Racial residential segregation shapes the relationship between early childhood lead exposure and fourth-grade standardized test scores.

Racial/ethnic disparities in academic performance may result from a confluence of adverse exposures that arise from structural racism and accrue to specific subpopulations. This study investigates childhood lead exposure, racial residential segregation, and early educational outcomes. Geocoded North Carolina birth data is linked to blood lead surveillance data and fourth-grade standardized test scores (n = 25,699). We constructed a census tract-level measure of racial isolation (RI) of the non-Hispanic Black (NHB) population. We fit generalized additive models of reading and mathematics test scores regressed on individual-level blood lead level (BLL) and neighborhood RI of NHB (RINHB). Models included an interaction term between BLL and RINHB. BLL and RINHB were associated with lower reading scores; among NHB children, an interaction was observed between BLL and RINHB. Reading scores for NHB children with BLLs of 1 to 3 µg/dL were similar across the range of RINHB values. For NHB children with BLLs of 4 µg/dL, reading scores were similar to those of NHB children with BLLs of 1 to 3 µg/dL at lower RINHB values (less racial isolation/segregation). At higher RINHB levels (greater racial isolation/segregation), children with BLLs of 4 µg/dL had lower reading scores than children with BLLs of 1 to 3 µg/dL. This pattern becomes more marked at higher BLLs. Higher BLL was associated with lower mathematics test scores among NHB and non-Hispanic White (NHW) children, but there was no evidence of an interaction. In conclusion, NHB children with high BLLs residing in high RINHB neighborhoods had worse reading scores.

Wastewater Target Pathogens of Public Health Importance for Expanded Sampling, Houston, Texas, USA.

Building on the success of initiatives put forth during the COVID-19 pandemic response, US health officials are expanding wastewater surveillance programs to track other target pathogens and diseases of public health interest. The Houston Health Department in Houston, Texas, USA, conducted a hypothesis-generating study whereby infectious disease subject matter experts suggested potential targets. This study addressed 2 criteria recommended by the National Academies of Sciences, Engineering, and Medicine for selecting wastewater targets. Results can be used as a basis of a questionnaire for a future population-based study to recommend targets of highest priority to include for expanded wastewater sampling.

Assessment of Public Health Agency and Utility Training Needs for CDC National Wastewater Surveillance System Jurisdictions in the United States.

Since the start of the COVID-19 pandemic, wastewater surveillance hasemerged as a critical tool for tracking the spread of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus and other pathogens in communities throughout the United States. In 2020, the Centers for Disease Control and Prevention (CDC) launched the National Wastewater Surveillance System (NWSS), which partners with state, local, tribal, and territorial health departments to develop and implement wastewater collection and analysis systems and to share data. In 2022, the CDC established the first two NWSS Centers of Excellence to lead its implementation and coordination efforts-one in Colorado (Colorado CoE) and one in Houston (Houston CoE). As the NWSS expands, it is becoming more important to support the training needs of jurisdictions at different stages of developing their wastewater surveillance infrastructure. To evaluate these needs, the Colorado CoE and Houston CoE conducted a needs assessment study of NWSS-funded public health agencies and public utilities departments located in the United States using surveys developed by the Colorado CoE. The results of the surveys showed that although some training needs were universal, it will be most beneficial to develop training modules tailored to the needs of entities that operate wastewater surveillance programs of various sizes, workforce experience levels, and at different stages in the infrastructure development process.

Denoising Non-Stationary Signals via Dynamic Multivariate Complex Wavelet Thresholding.

Over the past few years, we have seen an increased need to analyze the dynamically changing behaviors of economic and financial time series. These needs have led to significant demand for methods that denoise non-stationary time series across time and for specific investment horizons (scales) and localized windows (blocks) of time. Wavelets have long been known to decompose non-stationary time series into their different components or scale pieces. Recent methods satisfying this demand first decompose the non-stationary time series using wavelet techniques and then apply a thresholding method to separate and capture the signal and noise components of the series. Traditionally, wavelet thresholding methods rely on the discrete wavelet transform (DWT), which is a static thresholding technique that may not capture the time series of the estimated variance in the additive noise process. We introduce a novel continuous wavelet transform (CWT) dynamically optimized multivariate thresholding method (WaveL2E). Applying this method, we are simultaneously able to separate and capture the signal and noise components while estimating the dynamic noise variance. Our method shows improved results when compared to well-known methods, especially for high-frequency signal-rich time series, typically observed in finance.

