ACS ES&T Water

Wastewater surveillance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has proven a practical complement to clinical data for assessing community-scale infection trends. Clinical assays, such as the CDC-promulgated N1, N2, and N3 have been used to detect and quantify viral RNA in wastewater but, to date, have not included estimates of reliability of true positive or true negative. Bayes Theorem was applied to estimate Type I and Type II error rates for detections of the virus in wastewater. Conditional probabilities of true positive or true negative were investigated when one assay was used, or multiple assays were run concurrently. Cumulative probability analysis was used to assess the likelihood of true SARS-CoV-2 detection using multiple samples. Results demonstrate highly reliable positive (>0.86 for priors >0.25) and negative (>0.80 for priors = 0.50) results using a single assay. Using N1 and N2 concurrently caused greater reliability (>0.99 for priors <0.05) when results concurred but generated potentially counterintuitive interpretations when results were discordant. Regional wastewater surveillance data was investigated as a means of setting prior probabilities. Probability of true detection with a single marker was investigated using cumulative probability across all combinations of positive and negative results for a set of three samples. Findings using a low (0.11) and uniformed (0.50) initial prior resulted in high probabilities of detection (>0.95) even when a set of samples included one or two negative results, demonstrating the influence of high sensitivity and specificity values. Analyses presented here provide a practical framework for understanding analytical results generated by wastewater surveillance programs.

BackgroundWastewater monitoring is increasingly used for community surveillance of infectious diseases, especially after the COVID-19 pandemic as the genomic footprints of pathogens shed by infected individuals can be traced in the environment. However, detection and concentration of pathogens in the environmental samples and their efficacy in predicting infectious diseases can be influenced by meteorological conditions and quality of samples. ObjectivesThis research examines whether meteorological conditions and sample pH affect SARS-CoV-2 concentrations in wastewater samples, and whether the association of SARS-CoV-2 with COVID-19 cases and mortality improves when adjusted for meteorological conditions and sample pH value in Miami-Dade County, FL. MethodsDaily wastewater samples were collected from Miami-Dade Wastewater Treatment Plant in Key Biscayne, Florida from August 2021 to August 2022. The samples were analyzed for pH and spiked with OC43. RNA was extracted from the concentrated wastewater sample and SARS-CoV-2 was quantified using qPCR. COVID-19 and mortality data were acquired from the Centers for Disease Control and Prevention (CDC) and meteorological data from the National Climatic Data Center. COVID-19 case and mortality rates were modelled with respect to time-lagged wastewater SARS-CoV-2 adjusting for meteorological conditions, and sample pH value and OC43 recovery. ResultsTemperature, dew point, pH values and OC43 recovery showed significant associations with wastewater SARS-CoV-2. Time-lagged wastewater SARS-CoV-2 showed significant associations with COVID-19 case and mortality incidence rates. This association improved when wastewater SARS-CoV-2 levels were adjusted for (or instrumented on) meteorological conditions, OC43 recovery, and sample pH. A 0.47% change in COVID-19 case incidence rate was associated with 1% change in wastewater SARS-CoV-2 ({beta} [~] 0.47; 95% CI = 0.29 - 0.64; p < 0.001). A 0.12 % change in COVID-19 mortality rate was associated with 1 % change in SARS-CoV-2 in wastewater 44 days prior. A 0.07% decline in COVID-19 mortality rate was associated with a unit increase in ambient temperature 28 days prior. DiscussionTime lagged wastewater SARS-CoV-2 (and its adjustment for sample pH and RNA recovery) and meteorological conditions can be used for the surveillance of COVID-19 case and mortality. These findings can be extrapolated to improve the surveillance of other infectious diseases by proactive measurements of infectious agent(s) in the wastewater samples, adjusting for meteorological conditions and sample pH value.

Wastewater-based epidemiology provides an approach for assessing the prevalence of pathogens such as COVID-19 in a sewer service area. In this study, SARS-CoV-2 RNA was measured serially in 44 wastewater treatment plants of varying service capacities comprising approximately 67% of the population of Minnesota, from September 2020 through December 2022. We employed linear regression models to establish a predictive relationship between the weekly SARS-CoV-2 RNA concentrations in wastewater and clinical case counts. Metrics were assessed under specified transformation and normalization methods which we confirmed by cross-validation averaged across the enrolled treatment plants. We report that the relationship between COVID-19 incidence and SARS-CoV-2 RNA in wastewater may be treatment plant-specific. Toward establishing guidelines for pathogen surveillance, we further studied storage and time-to-analysis for RNA wastewater data and observed large effects of storage temperature, indicating that collection methods may have an important effect on the utility and validity of wastewater data for infectious disease monitoring. Our findings are additive for any large-scale wastewater surveillance program.

