/strong\u003e for the USA spatial scale. Test positivity and wastewater metrics were positively, temporally, and significantly correlated for all aggregation approaches at all spatial scales considered (median tau: 0.56; range: 0.39\u0026ndash;0.72; p \u0026lt; 0.0001) (\u003cstrong\u003eTable S4\u0026ndash;6\u003c/strong\u003e). Kendall\u0026rsquo;s tau correlation coefficients with 95% confidence interval error bars are shown in \u003cstrong\u003eFig. 4f\u0026ndash;g\u003c/strong\u003e for the California spatial scale, \u003cstrong\u003eFig. 5\u0026nbsp;\u003c/strong\u003efor each HHS region, and \u003cstrong\u003eFig. 6d\u0026nbsp;\u003c/strong\u003efor the USA spatial scale. Overlapping 95% confidence intervals do not always indicate statistically insignificant differences [40], but herein we interpret overlapping 95% confidence intervals as suggesting differences are not substantial.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Spatial aggregation methods using a PMMoV-normalized wastewater value generally resulted in a stronger correlation than the counterpart aggregation method using an unnormalized wastewater value, but differences were not substantial as suggested by overlapping 95% confidence intervals (\u003cstrong\u003eFig. 4f\u0026ndash;g\u003c/strong\u003e, \u003cstrong\u003eFig. 5\u003c/strong\u003e, \u003cstrong\u003eFig. 6d\u003c/strong\u003e). Across all spatial scales using data from the single laboratory program (for which both PMMoV-normalized and unnormalized concentrations were considered), the median tau was 0.57 (range: 0.39\u0026ndash;0.72) for methods using a PMMoV-normalized wastewater value versus 0.54 (range: 0.41\u0026ndash;0.71) for methods using an unnormalized wastewater value. Similarly, any observed differences between wastewater metric type (wastewater concentrations versus WVAL values) and among spatial aggregation approaches (median versus unweighted average versus population-weighted average) were not substantial as suggested by highly overlapping 95% confidence intervals (\u003cstrong\u003eFig. 4f\u0026ndash;g\u003c/strong\u003e, \u003cstrong\u003eFig. 5\u003c/strong\u003e, \u003cstrong\u003eFig. 6d\u003c/strong\u003e). Across all spatial scales, the median tau was 0.56 (range: 0.39\u0026ndash;0.72) for methods using non-transformed wastewater concentrations versus 0.56 (range: 0.40\u0026ndash;0.71) for methods using WVAL values. Additionally, the median tau was 0.56 (range: 0.42\u0026ndash;0.68) for methods using a median, 0.56 (range: 0.39\u0026ndash;0.69) for methods using an unweighted average, and 0.56 (range: 0.40\u0026ndash;0.72) for methods using a population-weighted average across all spatial scales.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; At the California spatial scale, wastewater monitoring data from two distinct monitoring programs were considered. For the spatial aggregation methods that were considered for both monitoring programs, the correlations between test positivity and wastewater metrics were similar between programs, with only minor differences observed that were not substantial as suggested by overlapping 95% confidence intervals (\u003cstrong\u003eFig. 4f\u0026ndash;g\u003c/strong\u003e,\u003cstrong\u003e\u0026nbsp;Table S4\u003c/strong\u003e). It is hard to assess whether these minor differences in correlation strength are due to differences in laboratory methods or differences in population coverage as the multi-laboratory program includes more California WWTPs and higher population coverage than the single laboratory program.\u003c/p\u003e"},{"header":"4\tDiscussion","content":"\u003cp\u003eMeasured wastewater concentrations represent individual WWTP sewersheds. Aggregation of wastewater concentrations across WWTPs is essential for interpreting data at spatial scales that are relevant to public health jurisdictions as public health decisions are often enacted at these broader spatial scales. Here, we evaluated methods for spatially aggregating wastewater monitoring data using comparisons to clinical test positivity. The methodological approaches we considered included computing the median, unweighted average, or population-weighted average of wastewater concentrations across geographic units within a spatial scale. We additionally assessed differences in spatially aggregating non-transformed versus transformed wastewater metrics and using PMMoV-normalized versus unnormalized wastewater concentrations to determine such metrics. All spatial aggregation methods we considered were positively and temporally correlated with test positivity. An optimal spatial aggregation method should work with a mix of wastewater matrices, laboratory methods, reporting units, and normalization methods and be practical to implement and interpret.