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.

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.

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.