Economic and mental health impacts of multiple adverse events: Hurricane Harvey, other flooding events, and the COVID-19 pandemic.

OBJECTIVES: To assess the economic and mental health impacts of COVID-19 in the presence of previous exposure to flooding events. METHODS: Starting in April 2018, the Texas Flood Registry (TFR) invited residents to complete an online survey regarding their experiences with Hurricane Harvey and subsequent flooding events. Starting in April 2020, participants nationwide were invited to complete a brief online survey on their experiences during the pandemic. This study includes participants in the TFR (N = 20,754) and the COVID-19 Registry (N = 8568) through October 2020 (joint N = 2929). Logistic regression and generalized estimating equations were used to examine the relationship between exposure to flooding events and the economic and mental health impacts of COVID-19. RESULTS: Among COVID-19 registrants, 21% experienced moderate to severe anxiety during the pandemic, and 7% and 12% of households had difficulty paying rent and bills, respectively. Approximately 17% of Black and 15% of Hispanic households had difficulty paying rent, compared to 5% of non-Hispanic white households. The odds of COVID-19 income loss are 1.20 (1.02, 1.40) times higher for those who previously had storm-related home damage compared to those who did not and 3.84 (3.25-4.55) times higher for those who experienced Harvey income loss compared to those who did not. For registrants for whom Harvey was a severe impact event, the odds of having more severe anxiety during the pandemic are 5.14 (4.02, 6.58) times higher than among registrants for whom Harvey was a no meaningful impact event. CONCLUSIONS: Multiple crises can jointly and cumulatively shape health and wellbeing outcomes. This knowledge can help craft emergency preparation and intervention programs.

Population-Based Estimates of SARS-CoV-2 Seroprevalence in Houston, Texas as of September 2020.

BACKGROUND: In contrast to studies that relied on volunteers or convenience sampling, there are few population-based severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) seroprevalence investigations and most were conducted early in the pandemic. The health department of the fourth largest US city recognized that sound estimates of viral impact were needed to inform decision making. METHODS: Adapting standardized disaster research methodology, in September 2020 the city was divided into high and low strata based on reverse-transcriptase polymerase chain reaction (RT-PCR) positivity rates; census block groups within each stratum were randomly selected with probability proportional to size, followed by random selection of households within each group. Using 2 immunoassays, the proportion of infected individuals was estimated for the city, by positivity rate and sociodemographic and other characteristics. The degree of underascertainment of seroprevalence was estimated based on RT-PCR-positive cases. RESULTS: Seroprevalence was estimated to be 14% with near 2-fold difference in areas with high (18%) versus low (10%) RT-PCR positivity rates and was 4 times higher compared to case-based surveillance data. CONCLUSIONS: Seroprevalence was higher than previously reported and greater than estimated from RT-PCR data. Results will be used to inform public health decisions about testing, outreach, and vaccine rollout.

Bayesian variable selection for understanding mixtures in environmental exposures.

Social and environmental stressors are crucial factors in child development. However, there exists a multitude of measurable social and environmental factors-the effects of which may be cumulative, interactive, or null. Using a comprehensive cohort of children in North Carolina, we study the impact of social and environmental variables on 4th end-of-grade exam scores in reading and mathematics. To identify the essential factors that predict these educational outcomes, we design new tools for Bayesian linear variable selection using decision analysis. We extract a predictive optimal subset of explanatory variables by coupling a loss function with a novel model-based penalization scheme, which leads to coherent Bayesian decision analysis and empirically improves variable selection, estimation, and prediction on simulated data. The Bayesian linear model propagates uncertainty quantification to all predictive evaluations, which is important for interpretable and robust model comparisons. These predictive comparisons are conducted out-of-sample with a customized approximation algorithm that avoids computationally intensive model refitting. We apply our variable selection techniques to identify the joint collection of social and environmental stressors-and their interactions-that offer clear and quantifiable improvements in prediction of reading and mathematics exam scores.

Effectiveness of the Dual Dispatch to Cardiac Arrest Policy in Houston, Texas.

