Exposure to pathogens remains the greatest acute health concern related to potable water reuse applications. Here, we implement high-volume sample concentration for both molecular- and culture-based analyses to evaluate pathogen and surrogate removal through a 1-MGD scale coagulation, flocculation, sedimentation, ozonation, and biofiltration treatment train. The reduction of Cryptosporidium and Giardia was quantified across the wastewater treatment plant and the advanced water treatment (AWT) process. Considering the low influent concentrations, only 2 and 4 total log-reduction values (LRVs) could be demonstrated for Cryptosporidium and Giardia, respectively. Adenovirus, rotavirus, norovirus GI, and norovirus GII concentration and reduction were quantified using droplet digital polymerase chain reaction (PCR) across the AWT process. Average enteric virus LRV, through coagulation/flocculation/sedimentation, ozonation, and biofiltration, was shown to be 1.5, 0.3, and 2 LRV, respectively. Both molecular and culture-based nonpathogenic viral surrogates were shown to be representatives of enteric virus reduction by physical removal treatment processes. Due to the low concentration of indigenous pathogens and surrogates, challenge tests were performed on the pilot scale to evaluate the inactivation and removal of pathogens by ozone and biofiltration. These full-scale monitoring data and pilot challenge testing data provide validation of the pathogen LRV credit claimed in ozone-biofiltration-based AWT, which is necessary to protect public health in reuse scenarios.

Modeling efforts are needed to predict trends in COVID-19 cases and related health outcomes, aiding in the development of management strategies and adaptation measures. This study was conducted to assess whether the SARS-CoV-2 viral load in wastewater could serve as a predictor for forecasting COVID-19 cases, hospitalizations, and deaths using copula-based time series modeling. SARS-CoV-2 RNA load in wastewater in Chesapeake, VA, was measured using the RT-qPCR method. A Gaussian copula time series (CTS) marginal regression model, incorporating an autoregressive moving average model and Gaussian copula function, was used as a forecasting model. Wastewater SARS-CoV-2 viral loads were correlated with COVID-19 cases. The forecasted model with both Poisson and negative binomial marginal distributions yielded trends in COVID-19 cases that closely paralleled the reported cases, with 90% of the forecasted COVID-19 cases falling within the 99% confidence interval of the reported data. However, the model did not effectively forecast the trends and the rising cases of hospital admissions and deaths. The forecasting model was validated for predicting clinical cases and trends with a non-normal distribution in a time series manner. Additionally, the model showed potential for using wastewater SARS-CoV-2 viral load as a predictor for forecasting COVID-19 cases.

Host-associated fecal indicator measurements can be coupled with quantitative microbial risk assessment to develop risk-based thresholds for recreational use of potential sewage-contaminated waters. These assessments require information on the relative concentrations of indicators and pathogens in discharged sewage, typically based on data collected from wastewater treatment plant influent samples. However, most untreated sewage releases occur from within the collection system itself (i.e. compromised sewer laterals, compromised gravity and force mains, sanitary sewer overflows), where these relationships may differ. This study therefore analyzed the concentrations of a selected reference pathogen (norovirus) and fecal indicator (HF183) in sewage samples from upper and lower segments of gravity sewage collection systems, wastewater pumpstations, and the influent and effluent of treatment plants, to characterize variability in their relative concentrations. Norovirus detection rates were lower and more variable in upper collection system samples due to the smaller population represented; whereas, HF183 was routinely detected at all sites with higher concentrations in the collection system compared to treatment plant influent, resulting in variable comparative relationships across sample locations (types). Mean HF183:NoV ratios ranged from 1.0 × 105 for sewer lateral samples to 7 × 10° for force main samples. Results were used to develop risk-based thresholds for HF183 based on estimated recreational exposure to norovirus following a release from each potential sewage source, with higher thresholds for treatment facility influent compared to forced mains, or effluent. Consequently, this approach can allow for the rapid application of potential risk-based thresholds for recreational water quality applications based on different types of sewage discharge events.

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.

Given its mortality benefit, renal transplantation remains the ideal treatment modality for end stage renal disease in children. Despite the recent expansion of use in young children, the novel SARS-CoV-2 vaccine has not been universally accepted. Similarly, vaccine related state regulations are heterogenous. We present a cross-sectional analysis of institutional specific vaccination policies at US pediatric renal transplant centers and relationships to state legislation. We found that 36.1% of institutions require COVID-19 vaccination prior to transplant, while 17 states have current legislation prohibiting proof of vaccination as a means of access to public services. Of the 63.9% of transplant centers without immunization requirement, almost two-thirds are located in states without prohibitory regulations. Despite an unclear primary influence of institutional policy, our study demonstrates a lack of standardization and potential to create unnecessary inequities.