The COVID-19 pandemic has exacerbated the disparities in healthcare delivery in the US. Many communities had, and continue to have, limited access to COVID-19 testing, making it difficult to track the spread and impact of COVID-19 in early days of the outbreak. To address this issue we monitored severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA at the population-level using municipal wastewater influent from 19 cities across the state of Minnesota during the COVID-19 outbreak in Summer 2020. Viral RNA was detected in wastewater continually for 20-weeks for cities ranging in populations from 500 to >1, 000, 000. Using a novel indexing method, we were able to compare the relative levels of SARS-CoV-2 RNA for each city during this sampling period. Our data showed that viral RNA trends appeared to precede clinically confirmed cases across the state by several days. Lag analysis of statewide trends confirmed that wastewater SARS-CoV-2 RNA levels preceded new clinical cases by 15-17 days. At the regional level, new clinical cases lagged behind wastewater viral RNA anywhere from 4-20 days. Our data illustrates the advantages of monitoring at the population-level to detect outbreaks. Additionally, by tracking infections with this unbiased approach, resources can be directed to the most impacted communities before the need outpaces the capacity of local healthcare systems.

Surveillance of rotavirus infections remains critical because vaccines are underutilized in the USA. Using wastewater solids measurements of rotavirus RNA collected over one year from 185 wastewater treatment plants (WWTPs) in the USA, we inferred spatiotemporal occurrence patterns of rotavirus infections and compared occurrence patterns to clinical metrics of infections and markers of vaccination coverage. We also estimated infection prevalence from wastewater measurements using available data on rotavirus RNA shedding in feces. Nationally, wastewater measurements of rotavirus RNA were correlated with clinical metrics of infection and exhibited elevated winter-spring concentrations beginning in the South. WWTP service areas characterized by markers of high vaccination coverage generally experienced a shorter duration of elevated rotavirus concentrations compared to areas characterized by markers of low vaccination coverage. Rotavirus infection prevalence estimates were highly uncertain and sensitive to shedding parameters. Wastewater monitoring of vaccine-preventable diseases is valuable for informing where vaccination campaigns should be targeted.

BackgroundWastewater monitoring for virus infections within communities can complement conventional clinical surveillance. Currently, most SARS-CoV-2 testing is performed during clinical encounters with symptomatic individuals, and therefore likely underrepresents actual population prevalence. Randomized testing on a regular basis to estimate population-level infection rates is prohibitively costly and is hampered by a range of barriers associated with participation in clinical research. In comparison, community-level fecal monitoring can be performed through wastewater surveillance and can effectively surveil communities with less temporal lag than other surveillance methods. However, epidemiologically-defined protocols for wastewater sample site selection are lacking. MethodsHerein we describe methods for developing a geographically-resolved population-level wastewater sampling approach in Jefferson County, Kentucky which may have general applicability for cities throughout the United States. This approach was developed by the selection of sampling locations along sewer lines transporting raw wastewater from geographically and demographically distinct areas that correspond with locations where random testing of residents occurs. ConclusionsDevelopment of this protocol for population-level sampling for SARS-CoV-2 prevalence in wastewater can be utilized to inform consistent wastewater monitoring among cities for up-to-date and geographically-resolved information on COVID-19 prevalence within communities. This information could substantially supplement public health surveillance of COVID-19 and thus serve to better guide targeted mitigation strategies throughout the United States.