CONTEXT: Houston policy is to dual dispatch medically trained firefighters, in addition to emergency medical services (EMS) units to out-of-hospital cardiac arrest (OHCA) cases. While believed to improve public health outcomes, no research exists supporting the policy that when firefighters respond before a better-equipped EMS unit, they increase the probability of survival. OBJECTIVE: To inform EMS policy decisions regarding the effectiveness of dual dispatch by determining the impact of medically trained firefighter dispatch on return of spontaneous circulation (ROSC), a measure of survivability, in OHCA 911 calls while controlling for the subsequent arrival of an EMS unit. DESIGN: This retrospective study uses logistic regression to determine the association between ROSC and response time for fire apparatus first responders controlling for arrival of the EMS unit. SETTING: Out-of-hospital cardiac arrest cases in Houston between May 2008 and April 2013 when dual dispatch was used. PARTICIPANTS: A total of 6961 OHCA cases with the complete data needed for the analysis. MAIN OUTCOME MEASURES: Logistic regression of the dependence of OHCA survival using the indicator ROSC, as related to the fire first responder response times controlling for subsequent arrival of the EMS. RESULTS: Fire apparatus arrived first in 46.7% of cases, a median value of 1.5 minutes before an EMS unit. Controlling for subsequent arrival time of EMS has no effect on ROSC achieved by the fire first responder. If the firefighters had not responded, the resulting 1.5-minute increase in response time equates to a decrease in probability of attaining ROSC of 20.1% for cases regardless of presenting heart rhythm and a 47.7% decrease for ventricular fibrillation cases in which bystander cardiopulmonary resuscitation was initiated. CONCLUSIONS: The firefighter first responder not only improved response time but also greatly increased survivability independent of the arrival time of the better-equipped EMS unit, validating the public health benefit of the dual dispatch policy in Houston.

A case-crossover analysis of out-of-hospital cardiac arrest and air pollution.

BACKGROUND: Evidence of an association between the exposure to air pollution and overall cardiovascular morbidity and mortality is increasingly found in the literature. However, results from studies of the association between acute air pollution exposure and risk of out-of-hospital cardiac arrest (OHCA) are inconsistent for fine particulate matter, and, although pathophysiological evidence indicates a plausible link between OHCA and ozone, none has been reported. Approximately 300 000 persons in the United States experience an OHCA each year, of which >90% die. Understanding the association provides important information to protect public health. METHODS AND RESULTS: The association between OHCA and air pollution concentrations hours and days before onset was assessed by using a time-stratified case-crossover design using 11 677 emergency medical service-logged OHCA events between 2004 and 2011 in Houston, Texas. Air pollution concentrations were obtained from an extensive area monitor network. An average increase of 6 µg/m(3) in fine particulate matter 2 days before onset was associated with an increased risk of OHCA (1.046; 95% confidence interval, 1.012-1.082). A 20-ppb ozone increase for the 8-hour average daily maximum was associated with an increased risk of OHCA on the day of the event (1.039; 95% confidence interval, 1.005-1.073). Each 20-ppb increase in ozone in the previous 1 to 3 hours was associated with an increased risk of OHCA (1.044; 95% confidence interval, 1.004-1.085). Relative risk estimates were higher for men, blacks, or those aged >65 years. CONCLUSIONS: The findings confirm the link between OHCA and fine particulate matter and introduce evidence of a similar link with ozone.

Using community level strategies to reduce asthma attacks triggered by outdoor air pollution: a case crossover analysis.

BACKGROUND: Evidence indicates that asthma attacks can be triggered by exposure to ambient air pollutants, however, detailed pollution information is missing from asthma action plans. Asthma is commonly associated with four criteria pollutants with standards derived by the United States Environmental Protection Agency. Since multiple pollutants trigger attacks and risks depend upon city-specific mixtures of pollutants, there is lack of specific guidance to reduce exposure. Until multi-pollutant statistical modeling fully addresses this gap, some guidance on pollutant attack risk is required. This study examines the risks from exposure to the asthma-related pollutants in a large metropolitan city and defines the city-specific association between attacks and pollutant mixtures. Our goal is that city-specific pollution risks be incorporated into individual asthma action plans as additional guidance to prevent attacks. METHODS: Case-crossover analysis and conditional logistic regression were used to measure the association between ozone, fine particulate matter, nitrogen dioxide, sulfur dioxide and carbon monoxide pollution and 11,754 emergency medical service ambulance treated asthma attacks in Houston, Texas from 2004-2011. Both single and multi-pollutant models are presented. RESULTS: In Houston, ozone and nitrogen dioxide are important triggers (RR = 1.05; 95% CI: 1.00, 1.09), (RR = 1.10; 95% CI: 1.05, 1.15) with 20 and 8 ppb increase in ozone and nitrogen dioxide, respectively, in a multi-pollutant model. Both pollutants are simultaneously high at certain times of the year. The risk attributed to these pollutants differs when they are considered together, especially as concentrations increase. Cumulative exposure for ozone (0-2 day lag) is of concern, whereas for nitrogen dioxide the concern is with single day exposure. Persons at highest risk are aged 46-66, African Americans, and males. CONCLUSIONS: Accounting for cumulative and concomitant outdoor pollutant exposure is important to effectively attribute risk for triggering of an asthma attack, especially as concentrations increase. Improved asthma action plans for Houston individuals should warn of these pollutants, their trends, correlation and cumulative effects. Our Houston based study identifies nitrogen dioxide levels and the three-day exposure to ozone to be of concern whereas current single pollutant based national standards do not.