\u003c/p\u003e\n\u003cp\u003eWe did not observe substantial differences among the three methodological approaches (median, unweighted average, population-weighted average) or between the two wastewater metric types (wastewater concentrations versus WVAL values) in terms of correlation with test positivity. Nonetheless, some approaches are easier to interpret. Conceptually, population weighting makes epidemiological sense: geographic units with larger populations represent more people in the aggregate area, and it is reasonable to give these geographic units greater weight than geographic units representing smaller populations when attempting to summarize overall disease activity in the broader region. The WVAL metric can better account for differences in wastewater matrices, laboratory methods, reporting units, and normalization methods because, prior to aggregation, wastewater concentrations are standardized by converting measured concentrations to laboratory- and site-specific standard deviations above a laboratory- and site-specific baseline. Delatolla et al. [15] implemented the WVAL framework using SARS-CoV-2 RNA wastewater concentrations collected across Ontario, Canada and demonstrated its use for interpreting regional trends in disease occurrence. However, the WVAL metric may be challenging to interpret in retrospective analyses because the baseline reference date used for the WVAL calculation differs among WWTPs. For example, if one WWTP stops monitoring in May, its baseline would be calculated using the past 12 months of data from the preceding January 1. After July 1, its baseline would not be recalculated whereas the baselines of all other WWTPs would be recalculated using the past 12 months of data from July 1. The WVAL method has several other limitations, including its highly variable performance at the individual WWTP level, making it difficult to compare standardized WVAL values between WWTPs [8,41]. WWTPs also cannot be included in analyses using the WVAL until they have at least 6 weeks (8 weeks as of August 2025) of concentration data available [22,35]. In contrast, the use of non-transformed wastewater concentrations allows WWTPs to be included in analyses as soon as monitoring begins and avoids imposing potentially misleading transformations to the data. Nevertheless, non-transformed wastewater concentrations are only suitable for aggregation when reporting units are consistent across WWTPs. Standardizing wastewater monitoring protocols across WWTPs and jurisdictions would allow for easier spatial aggregation of wastewater monitoring data\u0026mdash;and improved data reliability [42]. However, laboratory standardization may be challenging in practice, as each laboratory will likely have different resources, equipment, and assay validation procedures. Even with standardized laboratory procedures across WWTPs, wastewater concentrations may still vary between sites due to environmental matrix effects.\u003c/p\u003e\n\u003cp\u003eAlthough the difference was not substantial, using PMMoV-normalized versus unnormalized wastewater concentrations of SARS-CoV-2 RNA generally improved the correlation between wastewater metrics and COVID-19 test positivity. This finding aligns conceptually with a mass balance model relating PMMoV-normalized SARS-CoV-2 RNA concentrations in wastewater solids to the population fraction shedding SARS-CoV-2 RNA in stool [33]. Other studies that evaluated PMMoV normalization\u0026mdash;all of which quantified viral RNA in the liquid fraction of wastewater\u0026mdash;concluded that PMMoV normalization only sometimes improves correlation strength with clinical disease metrics [11,43\u0026ndash;47]. Presently, some wastewater monitoring programs do not measure PMMoV RNA concentrations and instead normalize wastewater concentrations by flow rate and population (\u0026ldquo;flowpop\u0026rdquo; normalization). Prior to August 2025, the CDC\u0026rsquo;s WVAL methodology preferentially used flowpop-normalized concentrations over PMMoV-normalized concentrations when both normalization approaches were available [35]. The methodology has since been updated to use only unnormalized concentrations [22]. The multi-laboratory program dataset includes some WWTPs with flow rate measurements, so we conducted a supplementary analysis using wastewater metrics calculated using flowpop-normalized values (\u003cstrong\u003eFig. S11\u003c/strong\u003e, \u003cstrong\u003eTable S7\u003c/strong\u003e). Only 29 WWTPs from the multi-laboratory program reported flow rate measurements for this supplementary analysis (compared to 78 WWTPs reporting PMMoV RNA concentrations for the main analysis), but Kendall\u0026rsquo;s tau estimates calculated using flowpop-normalized wastewater values were similar to estimates calculated using PMMoV-normalized wastewater values. Regardless, a standard normalization approach is still needed to account for dilution effects, such as from rainfall or other changes in flow rate, and to improve comparisons across WWTPs.\u003c/p\u003e\n\u003cp\u003eAt the California state spatial scale, we did not observe differences in correlations between wastewater aggregates and test positivity using wastewater concentrations from the single laboratory versus multi-laboratory monitoring programs. Future studies could assess the correlations tested herein at finer spatial scales, such as California Regional Public Health Office regions [48] and California counties, although the aggregation approaches appear to be robust to differences in wastewater monitoring protocols at the state spatial scale. This finding aligns with previous work examining inter-laboratory variability. Chik et al. [49] provided wastewater samples spiked with SARS-CoV-2 surrogates to eight laboratories in Canada. Concentrations measured using reverse transcription quantitative polymerase chain reaction (RT-qPCR) were consistently on the same order of magnitude across laboratories [49]. Moreover, Pecson et al. [50] evaluated the sensitivity and reproducibility of 36 quantification methods, including both RT-qPCR and RT-dPCR platforms, and observed overall reproducible results with slight differences across methodological variations. Thus, spatial aggregation of wastewater concentrations is still feasible, even when concentrations are generated using different protocols, so long as concentrations are reported using the same units.