Wastewater-based epidemiology, which seeks to assess disease occurrence in communities through measurements of infectious disease biomarkers in wastewater, may represent a valuable tool for understanding occurrence of hepatitis A infections in communities. In this study, we measured concentrations of Hepatovirus A (HAV) RNA, in samples from 191 wastewater treatment plants spanning 40 US states and the District of Columbia from September 2023 to June 2024 and compared the measurements with traditional measures of disease occurrence. Nationally, 13.76% of the 21,602 wastewater samples were positive for HAV RNA, and both concentrations and positivity rates were associated with NNDSS hepatitis A case data nationally (Kendall rank correlation coefficient = 0.20, concentrations; and 0.33, positivity rate; both p<0.05). We further demonstrated that higher rates of wastewater HAV detection were positively associated with socioeconomic indicators of vulnerability including homelessness and drug overdose deaths (both p<0.0001). Areas with above average levels of homelessness were 48% more likely to have HAV wastewater detections, while areas with above average levels of drug overdose deaths were 14% more likely to have HAV wastewater detections. Using more granular case data, we present a case study in the state of Maine that reinforces these results and suggests a potential lead time for wastewater over clinical case detection and exposure events. The ability to detect HAV RNA in wastewater before clinical cases emerge could allow public health officials to implement targeted interventions like vaccination campaigns. ImportanceDespite the existence of a highly effective vaccine for Hepatitis A, outbreaks in vulnerable populations remain common. The disease can be asymptomatic or subclinical, and disproportionately impacts populations with inadequate access to healthcare, leading to a severe underestimation of the occurrence of this viral infection. This study investigates the potential for wastewater measurements of biomarkers of the causative agent of hepatitis A (HAV RNA) to provide insights into disease occurrence. Results highlight the potential for wastewater-based epidemiology to be a complementary tool to traditional surveillance for monitoring and controlling HAV transmission.

Wastewater monitoring for infectious disease targets is increasingly used to better understand circulation of diseases. The present study validated hydrolysis-probe digital droplet (reverse-transcriptase (RT))-PCR assays for important enteric viruses (rotavirus, adenovirus group F, norovirus GI and GII, and enteroviruses), outbreak or emerging viruses (hepatitis A and West Nile virus), and an emerging drug resistant fungal pathogen (Candida auris). We used the assays to retrospectively measure concentrations of the targets in wastewater solids. Viral and fungal nucleic-acid concentrations were measured in two wastewater solids samples per week at two wastewater treatment plants in the San Francisco Bay Area of California, USA for 26 months. We detected all targets in wastewater solids with the exception of West Nile virus. At both wastewater treatment plants, human adenovirus group F was detected at the highest concentrations, followed by norovirus GII, enteroviruses, norovirus GI, and rotavirus at the lowest concentrations. Hepatitis A and C. auris were detected less consistently than the aforementioned viruses. Enterovirus D68 was detected in a limited time frame during fall 2022 at both sites. The measurements reported herein, and in some cases their seasonal trends, are consistent with previous reports of these targets in wastewater. These measurements represent some of the first quantitative measurements of these infectious disease targets in the solid fraction of wastewater. This study lays a foundation for the use of wastewater solids for the detection of specific infectious disease targets in wastewater monitoring programs aimed to better understand the spread of these diseases.

BACKGROUNDWastewater-based epidemiology (WBE) provides an opportunity for near real-time, cost-effective monitoring of community level transmission of SARS-CoV-2, the virus that causes COVID-19. Detection of SARS-CoV-2 RNA in wastewater can identify the presence of COVID-19 in the community, but methods are lacking for estimating the numbers of infected individuals based on wastewater RNA concentrations. METHODSComposite wastewater samples were collected from three sewersheds and tested for SARS-CoV-2 RNA. A Susceptible-Exposed-Infectious-Removed (SEIR) model based on mass rate of SARS-CoV-2 RNA in the wastewater was developed to predict the number of infected individuals. Predictions were compared to confirmed cases identified by the South Carolina Department of Health and Environmental Control for the same time period and geographic area. RESULTSModel predictions for the relationship between mass rate of virus release to the sewersheds and numbers of infected individuals were validated based on estimated prevalence from individual testing. A simplified equation to estimate the number of infected individuals fell within the 95% confidence limits of the model. The unreported rate for COVID-19 estimated by the model was approximately 12 times that of confirmed cases. This aligned well with an independent estimate for the state of South Carolina. CONCLUSIONSThe SEIR model provides a robust method to estimate the total number of infected individuals in a sewershed based on the mass rate of RNA copies released per day. This overcomes some of the limitations associated with individual testing campaigns and thereby provides an additional tool that can be used to better inform policy decisions.