Wastewater surveillance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has proven a practical complement to clinical data for assessing community-scale infection trends. Clinical assays, such as the CDC-promulgated N1, N2, and N3 have been used to detect and quantify viral RNA in wastewater but, to date, have not included estimates of reliability of true positive or true negative. Bayes’ Theorem was applied to estimate Type I and Type II error rates for detections of the virus in wastewater. Conditional probabilities of true positive or true negative were investigated when one assay was used, or multiple assays were run concurrently. Cumulative probability analysis was used to assess the likelihood of true SARS-CoV-2 detection using multiple samples. Results demonstrate highly reliable positive (>0.86 for priors >0.25) and negative (>0.80 for priors = 0.50) results using a single assay. Using N1 and N2 concurrently caused greater reliability (>0.99 for priors <0.05) when results concurred but generated potentially counterintuitive interpretations when results were discordant. Regional wastewater surveillance data was investigated as a means of setting prior probabilities. Probability of true detection with a single marker was investigated using cumulative probability across all combinations of positive and negative results for a set of three samples. Findings using a low (0.11) and uniformed (0.50) initial prior resulted in high probabilities of detection (>0.95) even when a set of samples included one or two negative results, demonstrating the influence of high sensitivity and specificity values. Analyses presented here provide a practical framework for understanding analytical results generated by wastewater surveillance programs.

Collection System Investigation Microbial Source Tracking (CSI-MST) is a novel, sensitive approach for identifying sewer infrastructure deficiencies using molecular markers. This method requires both a detailed understanding of collection and conveyance system infrastructure and quickly turned around molecular data to advise an adaptive, targeted in-pipe approach to detect deficiencies. Here we explain the CSI-MST approach and provide several case study examples of how this approach can be adapted to different scale watersheds to identify potential sewer infrastructure issues. This approach has been used to locate and confirm the remediation of numerous needed infrastructure repairs in the southeastern Virginia region. The selected case studies presented here serve as a proof of concept-this methodology can be adopted by other utilities and municipalities to address necessary wastewater infrastructure repairs in different regions.

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 com-munities. 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 10(1) to 10(4) 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. (c) 2020 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license. (http://creativecommons.org/licenses/by-nc-nd/4.0/)

AbstractThe ongoing COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) requires a significant, coordinated public health response. Assessing case density and spread of infection is critical and relies largely on clinical testing data. However, clinical testing suffers from known limitations, including test availability and a bias towards enumerating only symptomatic individuals. Wastewater-based epidemiology (WBE) has gained widespread support as a potential complement to clinical testing for assessing COVID-19 infections at the community scale. The efficacy of WBE hinges on the ability to accurately characterize SARS-CoV-2 RNA concentrations in wastewater. To date, a variety of sampling schemes have been used without consensus around the appropriateness of grab or composite sampling. Here we address a key WBE knowledge gap by examining the variability of SARS-CoV-2 RNA concentrations in wastewater grab samples collected every 2 hours for 72 hours compared with three corresponding 24-hour flow-weighted composite samples collected over the same period. Results show relatively low variability (respective means for N1, N2, N3 assays = 608, 847.9, 768.4 copies 100 mL-1, standard deviations = 501.4, 500.3, 505.8 copies 100 mL-1) for grab sample concentrations, and good agreement between most grab samples and their respective composite (mean deviation from composite = 159 copies 100 mL-1). When SARS-CoV-2 RNA concentrations are used to calculate viral load (RNA concentration * total influent flow the sample day), the discrepancy between grabs (log10range for all grabs = 11.9) or a grab and its associated 24-hour composite (log10difference = 11.6) are amplified. A similar effect is seen when estimating carrier prevalence in a catchment population with median estimates based on grabs ranging 63-1885 carriers. Findings suggest that grab samples may be sufficient to characterize SARS-CoV-2 RNA concentrations, but additional calculations using these data may be sensitive to grab sample variability and warrant the use of flow-weighted composite sampling. These data inform future WBE work by helping determine the most appropriate sampling scheme and facilitate sharing of datasets between studies via consistent methodology.

