Application of wastewater-based surveillance and copula time-series model for COVID-19 forecasts.

The objective of this study was to develop a novel copula-based time series (CTS) model to forecast COVID-19 cases and trends based on wastewater SARS-CoV-2 viral load and clinical variables. Wastewater samples were collected from wastewater pumping stations in five sewersheds in the City of Chesapeake VA. Wastewater SARS-CoV-2 viral load was measured using reverse transcription droplet digital PCR (RT-ddPCR). The clinical dataset included daily COVID-19 reported cases, hospitalization cases, and death cases. The CTS model development included two steps: an autoregressive moving average (ARMA) model for time series analysis (step I), and an integration of ARMA and a copula function for marginal regression analysis (step II). Poisson and negative binomial marginal probability densities for copula functions were used to determine the forecasting capacity of the CTS model for COVID-19 forecasts in the same geographical area. The dynamic trends predicted by the CTS model were well suited to the trend of the reported cases as the forecasted cases from the CTS model fell within the 99 % confidence interval of the reported cases. Wastewater SARS CoV-2 viral load served as a reliable predictor for forecasting COVID-19 cases. The CTS model provided robust modeling to predict COVID-19 cases.

Non-coding RNAs derived from the foot-and-mouth disease virus genome trigger broad antiviral activity against coronaviruses.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of a potentially severe respiratory disease, the coronavirus disease 2019 (COVID-19), an ongoing pandemic with limited therapeutic options. Here, we assessed the anti-coronavirus activity of synthetic RNAs mimicking specific domains in the non-coding regions of the foot-and-mouth disease virus (FMDV) genome (ncRNAs). These molecules are known to exert broad-spectrum antiviral activity in cell culture, mice and pigs effectively triggering the host innate immune response. The ncRNAs showed potent antiviral activity against SARS-CoV-2 after transfection in human intestinal Caco-2 and lung epithelium Calu-3 2B4 cells. When the in vivo efficacy of the FMDV ncRNAs was assessed in K18-hACE2 mice, administration of naked ncRNA before intranasal SARS-CoV-2 infection significantly decreased the viral load and the levels of pro-inflammatory cytokines in the lungs compared with untreated infected mice. The ncRNAs were also highly efficacious when assayed against common human HCoV-229E and porcine transmissible gastroenteritis virus (TGEV) in hepatocyte-derived Huh-7 and swine testis ST cells, respectively. These results are a proof of concept of the pan-coronavirus antiviral activity of the FMDV ncRNAs including human and animal divergent coronaviruses and potentially enhance our ability to fight future emerging variants.

Modeling infection from SARS-CoV-2 wastewater concentrations: promise, limitations, and future directions.

Estimating total infection levels, including unreported and asymptomatic infections, is important for understanding community disease transmission. Wastewater can provide a pooled community sample to estimate total infections that is independent of case reporting biases toward individuals with moderate to severe symptoms and by test-seeking behavior and access. We derive three mechanistic models for estimating community infection levels from wastewater measurements based on a description of the processes that generate SARS-CoV-2 RNA signals in wastewater and accounting for the fecal strength of wastewater through endogenous microbial markers, daily flow, and per-capita wastewater generation estimates. The models are illustrated through two case studies of wastewater data collected during 2020-2021 in Virginia Beach, VA, and Santa Clara County, CA. Median simulated infection levels generally were higher than reported cases, but at times, were lower, suggesting a discrepancy between the reported cases and wastewater data, or inaccurate modeling results. Daily simulated infection estimates showed large ranges, in part due to dependence on highly variable clinical viral fecal shedding data. Overall, the wastewater-based mechanistic models are useful for normalization of wastewater measurements and for understanding wastewater-based surveillance data for public health decision-making but are currently limited by lack of robust SARS-CoV-2 fecal shedding data.