BackgroundWastewater measurements of SARS-CoV-2 RNA have been extensively used to supplement clinical data on COVID-19. Most examples in the literature that describe wastewater monitoring for SARS-CoV-2 RNA use samples from wastewater treatment plants and individual buildings that serve as the primary residence of community members. However, wastewater surveillance can be an attractive supplement to clinical testing in K-12 schools where individuals only spend a portion of their time but interact with others in close proximity, increasing risk of potential transmission of disease. MethodsWastewater samples were collected from two K-12 schools in California and divided into solid and liquid fractions to be processed for detection of SARS-CoV-2. The resulting detection rate in each wastewater fraction was compared to each other and the detection rate in pooled clinical specimens. ResultsMost wastewater samples were positive for SARS-CoV-2 RNA when clinical testing was positive (75% for solid samples and 100% for liquid samples). Wastewater samples continued to test positive for SARS-CoV-2 RNA when clinical testing was negative or in absence of clinical testing (83% for both solid and liquid samples), indicating presence of infected individuals in the schools. Wastewater solids had a higher concentration of SARS-CoV-2 than wastewater liquids on an equivalent mass basis by three orders of magnitude.

Wastewater-based surveillance (WBS) is widely used to monitor respiratory viruses, yet uncertainties remain regarding how viral RNA concentrations in wastewater reflect infection dynamics. Specifically, diurnal variation in shedding and RNA losses during in-sewer transport can impact measured signals. We conducted a field study in a 5-km trunk sewer (travel time of one hour). Wastewater was sampled at the sewer inlet and outlet using autosamplers collecting time-proportional one-hour composite samples over 24 hours. The one-hour composite samples were analyzed for assessing intra-daily fluctuations, and 24-hour composites for signal change. Biofilms from the sewer-pipe walls were collected at three locations. Nucleic acids were extracted, and SARS-CoV-2, Influenza A/B, and Respiratory Syncytial Virus (RSV) RNA were quantified using a multiplex digital PCR assay. All viruses showed pronounced diurnal variation, with consistent morning load peaks. Viral RNA in the bulk liquid decreased during in-sewer transport, with modelled changes ranging from 15% to 72% across pathogens. Biofilms served as minor reservoirs of viral RNA; for SARS-CoV-2, sequencing revealed similarity between biofilm and bulk liquid RNA. Our study provides a full-scale assessment of in-sewer transport effects on viral RNA and highlights the need to account for complex in-sewer dynamics when interpreting WBS data.

Concentrations of nucleic acids from a range of respiratory viruses including human influenza A and B, respiratory syncytial virus (RSV), metapneumovirus, parainfluenza virus, rhinovirus, and seasonal coronaviruses in wastewater solids collected from wastewater treatment plants correlate to clinical data on disease occurrence in the community contributing to the wastewater. Viral nucleic acids enter wastewater from various excretions including stool, urine, mucus, sputum, and saliva deposited in toilets or other drains in buildings. In order to relate the measured concentrations in wastewater at a treatment plant to actual number of infections in a community, concentrations of the viral nucleic acids in these human excretions are needed as inputs to a mass balance model. In this study, we carried out a systematic review and meta-analysis to characterize the concentrations and presence of influenza A and B, respiratory syncytial virus (RSV), metapneumovirus, parainfluenza virus, rhinovirus, and seasonal coronaviruses in stool, urine, mucus, sputum, and saliva. The systematic review protocol can be accessed at https://doi.org/10.17605/OSF.IO/ESVYC. We identified 220 data sets from 50 unique articles that met inclusion criteria and reported information on viral concentrations and presence in these excretions. Data were unevenly distributed across virus type (with the most available for influenza) and excretion type (with the most available for respiratory excretions). The majority of data sets only reported the presence or absence of the virus in an excretion in a cross-sectional study design. There is a need for more concentration data, including longitudinal data, across all respiratory virus and excretion types. Such data would allow quantitatively linking virus wastewater concentrations to numbers of infected individuals.

