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.

Direct comparison of RT-ddPCR and targeted amplicon sequencing for SARS-CoV-2 mutation monitoring in wastewater.

Over the course of the COVID-19 pandemic, variants of SARS-CoV-2 have emerged that are more contagious and more likely to cause breakthrough infections. Targeted amplicon sequencing approach is a gold standard for identification and analysis of variants. However, when applied to environmental samples such as wastewater, it remains unclear how sensitive this method is for detecting variant-associated mutations in environmental samples. Here we directly compare a targeted amplicon sequencing approach (using ARTIC v3; hereafter referred to as sequencing) with RT-ddPCR quantification for the detection of five mutations that are characteristic of variants of concern (VoCs) in wastewater samples. In total, 547 wastewater samples were analyzed using both methods in parallel. When we observed positive mutation detections by RT-ddPCR, 42.6% of the detection events were missed by sequencing, due to negative detection or the limited read coverage at the mutation position. Further, when sequencing reported negative or depth-limited mutation detections, 26.7% of those events were instead positive detections by RT-ddPCR, highlighting the relatively poor sensitivity of sequencing. No or weak associations were observed between quantitative measurements of target mutations determined by RT-ddPCR and sequencing. These findings caution the use of quantitative measurements of SARS-CoV-2 variants in wastewater samples determined solely based on sequencing.

Identification of multiple potential viral diseases in a large urban center using wastewater surveillance.

Viruses are linked to a multitude of human illnesses and can disseminate widely in urbanized environments causing global adverse impacts on communities and healthcare infrastructures. Wastewater-based epidemiology was employed using metagenomics and quantitative polymerase chain reaction (qPCR) assays to identify enteric and non-enteric viruses collected from a large urban area for potential public health monitoring and outbreak analysis. Untreated wastewater samples were collected from November 2017 to February 2018 (n = 54) to evaluate the diversity of human viral pathogens in collected samples. Viruses were classified into virus types based on primary transmission routes and reviewed against viral associated diseases reported in the catchment area. Metagenomics detected the presence of viral pathogens that cause clinically significant diseases reported within the study area during the sampling year. Detected viruses belong to the Adenoviridae, Astroviridae, Caliciviridae, Coronaviridae, Flaviviridae, Hepeviridae, Herpesviridae, Matonaviridae, Papillomaviridae, Parvoviridae, Picornaviridae, Poxviridae, Retroviridae, and Togaviridae families. Furthermore, concentrations of adenovirus, norovirus GII, sapovirus, hepatitis A virus, human herpesvirus 6, and human herpesvirus 8 were measured in wastewater samples and compared to metagenomic findings to confirm detected viral genus. Hepatitis A virus obtained the greatest average viral load (1.86 × 107 genome copies/L) in wastewater samples compared to other viruses quantified using qPCR with a 100% detection rate in metagenomic samples. Average concentration of sapovirus (1.36 × 106 genome copies/L) was significantly greater than norovirus GII (2.94 × 104 genome copies/L) indicating a higher burden within the study area. Findings obtained from this study aid in evaluating the utility of wastewater-based epidemiology for identification and routine monitoring of various viruses in large communities. This methodology has the potential to improve public health responses to large scale outbreaks and viral pandemics.