Subsewershed SARS-CoV-2 Wastewater Surveillance and COVID-19 Epidemiology Using Building-Specific Occupancy and Case Data

To evaluate the use of wastewater-based surveillance and epidemiology to monitor and predict SARS-CoV-2 virus trends, over the 2020–2021 academic year we collected wastewater samples twice weekly from 17 manholes across Virginia Tech’s main campus. We used data from external door swipe card readers and student isolation/quarantine status to estimate building-specific occupancy and COVID-19 case counts at a daily resolution. After analyzing 673 wastewater samples using reverse transcription quantitative polymerase chain reaction (RT-qPCR), we reanalyzed 329 samples from isolation and nonisolation dormitories and the campus sewage outflow using reverse transcription digital droplet polymerase chain reaction (RT-ddPCR). Population-adjusted viral copy means from isolation dormitory wastewater were 48% and 66% higher than unadjusted viral copy means for N and E genes (1846/100 mL to 2733/100 mL/100 people and 2312/100 mL to 3828/100 mL/100 people, respectively;n = 46). Prespecified analyses with random-effects Poisson regression and dormitory/cluster-robust standard errors showed that the detection of N and E genes were associated with increases of 85% and 99% in the likelihood of COVID-19 cases 8 days later (incident–rate ratio (IRR) = 1.845, p = 0.013 and IRR = 1.994, p = 0.007, respectively;n = 215), and one-log increases in swipe card normalized viral copies (copies/100 mL/100 people) for N and E were associated with increases of 21% and 27% in the likelihood of observing COVID-19 cases 8 days following sample collection (IRR = 1.206, p < 0.001, n = 211 for N;IRR = 1.265, p < 0.001, n = 211 for E). One-log increases in swipe normalized copies were also associated with 40% and 43% increases in the likelihood of observing COVID-19 cases 5 days after sample collection (IRR = 1.403, p = 0.002, n = 212 for N;IRR = 1.426, p < 0.001, n = 212 for E). Our findings highlight the use of building-specific occupancy data and add to the evidence for the potential of wastewater-based epidemiology to predict COVID-19 trends at subsewershed scales. Wastewater samples, population estimates, and case outcome data from individual buildings can be used to monitor and predict COVID-19 trends.

Excellence Available Everywhere: The Virtual Pathology Grand Rounds Experience.

OBJECTIVES: The goal is to describe the use of a virtual platform in the delivery of Virtual Pathology Grand Rounds (VPGR) and discuss the overall experience from the perspective of hosts, speakers, and participants. METHODS: Zoom was a natural choice for an online format because virtual platforms had been increasingly used to conduct meetings and medical education. VPGR hosted 14 speakers on a variety of topics, including subspecialty anatomic pathology material, digital pathology, molecular pathology, and medical education. RESULTS: There were 221 registrants and 114 participants for the first lecture, reaching a maximum of 1,268 registrants for the 12th lecture and the maximum limit of 300 participants during 3 lectures. Speakers stated that VPGR conveniently provided career-building opportunities through partnerships with host universities and remote attendance. Participants identified a lack of interpersonal communication and technical challenges as downsides. CONCLUSIONS: VPGR serves as strong proof of concept for the feasibility and demand for high-quality, remote academic pathology talks.

Clinical spectrum of COVID-19 and risk factors associated with severity in Spanish children.

We aimed to identify the spectrum of disease in children with COVID-19, and the risk factors for admission in paediatric intensive care units (PICUs). We conducted a multicentre, prospective study of children with SARS-CoV-2 infection in 76 Spanish hospitals. We included children with COVID-19 or multi-inflammatory syndrome (MIS-C) younger than 18 years old, attended during the first year of the pandemic. We enrolled 1200 children. A total of 666 (55.5%) were hospitalised, and 123 (18.4%) required admission to PICU. Most frequent major clinical syndromes in the cohort were mild syndrome (including upper respiratory tract infection and flu-like syndrome, skin or mucosae problems and asymptomatic), 44.8%; bronchopulmonary syndrome (including pneumonia, bronchitis and asthma flare), 18.5%; fever without a source, 16.2%; MIS-C, 10.6%; and gastrointestinal syndrome, 10%. In hospitalised children, the proportions were 28.5%, 25.7%, 16.5%, 19.1% and 10.2%, respectively. Risk factors associated with PICU admission were age in months (OR: 1.007; 95% CI 1.004 to 1.01), MIS-C (OR: 14.4, 95% CI 8.9 to 23.8), chronic cardiac disease (OR: 4.8, 95% CI 1.8 to 13), asthma or recurrent wheezing (OR: 2.5, 95% CI 1.2 to 5.2) and after excluding MIS-C patients, moderate/severe liver disease (OR: 8.6, 95% CI 1.6 to 47.6). However, asthmatic children were admitted into the PICU due to MIS-C or pneumonia, not due to asthma flare.Conclusion: Hospitalised children with COVID-19 usually present as one of five major clinical phenotypes of decreasing severity. Risk factors for PICU include MIS-C, elevation of inflammation biomarkers, asthma, moderate or severe liver disease and cardiac disease. What is Known: • All studies suggest that children are less susceptible to serious SARS-CoV-2 infection when compared to adults. Most studies describe symptoms at presentation. However, it remains unclear how these symptoms group together into clinically identifiable syndromes and the severity associated with them. What is New: • We have gathered the primary diagnoses into five major syndromes of decreasing severity: MIS-C, bronchopulmonary syndrome, gastrointestinal syndrome, fever without a source and mild syndrome. Classification of the children in one of the syndromes is unique and helps to assess the risk of critical illness and to define the spectrum of the disease instead of just describing symptoms and signs.