The ongoing COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) requires a significant, coordinated public health response. Assessing case density and spread of infection is critical and relies largely on clinical testing data. However, clinical testing suffers from known limitations, including test availability and a bias towards enumerating only symptomatic individuals. Wastewater-based epidemiology (WBE) has gained widespread support as a potential complement to clinical testing for assessing COVID-19 infections at the community scale. The efficacy of WBE hinges on the ability to accurately characterize SARS-CoV-2 RNA concentrations in wastewater. To date, a variety of sampling schemes have been used without consensus around the appropriateness of grab or composite sampling. Here we address a key WBE knowledge gap by examining the variability of SARS-CoV-2 RNA concentrations in wastewater grab samples collected every 2 hours for 72 hours compared with three corresponding 24-hour flow-weighted composite samples collected over the same period. Results show relatively low variability (respective means for N1, N2, N3 assays = 608, 847.9, 768.4 copies 100 mL-1, standard deviations = 501.4, 500.3, 505.8 copies 100 mL-1) for grab sample concentrations, and good agreement between most grab samples and their respective composite (mean deviation from composite = 159 copies 100 mL-1). When SARS-CoV-2 RNA concentrations are used to calculate viral load (RNA concentration * total influent flow the sample day), the discrepancy between grabs (log10 range for all grabs = 11.9) or a grab and its associated 24-hour composite (log10 difference = 11.6) are amplified. A similar effect is seen when estimating carrier prevalence in a catchment population with median estimates based on grabs ranging 63-1885 carriers. Findings suggest that grab samples may be sufficient to characterize SARS-CoV-2 RNA concentrations, but additional calculations using these data may be sensitive to grab sample variability and warrant the use of flow-weighted composite sampling. These data inform future WBE work by helping determine the most appropriate sampling scheme and facilitate sharing of datasets between studies via consistent methodology.

Wastewater-based epidemiology (WBE) may be useful for informing public health response to viral diseases like COVID-19 caused by SARS-CoV-2. We quantified SARS-CoV-2 RNA in wastewater influent and primary settled solids in two wastewater treatment plants to inform the pre-analytical and analytical approaches, and to assess whether influent or solids harbored more viral targets. The primary settled solids samples resulted in higher SARS-CoV-2 detection frequencies than the corresponding influent samples. Likewise, SARS-CoV-2 RNA was more readily detected in solids using one-step digital droplet (dd)RT-PCR than with two-step RT-QPCR and two-step ddRT-PCR, likely owing to reduced inhibition with the one-step ddRT-PCR assay. We subsequently analyzed a longitudinal time series of 89 settled solids samples from a single plant for SARS-CoV-2 RNA as well as coronavirus recovery (bovine coronavirus) and fecal strength (pepper mild mottle virus, PMMoV) controls. SARS-CoV-2 RNA targets N1 and N2 concentrations correlate positively and significantly with COVID-19 clinical confirmed case counts in the sewershed. Together, the results demonstrate that measuring SARS-CoV-2 RNA concentrations in settled solids may be a more sensitive approach than measuring SARs-CoV-2 in influent.

BackgroundThe effective reproduction number (Re) serves as a metric of population-wide, time-varying disease spread. During the COVID-19 pandemic, Re was primarily estimated from clinical surveillance data streams (Rcc), which have varied in quality and representativeness due to changes in testing volume, test-seeking behavior, and resource constraints. Deriving Re from alternative data sources such as wastewater could inform future public health responses. ObjectivesWe estimated county-aggregated, sewershed-restricted wastewater-based SARS-CoV-2 Re (Rww) from May 1, 2022 to April 30, 2023 for five counties in California of varying population sizes, clinical testing rates, demographics, proportions surveilled by wastewater, and sampling frequencies to validate the reliability of Rww as a real-time disease surveillance metric. MethodsWe produced both instantaneous and cohort sewershed-restricted Re using smoothed and deconvolved wastewater concentrations. We then population-weighted and aggregated these sewershed-level estimates to arrive at county-level Re. Using mean absolute error (MAE), Spearmans rank correlation ({rho}), confusion matrix classification, and cross-correlation analyses, we compared the timing and trajectory of two Rww models to: (1) a publicly available, county-level ensemble of Rcc estimates, and (2) a county-aggregated, sewershed-restricted Rcc. ResultsBoth Rww models demonstrated high concordance with traditional Rcc estimates, as indicated by low mean absolute errors (MAE [&le;] 0.09), significant positive Spearman correlation (Spearman {rho} [&ge;] 0.66, p < 0.001), and high confusion matrix classification accuracy ([&ge;] 0.81). The relative timings of Rwwand Rcc were less clear, with cross-correlation analyses suggesting strong associations for a wide range of temporal lags that varied by county and Rww model type. DiscussionThis Re estimation methodology provides a generalizable, robust, and operationalizable framework for estimating county-level Rww. Our results support the additional use of Rwwas an epidemiological tool for surveillance. Based on this research, we produced publicly available Rww nowcasts for the California Communicable diseases Assessment Tool (https://calcat.covid19.ca.gov/cacovidmodels/).

