Model training periods impact estimation of COVID-19 incidence from wastewater viral loads.

Wastewater-based epidemiology (WBE) has been deployed broadly as an early warning tool for emerging COVID-19 outbreaks. WBE can inform targeted interventions and identify communities with high transmission, enabling quick and effective responses. As the wastewater (WW) becomes an increasingly important indicator for COVID-19 transmission, more robust methods and metrics are needed to guide public health decision-making. This research aimed to develop and implement a mathematical framework to infer incident cases of COVID-19 from SARS-CoV-2 levels measured in WW. We propose a classification scheme to assess the adequacy of model training periods based on clinical testing rates and assess the sensitivity of model predictions to training periods. A testing period is classified as adequate when the rate of change in testing is greater than the rate of change in cases. We present a Bayesian deconvolution and linear regression model to estimate COVID-19 cases from WW data. The effective reproductive number is estimated from reconstructed cases using WW. The proposed modeling framework was applied to three Northern California communities served by distinct WW treatment plants. The results showed that training periods with adequate testing are essential to provide accurate projections of COVID-19 incidence.

SARS-CoV-2 RNA Is Readily Detectable at Least 8 Months after Shedding in an Isolation Facility.

Environmental monitoring of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) for research and public health purposes has grown exponentially throughout the coronavirus disease 2019 (COVID-19) pandemic. Monitoring wastewater for SARS-CoV-2 provides early warning signals of virus spread and information on trends in infections at a community scale. Indoor environmental monitoring (e.g., swabbing of surfaces and air filters) to identify potential outbreaks is less common, and the evidence for its utility is mixed. A significant challenge with surface and air filter monitoring in this context is the concern of "relic RNA," noninfectious RNA found in the environment that is not from recently deposited virus. Here, we report detection of SARS-CoV-2 RNA on surfaces in an isolation unit (a university dorm room) for up to 8 months after a COVID-19-positive individual vacated the space. Comparison of sequencing results from the same location over two time points indicated the presence of the entire viral genome, and sequence similarity confirmed a single source of the virus. Our findings highlight the need to develop approaches that account for relic RNA in environmental monitoring. IMPORTANCE Environmental monitoring of SARS-CoV-2 is rapidly becoming a key tool in infectious disease research and public health surveillance. Such monitoring offers a complementary and sometimes novel perspective on population-level incidence dynamics relative to that of clinical studies by potentially allowing earlier, broader, more affordable, less biased, and less invasive identification. Environmental monitoring can assist public health officials and others when deploying resources to areas of need and provides information on changes in the pandemic over time. Environmental surveillance of the genetic material of infectious agents (RNA and DNA) in wastewater became widely applied during the COVID-19 pandemic. There has been less research on other types of environmental samples, such as surfaces, which could be used to indicate that someone in a particular space was shedding virus. One challenge with surface surveillance is that the noninfectious genetic material from a pathogen (e.g., RNA from SARS-CoV-2) may be detected in the environment long after an infected individual has left the space. This study aimed to determine how long SARS-CoV-2 RNA could be detected in a room after a COVID-positive person had been housed there.

Wastewater-Based Epidemiology for COVID-19: Handling qPCR Nondetects and Comparing Spatially Granular Wastewater and Clinical Data Trends

Wastewater-based epidemiology (WBE) is a useful complement to clinical testing for managing COVID-19. While community-scale wastewater and clinical data frequently correlate, less is known about subcommunity relationships between the two data types. Moreover, nondetects in qPCR wastewater data are typically handled through methods known to bias results, overlooking perhaps better alternatives. We address these knowledge gaps using data collected from September 2020–June 2021 in Davis, California (USA). We hypothesize that coupling the expectation maximization (EM) algorithm with the Markov Chain Monte Carlo (MCMC) method could improve estimation of “missing” values in wastewater qPCR data. We test this hypothesis by applying EM-MCMC to city wastewater treatment plant data and comparing output to more conventional nondetect handling methods. Dissimilarities in results (i) underscore the importance of specifying nondetect handling method in reporting and (ii) suggest that using EM-MCMC may yield better agreement between community-scale clinical and wastewater data. We also present a novel framework for spatially aligning clinical data with wastewater data collected upstream of a treatment plant (i.e., distributed across a sewershed). Applying the framework to data from Davis reveals reasonable agreement between wastewater and clinical data at highly granular spatial scales—further underscoring the public-health value of WBE. Exploration of novel methods for handling qPCR nondetects and enabling multiscale data comparisons in wastewater-based epidemiology.