From 1991 to 2013, Mississippi was without liver transplant services. In 2013, a new liver transplant program was established at the University of Mississippi Medical Center. Here, we describe our experience with the first 150 transplants over a 4.5-year period. This study is a review of 147 patients who underwent the first 150 liver transplants at the University of Mississippi Medical Center between March 5, 2013, and January 4, 2018. There were no exclusion criteria for this study. Donor, recipient, and outcome variables were analyzed. Recipients were 46% female and 74% white. Age at the time of transplant was 57 [IQR 49-63]. BMI at transplant was 30 [IQR 25-35]. Thirty per cent of transplants were for alcoholic cirrhosis, 25% non-alcoholic steatohepatitis, 24% hepatitis C, and 12% cholestatic. Mean model for end-stage liver disease (MELD) at the time of transplant was 20 [95% confidence interval 19-21] and MELD-Na was 22 [95% confidence interval 20-23]. One-year patient- and graft survival were 89% and 87%, respectively, which were as expected based on Scientific Registry of Transplant Recipient reports after risk adjustment. The data published here verifies it is possible to establish a new liver transplant center in an underserved area previously lacking comprehensive liver care and to achieve results similar to other high-volume centers across the country.

Microbial source tracking can identify waterbodies at risk of contamination using host-associated molecular markers. No assay used for microbial source tracking is both 100% host-specific and sensitive for human or animal fecal contamination. Using literature sensitivity and specificity values, Bayes' Theorem for conditional probability was applied to the human fecal-associated HF183 marker in a microbial source tracking context. Type I and Type II error rates were examined across a range of priors. Conditional probabilities were investigated using two human-associated markers, HF183 and HumM2, concurrently. Cumulative probability analysis was used to explore the likelihood of true contaminant detection using multiple samples. Probability of human fecal contamination was calculated for all combinations of positive and negative marker results given three samples. Results demonstrate the respective influence that specificity and sensitivity values exert on the likelihood of true positive and true negative. Using practical priors, high levels of confidence (99%) in results were observed when HF183 and HumM2 were used concurrently. Cumulative probability analyses showed that multiple samples from a single location can provide a >95% level of confidence in positive and negative results, suggesting that when multiple samples are necessary to account for in situ variability, a single marker can yield sufficiently reliable results.

It is widely understood that stormwater drainage has a significant impact on the health of tidal creek systems via regular inputs of runoff from the surrounding watershed. Due to this hydrologic connection, contamination of the upstream drainage basin will have a direct effect on estuaries and tidal creeks that often act as receiving waters. This study focuses on the importance of drainage basin sediments as they enhance the persistence and transport of the fecal indicator bacteria E. coli within a watershed. Experiments presented use microcosm environments with drainage basin sediments and stormwater to investigate E. coli colonization of stagnant waters and to examine the importance of host sources to bacterial survival. A novel method for establishing microcosms using environmental sediments with in situ bacterial populations and sterile overlying waters is used to examine E. coli colonization of the water column in the absence of flow. Colonization of sterile sediment environments also is examined using two common host sources (human and avian). Each experiment uses sediments of varying grain size and organic content to examine the influence of physical characteristics on bacterial prevalence. Results suggest host source of bacteria may be more important to initial bacterial colonization while physical characteristics of drainage basin sediments better explains extended E. coli persistence. Findings also suggest an indirect control of water column bacterial concentration by sediment type and erodibility.

While the presence of fecal indicator bacteria such as Escherichia coli in urban stormwater has been widely documented, their occurrence and persistence in sediments are not as well understood. Recent investigations suggest that E. coli can accumulate in drainage basin sediments and act as a fecal bacterial reservoir within a watershed. We investigate the prevalence of E. coli populations in a tidal creek stormwater catchment and examine their interaction with overlying stormwater under wet and dry weather conditions. Two rain events are sampled more intensively with samples collected prior to, during, and after rainfall to profile bacteria in each matrix throughout a storm. Results of profile sampling and estimates of sediment resuspension provide evidence for E. coli accumulation during dry conditions and entrainment in overlying waters during storm conditions. Profile results suggest the occurrence of steady-state E. coli populations in drainage basin sediments.

While the presence of fecal indicator bacteria such as Escherichia coli in urban stormwater has been well documented, their occurrence and propagation in sediments are not well understood. Traditionally, fecal bacteria such as E. coli are thought to have short lifetimes in the environment. Recent research suggests that E. coli can accumulate in the soils, sands, and sediments and can act as a fecal bacterial reservoir within a watershed. In this study, we investigate the presence, concentration, and survival of E. coli in a variety of sediment types within the Withers Swash tidal creek stormwater catchment area as part of a microbial source tracking project. By examining the effects of physical and climactic conditions as well as biological inputs we hope to better understand the factors influencing bacterial success in drainage basin sediments. The Relationship between Sediment Grain Size and Percent Organic Content of Sediment to E. coli Prevalence