Standardizing data reporting in the research community to enhance the utility of open data for SARS-CoV-2 wastewater surveillance.

SARS-CoV-2 RNA detection in wastewater is being rapidly developed and adopted as a public health monitoring tool worldwide. With wastewater surveillance programs being implemented across many different scales and by many different stakeholders, it is critical that data collected and shared are accompanied by an appropriate minimal amount of metainformation to enable meaningful interpretation and use of this new information source and intercomparison across datasets. While some databases are being developed for specific surveillance programs locally, regionally, nationally, and internationally, common globally-adopted data standards have not yet been established within the research community. Establishing such standards will require national and international consensus on what metainformation should accompany SARS-CoV-2 wastewater measurements. To establish a recommendation on minimum information to accompany reporting of SARS-CoV-2 occurrence in wastewater for the research community, the United States National Science Foundation (NSF) Research Coordination Network on Wastewater Surveillance for SARS-CoV-2 hosted a workshop in February 2021 with participants from academia, government agencies, private companies, wastewater utilities, public health laboratories, and research institutes. This report presents the primary two outcomes of the workshop: (i) a recommendation on the set of minimum meta-information that is needed to confidently interpret wastewater SARS-CoV-2 data, and (ii) insights from workshop discussions on how to improve standardization of data reporting.

Minimizing errors in RT-PCR detection and quantification of SARS-CoV-2 RNA for wastewater surveillance.

Wastewater surveillance for pathogens using reverse transcription-polymerase chain reaction (RT-PCR) is an effective and resource-efficient tool for gathering community-level public health information, including the incidence of coronavirus disease-19 (COVID-19). Surveillance of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) in wastewater can potentially provide an early warning signal of COVID-19 infections in a community. The capacity of the world's environmental microbiology and virology laboratories for SARS-CoV-2 RNA characterization in wastewater is increasing rapidly. However, there are no standardized protocols or harmonized quality assurance and quality control (QA/QC) procedures for SARS-CoV-2 wastewater surveillance. This paper is a technical review of factors that can cause false-positive and false-negative errors in the surveillance of SARS-CoV-2 RNA in wastewater, culminating in recommended strategies that can be implemented to identify and mitigate some of these errors. Recommendations include stringent QA/QC measures, representative sampling approaches, effective virus concentration and efficient RNA extraction, PCR inhibition assessment, inclusion of sample processing controls, and considerations for RT-PCR assay selection and data interpretation. Clear data interpretation guidelines (e.g., determination of positive and negative samples) are critical, particularly when the incidence of SARS-CoV-2 in wastewater is low. Corrective and confirmatory actions must be in place for inconclusive results or results diverging from current trends (e.g., initial onset or reemergence of COVID-19 in a community). It is also prudent to perform interlaboratory comparisons to ensure results' reliability and interpretability for prospective and retrospective analyses. The strategies that are recommended in this review aim to improve SARS-CoV-2 characterization and detection for wastewater surveillance applications. A silver lining of the COVID-19 pandemic is that the efficacy of wastewater surveillance continues to be demonstrated during this global crisis. In the future, wastewater should also play an important role in the surveillance of a range of other communicable diseases.

Assessing sensitivity and reproducibility of RT-ddPCR and RT-qPCR for the quantification of SARS-CoV-2 in wastewater.