The challenge of SARS-CoV-2 environmental monitoring in schools using floors and portable HEPA filtration units: Fresh or relic RNA?

Testing surfaces in school classrooms for the presence of SARS-CoV-2, the virus that causes COVID-19, can provide public-health information that complements clinical testing. We monitored the presence of SARS-CoV-2 RNA in five schools (96 classrooms) in Davis, California (USA) by collecting weekly surface-swab samples from classroom floors and/or portable high-efficiency particulate air (HEPA) units (n = 2,341 swabs). Twenty-two surfaces tested positive, with qPCR cycle threshold (Ct) values ranging from 36.07-38.01. Intermittent repeated positives in a single room were observed for both floor and HEPA filter samples for up to 52 days, even following regular cleaning and HEPA filter replacement after a positive result. We compared the two environmental sampling strategies by testing one floor and two HEPA filter samples in 57 classrooms at Schools D and E. HEPA filter sampling yielded 3.02% and 0.41% positivity rates per filter sample collected for Schools D and E, respectively, while floor sampling yielded 0.48% and 0% positivity rates. Our results indicate that HEPA filter swabs are more sensitive than floor swabs at detecting SARS-CoV-2 RNA in interior spaces. During the study, all schools were offered weekly free COVID-19 clinical testing through Healthy Davis Together (HDT). HDT also offered on-site clinical testing in Schools D and E, and upticks in testing participation were observed following a confirmed positive environmental sample. However, no confirmed COVID-19 cases were identified among students associated with classrooms yielding positive environmental samples. The positive samples detected in this study appeared to contain relic viral RNA from individuals infected before the monitoring program started and/or RNA transported into classrooms via fomites. High-Ct positive results from environmental swabs detected in the absence of known active infections supports this conclusion. Additional research is needed to differentiate between fresh and relic SARS-CoV-2 RNA in environmental samples and to determine what types of results should trigger interventions.

The need of an environmental justice approach for wastewater based epidemiology for rural and disadvantaged communities: A review in California.

Amid the 2019 coronavirus disease pandemic (COVID-19), the scientific community has a responsibility to provide accessible public health resources within their communities. Wastewater based epidemiology (WBE) has been used to monitor community spread of the pandemic. The goal of this review was to evaluate the need for an environmental justice approach for COVID-19 WBE starting with the state of California in the United States. Methods included a review of the peer-reviewed literature, government-provided data, and news stories. As of June 2021, there were twelve universities, nine public dashboards, and 48 of 384 wastewater treatment plants monitoring wastewater for SARS-CoV-2 within California. The majority of wastewater monitoring in California has been conducted in the urban areas of Coastal and Southern California (34/48), with a lack of monitoring in more rural areas of Central (10/48) and Northern California (4/48). Similar to the access to COVID-19 clinical testing and vaccinations, there is a disparity in access to wastewater testing which can often provide an early warning system to outbreaks. This research demonstrates the need for an environmental justice approach and equity considerations when determining locations for environmental monitoring.

Wastewater Surveillance for SARS-CoV-2 on College Campuses: Initial Efforts, Lessons Learned, and Research Needs.

Wastewater surveillance for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an emerging approach to help identify the risk of a coronavirus disease (COVID-19) outbreak. This tool can contribute to public health surveillance at both community (wastewater treatment system) and institutional (e.g., colleges, prisons, and nursing homes) scales. This paper explores the successes, challenges, and lessons learned from initial wastewater surveillance efforts at colleges and university systems to inform future research, development and implementation. We present the experiences of 25 college and university systems in the United States that monitored campus wastewater for SARS-CoV-2 during the fall 2020 academic period. We describe the broad range of approaches, findings, resources, and impacts from these initial efforts. These institutions range in size, social and political geographies, and include both public and private institutions. Our analysis suggests that wastewater monitoring at colleges requires consideration of local information needs, sewage infrastructure, resources for sampling and analysis, college and community dynamics, approaches to interpretation and communication of results, and follow-up actions. Most colleges reported that a learning process of experimentation, evaluation, and adaptation was key to progress. This process requires ongoing collaboration among diverse stakeholders including decision-makers, researchers, faculty, facilities staff, students, and community members.