Spatial Variability in Relationships between Early Childhood Lead Exposure and Standardized Test Scores in Fourth Grade North Carolina Public School Students (2013-2016).

BACKGROUND: Exposure to lead during childhood is detrimental to children's health. The extent to which the association between lead exposure and elementary school academic outcomes varies across geography is not known. OBJECTIVE: Estimate associations between blood lead levels (BLLs) and fourth grade standardized test scores in reading and mathematics in North Carolina using models that allow associations between BLL and test scores to vary spatially across communities. METHODS: We link geocoded, individual-level, standardized test score data for North Carolina public school students in fourth grade (2013-2016) with detailed birth records and blood lead testing data retrieved from the North Carolina childhood blood lead state registry on samples typically collected at 1-6 y of age. BLLs were categorized as: 1µg/dL (reference), 2µg/dL, 3-4µg/dL and ≥5µg/dL. We then fit spatially varying coefficient models that incorporate information sharing (smoothness), across neighboring communities via a Gaussian Markov random field to provide a global estimate of the association between BLL and test scores, as well as census tract-specific estimates (i.e., spatial coefficients). Models adjusted for maternal- and child-level covariates and were fit separately for reading and math. RESULTS: The average BLL across the 91,706 individuals in the analysis dataset was 2.84µg/dL. Individuals were distributed across 2,002 (out of 2,195) census tracts in North Carolina. In models adjusting for child sex, birth weight percentile for gestational age, and Medicaid participation as well as maternal race/ethnicity, educational attainment, marital status, and tobacco use, BLLs of 2µg/dL, 3-4µg/dL and ≥5µg/dL were associated with overall lower reading test scores of -0.28 [95% confidence interval (CI): -0.43, -0.12], -0.53 (-0.69, -0.38), and -0.79 (-0.99, -0.604), respectively. For BLLs of 1µg/dL, 2µg/dL, 3-4µg/dL and ≥5µg/dL, spatial coefficients-that is, tract-specific adjustments in reading test score relative to the "global" coefficient-ranged from -9.70 to 2.52, -3.19 to 3.90, -11.14 to 7.85, and -4.73 to 4.33, respectively. Results for mathematics were similar to those for reading. CONCLUSION: The association between lead exposure and reading and mathematics test scores exhibits considerable heterogeneity across North Carolina communities. These results emphasize the need for prevention and mitigation efforts with respect to lead exposures everywhere, with special attention to locations where the cognitive impact is elevated. https://doi.org/10.1289/EHP13898.

PreK-12 school and citywide wastewater monitoring of the enteric viruses astrovirus, rotavirus, and sapovirus.

Wastewater monitoring is an efficient and effective way to surveil for various pathogens in communities. This is especially beneficial in areas of high transmission, such as preK-12 schools, where infections may otherwise go unreported. In this work, we apply wastewater disease surveillance using school and community wastewater from across Houston, Texas to monitor three major enteric viruses: astrovirus, sapovirus genogroup GI, and group A rotavirus. We present the results of a 10-week study that included the analysis of 164 wastewater samples for astrovirus, rotavirus, and sapovirus in 10 preK-12 schools, 6 wastewater treatment plants, and 2 lift stations using newly designed RT-ddPCR assays. We show that the RT-ddPCR assays were able to detect astrovirus, rotavirus, and sapovirus in school, lift station, and wastewater treatment plant (WWTP) wastewater, and that a positive detection of a virus in a school sample was paired with a positive detection of the same virus at a downstream lift station or wastewater treatment plant over 97 % of the time. Additionally, we show how wastewater detections of rotavirus in schools and WWTPs were significantly associated with citywide viral intestinal infections. School wastewater can play a role in the monitoring of enteric viruses and in the detection of outbreaks, potentially allowing public health officials to quickly implement mitigation strategies to prevent viral spread into surrounding communities.