Throughout the COVID-19 global pandemic there has been significant interest and investment in using Wastewater-Based Epidemiology (WBE) for surveillance of viral pathogen presence and infections at the community level. There has been a push for widescale implementation of standardized protocols to quantify viral loads in a range of wastewater systems. To address concerns regarding sensitivity, limits of quantification, and large-scale reproducibility, a comparison of two similar workflows using RT-qPCR and RT-ddPCR was conducted. Sixty raw wastewater influent samples were acquired from nine distinct wastewater treatment plants (WWTP's) served by the Hampton Roads Sanitation District (HRSD, Virginia Beach, Virginia) over a 6-month period beginning March 9th, 2020. Common reagents, controls, master mixes and nucleic acid extracts were shared between two individual processing groups based out of HRSD and the UNC Chapel Hill Institute of Marine Sciences (IMS, Morehead City, North Carolina). Samples were analyzed in parallel using One-Step RT-qPCR and One-Step RT-ddPCR with Nucleocapsid Protein 2 (N2) specific primers and probe. Influent SARS-CoV-2 N2 concentrations steadily increased over time spanning a range from non-detectable to 2.13E + 05 copies/L. Systematic dilution of the extracts indicated that inhibitory components in the wastewater matrices did not significantly impede the detection of a positive N2 signal for either workflow. The RT-ddPCR workflow had a greater analytical sensitivity with a lower Limit of Detection (LOD) at 0.066 copies/µl of template compared to RT-qPCR with a calculated LOD of 12.0 copies/µL of template. Interlaboratory comparisons using non-parametric correlation analysis demonstrated that there was a strong, significant, positive correlation between split extracts when employing RT-ddPCR for analysis with a ρ value of 0.86.

Leveraging Technology for Remote Learning in the Era of COVID-19 and Social Distancing.

The ongoing global pandemic of coronavirus disease 2019 (COVID-19) has rapidly disrupted traditional modes of operation in health care and education. In March 2020, institutions in the United States began to implement a range of policies to discourage direct contact and encourage social distancing. These measures have placed us in an unprecedented position where education can no longer occur at close quarters-most notably, around a multiheaded microscope-but must instead continue at a distance. This guide is intended to be a resource for pathologists and pathologists-in-training who wish to leverage technology to continue collaboration, teaching, and education in this era. The article is focused mainly on anatomic pathology; however, the technologies easily lend themselves to clinical pathology education as well. Our aim is to provide curated lists of various online resources that can be used for virtual learning in pathology, provide tips and tricks, and share our personal experience with these technologies. The lists include videoconferencing platforms; pathology Web sites; free online educational resources, including social media; and whole slide imaging collections. We are currently living through a unique situation without a precedent or guidebook, and we hope that this guide will enable the community of pathology educators worldwide to embrace the opportunities that 21st century technology provides.

COVID-19 surveillance in Southeastern Virginia using wastewater-based epidemiology.

Wastewater-based epidemiology (WBE) has been used to analyze markers in wastewater treatment plant (WWTP) influent to characterize emerging chemicals, drug use patterns, or disease spread within communities. This approach can be particularly helpful in understanding outbreaks of disease like the novel Coronavirus disease-19 (COVID-19) when combined with clinical datasets. In this study, three RT-ddPCR assays (N1, N2, N3) were used to detect severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA in weekly samples from nine WWTPs in southeastern Virginia. In the first several weeks of sampling, SARS-CoV-2 detections were sporadic. Frequency of detections and overall concentrations of RNA within samples increased from mid March into late July. During the twenty-one week study, SARS-CoV-2 concentrations ranged from 101 to 104 copies 100 mL-1 in samples where viral RNA was detected. Fluctuations in population normalized loading rates in several of the WWTP service areas agreed with known outbreaks during the study. Here we propose several ways that data can be presented spatially and temporally to be of greatest use to public health officials. As the COVID-19 pandemic wanes, it is likely that communities will see increased incidence of small, localized outbreaks. In these instances, WBE could be used as a pre-screening tool to better target clinical testing needs in communities with limited resources.

Editorial Perspectives: will SARS-CoV-2 reset public health requirements in the water industry? Integrating lessons of the past and emerging research

Brian Pecson and Daniel Gerrity present an Editorial Perspective which focuses on the impact of SARS-CoV-2 on the water industry.