ImportanceIdentifying and tracking new infections during an emerging pandemic is crucial to design and deploy interventions to protect populations and mitigate its effects, yet it remains a challenging task. ObjectiveTo characterize the ability of non-probability online surveys to longitudinally estimate the number of COVID-19 infections in the population both in the presence and absence of institutionalized testing. DesignInternet-based non-probability surveys were conducted, using the PureSpectrum survey vendor, approximately every 6 weeks between April 2020 and January 2023. They collected information on COVID-19 infections with representative state-level quotas applied to balance age, gender, race and ethnicity, and geographic distribution. Data from this survey were compared to institutional case counts collected by Johns Hopkins University and wastewater surveillance data for SARS-CoV-2 from Biobot Analytics. SettingPopulation-based online non-probability survey conducted for a multi-university consortium --the Covid States Project. ParticipantsResidents of age 18+ across 50 US states and the District of Columbia in the US. Main Outcomes and MeasuresThe main outcomes are: (a) survey-weighted estimates of new monthly confirmed COVID-19 cases in the US from January 2020 to January 2023, and (b) estimates of uncounted test-confirmed cases, from February 1, 2022, to January 1, 2023. These are compared to institutionally reported COVID-19 infections and wastewater viral concentrations. ResultsThe survey spanned 17 waves deployed from June 2020 to January 2023, with a total of 408,515 responses from 306,799 respondents with mean age 42.8 (STD 13) years; 202,416 (66%) identified as women, and 104,383 (34%) as men. A total of 16,715 (5.4%) identified as Asian, 33,234 (10.8%) as Black, 24,938 (8.1%) as Hispanic, 219,448 (71.5%) as White, and 12,464 (4.1%) as another race. Overall, 64,946 respondents (15.9%) self-reported a test-confirmed COVID-19 infection. National survey-weighted test-confirmed COVID-19 estimates were strongly correlated with institutionally reported COVID-19 infections (Pearson correlation of r=0.96; p=1.8 e-12) from April 2020 to January 2022 (50-state correlation average of r=0.88, SD = 0.073). This was before the government-led mass distribution of at-home rapid tests. Following January 2022, correlation was diminished and no longer statistically significant (r=0.55, p=0.08; 50-state correlation average of r=0.48, SD = 0.227). In contrast, survey COVID-19 estimates correlated highly with SARS-CoV-2 viral concentrations in wastewater both before (r=0.92; p=2.2e-09) and after (r=0.89; p=2.3e-04) January 2022. Institutionally reported COVID-19 cases correlated (r = 0.79, p=1.10e-05) with wastewater viral concentrations before January 2022, but poorly (r = 0.31, p=0.35) after, suggesting both survey and wastewater estimates may have better captured test-confirmed COVID-19 infections after January 2022. Consistent correlation patterns were observed at the state-level. Based on national-level survey estimates, approximately 54 million COVID-19 cases were unaccounted for in official records between January 2022 and January 2023. Conclusions and RelevanceNon-probability survey data can be used to estimate the temporal evolution of test-confirmed infections during an emerging disease outbreak. Self-reporting tools may enable government and healthcare officials to implement accessible and affordable at-home testing for efficient infection monitoring in the future. Trial RegistrationNA Key PointsO_ST_ABSQuestionC_ST_ABSCan non-probability survey data accurately track institutionally confirmed COVID-19 cases in the United States, and provide estimates of unaccounted infections when rapid at-home tests are popularized and institutionalized tests are discontinued? FindingsThe proportion of individuals reporting a positive COVID-19 infection in a longitudinal non-probability survey closely tracked the institutionally reported proportions in the US, and nationally-aggregated wastewater SARS-CoV-2 viral concentrations, from April 2020 to February 2022. Survey estimates suggest that a high number of confirmed infections may have been unaccounted for in official records starting in February 2022, when large-scale distribution of rapid at-home tests occurred. This is further confirmed by viral concentrations in wastewater. MeaningNon-probability online surveys can serve as an effective complementary method to monitor infections during an emerging pandemic. They provide an alternative for estimating infections in the absence of institutional testing when at-home tests are widely available. Longitudinal surveys have the potential to guide real-time decision-making in future public health crises.