A Novel Framework for Internal Responses to Detection of Pathogens in Wastewater by Public Health Agencies.

OBJECTIVES: To build on the success of wastewater surveillance during the COVID-19 pandemic, jurisdictions funded under the Centers for Disease Control and Prevention National Wastewater Surveillance System are looking to expand their wastewater programs to detect more pathogens. However, many public health agencies do not know how to use the collected wastewater data to formulate public health responses, underscoring a need for guidance. To address this knowledge gap, the Houston Health Department (HHD) developed a novel response framework that outlines an internal action plan that is tailored by pathogen type after detection of various pathogens in wastewater. MATERIALS AND METHODS: In July 2023, HHD met with subject matter experts (eg, bureau chiefs, program managers) in internal departments, including epidemiology, immunization, and health education, to discuss the general outline of the response framework and each department's anticipated role after pathogen detection. RESULTS: The internal framework established a flow for notifications and the actions to be taken by departments in HHD, with the goals of (1) ensuring timely and efficient responses to pathogen detections, (2) creating accountability within departments for taking their assigned actions, and (3) making certain that HHD was prepared for intervention implementation when a new pathogen was detected. PRACTICE IMPLICATIONS: As more public health agencies expand their wastewater surveillance programs to target additional pathogens, development of internal action plans tailored to departmental capacity and programs is an important step for public health agencies. The information compiled in this response framework can be a model for other public health agencies to adopt when expanding the scope of their wastewater monitoring systems.

Implementing an Alert System for Communicating Actionable Wastewater Surveillance Results to School Communities, Houston, Texas, 2023-2024.

In 2020, the Houston Health Department (HHD) in Texas launched a citywide wastewater surveillance program, including a pilot program that monitored manholes at schools in Houston's largest school district (prekindergarten-12th grade). By 2022, the pilot program monitored wastewater for SARS-CoV-2, influenza A and B, and respiratory syncytial virus. To ensure effective communication of wastewater surveillance results to school communities, HHD designed and implemented a text- and email-based alert system using existing City of Houston resources. This alert program informs recipients about the presence of a virus at their schools and actions to protect themselves and others against that virus. To promote alert program sign-ups, a dedicated bilingual community involvement coordinator conducted in-person outreach geared toward school nurses and student caregivers. From September 2023 through February 2024, a combined 5178 alerts for 43 schools were sent following virus detections. As a supplemental initiative, HHD offered vaccination events to pilot program schools with consistent virus detection. As wastewater surveillance becomes more common across the United States, this alert program presents a framework for other public health agencies to scale and adapt according to their resources.

Online trend estimation and detection of trend deviations in sub-sewershed time series of SARS-CoV-2 RNA measured in wastewater.

Wastewater surveillance has proven a cost-effective key public health tool to understand a wide range of community health diseases and has been a strong source of information on community levels and spread for health departments throughout the SARS- CoV-2 pandemic. Studies spanning the globe demonstrate the strong association between virus levels observed in wastewater and quality clinical case information of the population served by the sewershed. Few of these studies incorporate the temporal dependence present in sampling over time, which can lead to estimation issues which in turn impact conclusions. We contribute to the literature for this important public health science by putting forward time series methods coupled with statistical process control that (1) capture the evolving trend of a disease in the population; (2) separate the uncertainty in the population disease trend from the uncertainty due to sampling and measurement; and (3) support comparison of sub-sewershed population disease dynamics with those of the population represented by the larger downstream treatment plant. Our statistical methods incorporate the fact that measurements are over time, ensuring correct statistical conclusions. We provide a retrospective example of how sub-sewersheds virus levels compare to the upstream wastewater treatment plant virus levels. An on-line algorithm supports real-time statistical assessment of deviations of virus level in a population represented by a sub-sewershed to the virus level in the corresponding larger downstream wastewater treatment plant. This information supports public health decisions by spotlighting segments of the population where outbreaks may be occurring.