\u003c/p\u003e\n\u003cp\u003eHerein, we only evaluated spatial aggregation methods using correlations with test positivity. However, test positivity data do not necessarily represent a gold standard metric for disease occurrence. Testing data are biased towards those with symptomatic disease and access to healthcare, and test positivity may become further biased by changes in testing volume over time and across different geographic regions. Other use cases for wastewater monitoring data include characterizing trends and relative levels in disease activity [8,20]. To report trends and relative levels at jurisdictional scales, Chan and Boehm [20]\u003csup\u003e \u003c/sup\u003edemonstrate how trends and levels may be calculated at the WWTP sewershed scale and then spatially aggregated based on frequency (i.e., the most frequent level or trend among all WWTPs in a state would be the level or trend representing that state). Population weighting could further be incorporated into this approach if desired by weighting counts of levels or trends by population prior to determining the most frequent level or trend among all geographic units in the aggregate area.\u003c/p\u003e\n\u003cp\u003eThis study used data from a multi-laboratory sampling program in California to assess the performance of spatial aggregation methods across different laboratories and methods. However, only laboratories sampling within this program that provided detailed laboratory method documentation to the author team were included. The exclusion of multiple laboratories that did not provide method documentation limited our ability to assess spatial aggregation across many laboratories. Future studies assessing all available laboratory data in California may provide further insight into the ideal spatial aggregation method for a complex, multi-laboratory program. In order for these studies to be completed, wastewater monitoring data should be accompanied by complete methodological protocols that follow minimum reporting guidelines [51,52]\u003c/p\u003e\n\u003cp\u003eWe only conducted this analysis using SARS-CoV-2 wastewater monitoring data and COVID-19 case surveillance data. These findings may be generalizable to other commonly detected pathogens that are at least seasonally abundant in wastewater, but further studies should be done to repeat this analysis for those pathogens. Meanwhile, a limitation of all the spatial aggregation approaches is that some sewersheds may intersect multiple jurisdictions (namely counties). Herein, we associated a sewershed with the jurisdiction its WWTP is physically located in for spatial aggregation purposes. Consequently, if a sewershed intersects a jurisdiction\u0026mdash;but the WWTP of the sewershed is not physically present in or confined to that jurisdiction\u0026mdash;the jurisdiction will not be represented in aggregate wastewater metrics. More sophisticated spatial aggregation approaches could account for the proportion of each jurisdiction that is serviced by a sewershed using geoprocessing tools. Another limitation associated with any spatial aggregation is that differences within the aggregate area may become masked. Zhu et al. [17] showed that geographic units with high wastewater viral load were masked in the spatially aggregated area which may negatively impact risk perception and public health response. Although spatial data aggregation is useful for data communication and interpretation at jurisdictional scales, paying attention to pathogen occurrence patterns at small spatial scales (i.e., WWTP sewersheds) is still important to recognize localized patterns of disease activity and mobilize appropriate public health response [8].\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCDPH\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCalifornia Department of Public Health\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHHS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHealth and Human Services\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNREVSS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNational Respiratory and Enteric Virus Surveillance System\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eSupporting Material\u003c/p\u003e\n\u003cp\u003eSpatialAggregationMethods_SI.pdf\u003c/p\u003e\n\u003cp id=\"_Toc214028155\"\u003eFunding statement\u003c/p\u003e\n\u003cp\u003eThis work was funded in part by a gift from the Sergey Brin Family Foundation to ABB. This study was supported in part by the CDC Epidemiology and Laboratory Capacity for Infectious Diseases Cooperative Agreement (Grant number 6NU50CK000539-04-04). The funders played no role in study design, data collection, analysis and interpretation of data, or the writing of this manuscript.\u003c/p\u003e\n\u003cp\u003eEthics statement\u003c/p\u003e\n\u003cp\u003eThis study was reviewed by the Stanford University Human \u0026amp; Animal Research Compliance Office and determined not to involve human subjects. The study is exempt from oversight.