The COVID-19 pandemic has highlighted limitations in case-based surveillance due to inconsistent testing and reporting. Wastewater-based epidemiology (WBE) has emerged as a promising alternative for tracking SARS-CoV-2 transmission, capturing both symptomatic and asymptomatic infections. The aim of this study is to evaluate the effectiveness of WBE in estimating the effective reproduction number (Rt) of SARS-CoV-2 in Georgia. We used a Generalized Linear Mixed Model to analyze viral concentration data from multiple wastewater treatment plants (WWTPs) collected between June 1, 2022 and December 15, 2022. After controlling for flow rates and other spatiotemporal differences between the plants, residuals from the model were used to estimate wastewater-based Rt. It then was compared to case-based Rt estimates using Spearman correlations. The two Rt estimates generally aligned across most sites, with stronger correlations in areas with higher case counts (Spearman correlations ranging from 0.29 to 0.88, p < 0.001). The two estimates reflected the increases and decreases in transmission within two weeks of each other with wastewater being a potentially more sensitive surveillance instrument for transmission trend. These findings suggest that WBE is a reliable tool for estimating SARS-CoV-2 transmissibility and can complement traditional surveillance methods for a more comprehensive public health response.

BackgroundEnteric infections are important causes of morbidity and mortality, yet clinical surveillance is limited. Wastewater-based epidemiology (WBE) has been used to study community circulation of individual enteric viruses and panels of respiratory diseases, but there is limited work studying concurrent circulation of a suite of important enteric viruses. MethodsA retrospective WBE study was carried out at two wastewater treatment plants located in California, United States. Using droplet digital polymerase chain reaction (PCR), we measured concentrations of human adenovirus group F, enteroviruses, norovirus genogroups I and II, and rotavirus nucleic-acids in wastewater solids two times per week for 26 months (n=459 samples) between 2/1/21 and 4/14/23. A novel probe-based PCR assay was developed and validated for adenovirus. We compared viral nucleic-acid concentrations to positivity rates for viral infections from clinical specimens submitted to a local clinical laboratory to assess concordance between the data sets. FindingsWe detected all viral targets in wastewater solids. At both wastewater treatment plants, human adenovirus group F and norovirus GII nucleic-acids were detected at the highest concentrations (median concentrations greater than 105 cp/g), while rotavirus RNA was detected at the lowest concentrations (median on the order of 103 cp/g). Rotavirus, adenovirus group F, and norovirus nucleic-acid concentrations were positivity associated with clinical specimen positivity rates. Concentrations of tested viral nucleic-acids exhibited complex associations with SARS-CoV-2 and other respiratory viral nucleic-acids in wastewater, suggesting divergent transmission patterns. InterpretationThis study provides evidence for the use of wastewater solids for the sensitive detection of enteric virus targets in WBE programs aimed to better understand the spread of enteric disease at a localized, community level without limitations associated with testing many individuals. Wastewater data can inform clinical, public health, and individual decision making aimed to reduce transmission of enteric disease.

Evaluating drug use within populations in the United States poses significant challenges due to various social, ethical, and legal constraints, often impeding the collection of accurate and timely data. Here, we aimed to overcome these barriers by conducting a comprehensive analysis of drug consumption trends and measuring their association with socioeconomic and demographic factors. From May 2022 to April 2023, we analyzed 208 wastewater samples from eight sampling locations across six wastewater treatment plants in Southern Nevada, covering a population of 2.4 million residents with 50 million annual tourists. Using bi-weekly influent wastewater samples, we employed mass spectrometry to detect 39 analytes, including pharmaceuticals and personal care products (PPCPs) and high risk substances (HRS). Our results revealed a significant increase over time in the level of stimulants such as cocaine (pFDR=1.40x10-10) and opioids, particularly norfentanyl (pFDR =1.66x10-12), while PPCPs exhibited seasonal variation such as peak usage of DEET, an active ingredient in insect repellents, during the summer (pFDR =0.05). Wastewater from socioeconomically disadvantaged or rural areas, as determined by Area Deprivation Index (ADI) and Rural-Urban Commuting Area Codes (RUCA) scores, demonstrated distinct overall usage patterns, such as higher usage/concentration of HRS, including cocaine (p=0.05) and norfentanyl (p=1.64x10-5). Our approach offers a near real-time, comprehensive tool to assess drug consumption and personal care product usage at a community level, linking wastewater patterns to socioeconomic and demographic factors. This approach has the potential to significantly enhance public health monitoring strategies in the United States.