Citywide wastewater SARS-CoV-2 levels strongly correlated with multiple disease surveillance indicators and outcomes over three COVID-19 waves.

Public health surveillance systems for COVID-19 are multifaceted and include multiple indicators reflective of different aspects of the burden and spread of the disease in a community. With the emergence of wastewater disease surveillance as a powerful tool to track infection dynamics of SARS-CoV-2, there is a need to integrate and validate wastewater information with existing disease surveillance systems and demonstrate how it can be used as a routine surveillance tool. A first step toward integration is showing how it relates to other disease surveillance indicators and outcomes, such as case positivity rates, syndromic surveillance data, and hospital bed use rates. Here, we present an 86-week long surveillance study that covers three major COVID-19 surges. City-wide SARS-CoV-2 RNA viral loads in wastewater were measured across 39 wastewater treatment plants and compared to other disease metrics for the city of Houston, TX. We show that wastewater levels are strongly correlated with positivity rate, syndromic surveillance rates of COVID-19 visits, and COVID-19-related general bed use rates at hospitals. We show that the relative timing of wastewater relative to each indicator shifted across the pandemic, likely due to a multitude of factors including testing availability, health-seeking behavior, and changes in viral variants. Next, we show that individual WWTPs led city-wide changes in SARS-CoV-2 viral loads, indicating a distributed monitoring system could be used to enhance the early-warning capability of a wastewater monitoring system. Finally, we describe how the results were used in real-time to inform public health response and resource allocation.

Enabling accurate and early detection of recently emerged SARS-CoV-2 variants of concern in wastewater.

As clinical testing declines, wastewater monitoring can provide crucial surveillance on the emergence of SARS-CoV-2 variant of concerns (VoCs) in communities. In this paper we present QuaID, a novel bioinformatics tool for VoC detection based on quasi-unique mutations. The benefits of QuaID are three-fold: (i) provides up to 3-week earlier VoC detection, (ii) accurate VoC detection (>95% precision on simulated benchmarks), and (iii) leverages all mutational signatures (including insertions & deletions).

Wastewater surveillance of SARS-CoV-2 and influenza in preK-12 schools shows school, community, and citywide infections.

Wastewater surveillance is a passive and efficient way to monitor the spread of infectious diseases in large populations and high transmission areas such as preK-12 schools. Infections caused by respiratory viruses in school-aged children are likely underreported, particularly because many children may be asymptomatic or mildly symptomatic. Wastewater monitoring of SARS-CoV-2 has been studied extensively and primarily by sampling at centralized wastewater treatment plants, and there are limited studies on SARS-CoV-2 in preK-12 school wastewater. Similarly, wastewater detections of influenza have only been reported in wastewater treatment plant and university manhole samples. Here, we present the results of a 17-month wastewater monitoring program for SARS-CoV-2 (n = 2176 samples) and influenza A and B (n = 1217 samples) in 51 preK-12 schools. We show that school wastewater concentrations of SARS-CoV-2 RNA were strongly associated with COVID-19 cases in schools and community positivity rates, and that influenza detections in school wastewater were significantly associated with citywide influenza diagnosis rates. Results were communicated back to schools and local communities to enable mitigation strategies to stop the spread, and direct resources such as testing and vaccination clinics. This study demonstrates that school wastewater surveillance is reflective of local infections at several population levels and plays a crucial role in the detection and mitigation of outbreaks.

Public Health Interventions Guided by Houston's Wastewater Surveillance Program During the COVID-19 Pandemic.

Since the start of the COVID-19 pandemic, wastewater surveillance has emerged as a powerful tool used by public health authorities to track SARS-CoV-2 infections in communities. In May 2020, the Houston Health Department began working with a coalition of municipal and academic partners to develop a wastewater monitoring and reporting system for the city of Houston, Texas. Data collected from the system are integrated with other COVID-19 surveillance data and communicated through different channels to local authorities and the general public. This information is used to shape policies and inform actions to mitigate and prevent the spread of COVID-19 at municipal, institutional, and individual levels. Based on the success of this monitoring and reporting system to drive public health protection efforts, the wastewater surveillance program is likely to become a standard part of the public health toolkit for responding to infectious diseases and, potentially, other disease-causing outbreaks.