\u003c/p\u003e\n\u003cp\u003eClinical trial number\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConsent to publish declaration\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConsent to participate declaration\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eAuthor contribution declaration\u003c/p\u003e\n\u003cp\u003eEMGC: Conceptualization, Methodology, Software, Validation, Formal Analysis, Investigation, Writing - Original Draft, Writing - Review \u0026amp; Editing, Visualization, Project Administration\u003c/p\u003e\n\u003cp\u003eEB: Conceptualization, Methodology, Investigation, Resources, Data Curation, Writing - Original Draft, Writing - Review \u0026amp; Editing\u003c/p\u003e\n\u003cp\u003eAZ: Conceptualization, Methodology, Writing - Review \u0026amp; Editing\u003c/p\u003e\n\u003cp\u003eCL: Methodology, Validation, Investigation, Data Curation, Writing \u0026ndash; Review \u0026amp; Editing\u003c/p\u003e\n\u003cp\u003eATY: Conceptualization, Writing - Review \u0026amp; Editing, Supervision, Project Administration, Funding Acquisition\u003c/p\u003e\n\u003cp\u003eABB: Conceptualization, Validation, Resources, Data Curation, Writing - Review \u0026amp; Editing, Supervision, Project Administration, Funding Acquisition\u003c/p\u003e\n\u003cp\u003eCompeting interests statement\u003c/p\u003e\n\u003cp\u003eThe authors have no competing interests to disclose.\u003c/p\u003e\n\u003cp\u003eData availability statement\u003c/p\u003e\n\u003cp\u003eData and R code used for this study are publicly available through the Stanford Digital Repository: https://purl.stanford.edu/tg451qc5869.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eWe thank the participating wastewater treatment plant staff for collecting samples for this project and CDPH Drinking Water and Radiation Lab (DWRL) wastewater team for processing and testing samples.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003ePaul JR, Trask JD, Gard S. 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Regional Public Health Office [Internet]. 2024 [cited 2025 Aug 29]. Available from: https://www.cdph.ca.gov/Programs/RPHO\u003c/li\u003e\n \u003cli\u003eChik AHS, Glier MB, Servos M, Mangat CS, Pang XL, Qiu Y, D\u0026rsquo;Aoust PM, Burnet JB, Delatolla R, Dorner S, Geng Q, Giesy JP, McKay RM, Mulvey MR, Prystajecky N, Srikanthan N, Xie Y, Conant B, Hrudey SE. Comparison of approaches to quantify SARS-CoV-2 in wastewater using RT-qPCR: Results and implications from a collaborative inter-laboratory study in Canada. J Environ Sci China. 2021 Sep;107:218\u0026ndash;29.\u003c/li\u003e\n \u003cli\u003ePecson BM, Darby E, Haas CN, Amha YM, Bartolo M, Danielson R, Dearborn Y, Giovanni GD, Ferguson C, Fevig S, Gaddis E, Gray D, Lukasik G, Mull B, Olivas L, Olivieri A, Qu Y, Consortium SC 2 I. 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Clin Chem. 2009 Apr 1;55(4):611\u0026ndash;22.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8205614/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8205614/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSpatial aggregation of wastewater concentrations is necessary to summarize wastewater monitoring data across multiple wastewater treatment plants (WWTPs) because public health practitioners often enact public health action at broader spatial scales. We applied various approaches for spatially aggregating wastewater concentrations and evaluated how well aggregated wastewater metrics correlated with clinical disease metrics on the same spatial scale. We used wastewater SARS-CoV-2 RNA concentrations from two wastewater monitoring programs. One included 188 WWTPs across the USA and a single laboratory; the other included 78 WWTPs across California and two distinct laboratories. We spatially aggregated wastewater concentrations across WWTPs using the following approaches: median, unweighted average, and population-weighted average. We considered wastewater concentrations with and without normalization by pepper mild mottle virus RNA concentrations and with and without transformation using the wastewater viral activity level prior to aggregation. For the single laboratory program, we spatially aggregated wastewater concentrations to the state, Health and Human Services (HHS) region, and USA spatial scales. For the multi-laboratory program, we spatially aggregated wastewater concentrations to the county and state (California) spatial scales. We then assessed the correlation between spatially aggregated wastewater metrics and clinical COVID-19 test positivity on a weekly basis for the following spatial scales: California, HHS region, and USA. Wastewater metrics and test positivity were positively and significantly correlated using all spatial aggregation methods at each spatial scale considered, and no method was superior. Public health practitioners should adopt a spatial aggregation method that is suitable for the setup of a wastewater monitoring program.\u003c/p\u003e","manuscriptTitle":"Spatial aggregation methods for interpreting wastewater concentrations at jurisdictional scales: Insights from two SARS-CoV-2 monitoring programs","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-03 06:33:25","doi":"10.21203/rs.3.rs-8205614/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"21257e2d-4138-4433-a188-2698c905e7e4","owner":[],"postedDate":"December 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-25T08:42:43+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-03 06:33:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8205614","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8205614","identity":"rs-8205614","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}