BackgroundMetagenomic sequencing of wastewater (W-MGS) can in principle detect any known or novel pathogen in a population. We quantify the sensitivity and cost of W-MGS for viral pathogen detection by jointly analysing W-MGS and epidemiological data for a range of human-infecting viruses. MethodsSequencing data from four studies were analysed to estimate the relative abundance (RA) of 11 human-infecting viruses. Corresponding prevalence and incidence estimates were obtained or calculated from academic and public-health reports. These estimates were combined using a hierarchical Bayesian model to predict RA at set prevalence or incidence values, allowing comparison across studies and viruses. These predictions were then used to estimate the sequencing depth and concomitant cost required for pathogen detection using W-MGS with or without use of a hybridization-capture enrichment panel. FindingsAfter controlling for variation in local infection rates, relative abundance varied by orders of magnitude across studies for a given virus. For instance, a local SARS-CoV-2 weekly incidence of 1% corresponds to predicted SARS-CoV-2 relative abundance ranging from 3.8 x 10-10 to 2.4 x 10-7 across studies, translating to orders-of-magnitude variation in the cost of operating a system able to detect a SARS-CoV-2-like pathogen at a given sensitivity. Use of a respiratory virus enrichment panel in two studies dramatically increased predicted relative abundance of SARS-CoV-2, lowering yearly costs by 24-to 29-fold for a system able to detect a SARS-CoV-2-like pathogen before reaching 0.01% cumulative incidence. InterpretationThe large variation in viral relative abundance after controlling for epidemiological factors indicates that other sources of inter-study variation, such as differences in sewershed hydrology and lab protocols, have a substantial impact on the sensitivity and cost of W-MGS. Well-chosen hybridization capture panels can dramatically increase sensitivity and reduce cost for viruses in the panel, but may reduce sensitivity to unknown or unexpected pathogens. FundingWellcome Trust; Open Philanthropy; Musk Foundation Research In ContextO_ST_ABSEvidence before this studyC_ST_ABSNumerous other studies have performed wastewater metagenomic sequencing (W-MGS), with a range of objectives. However, few have explicitly examined the performance of W-MGS as a monitoring tool. We searched PubMed between database inception and September 2024, using the search terms "MGS OR Metagenomic sequencing OR Metagenomics OR Shotgun sequencing" AND "Performance OR Precision OR Sensitivity OR Cost-effectiveness OR Effectiveness" AND "Virus OR Viral OR Virome" AND "Wastewater OR Sewage". Among the 88 resulting studies, 17 focused specifically on viruses in wastewater. A 2023 UK study by Child and colleagues assessed hybridization-capture and untargeted sequencing of wastewater for genomic epidemiology, concluding that the former but not the latter provided sufficient coverage for effective variant tracking. However, they did find untargeted sequencing sufficient for presence/absence calls of human pathogens in wastewater, a finding supported by numerous other W-MGS studies. While several studies examined the effect of different W-MGS protocols on viral abundance and composition, none accounted for epidemiological or study effects, and none explicitly quantified the sensitivity and cost of W-MGS for viral detection. Added value of this studyTo our knowledge, this study provides the first quantitative assessment of the sensitivity and cost of untargeted and hybridization-capture W-MGS for pathogen surveillance. Linking a large corpus of public wastewater metagenomic sequencing with epidemiological data in a Bayesian model, we predict pathogen relative abundance in W-MGS data at set infection prevalence or incidence, and estimate concomitant read-depth and cost requirements for effective detection across different studies and viruses. Our flexible modelling framework provides a valuable tool for evaluation of sequencing-based surveillance in other contexts. Implications of all the available evidenceThe sensitivity of untargeted W-MGS varies greatly with pathogen and study design, and large gaps in our understanding remain for pathogens not present in our data. As untargeted W-MGS protocols undergo further improvements, our Bayesian modelling framework is an effective tool for evaluating the sensitivity of new protocols under different epidemiological conditions. While less pathogen-agnostic, hybridization capture can dramatically increase the sensitivity of W-MGS-based pathogen monitoring, and our findings support piloting it as a tool for biosurveillance of known viruses.