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A general approach for predicting indicator and pathogen decay in surface waters was developed using Bayesian hierarchical modeling, a persistence database, and a two-parameter model form. The resulting hierarchical regression describes general persistence behaviors with target-level intercepts and population-level coefficients. Uncertainty factors calculated with the approach suggest fecal indicator bacteria (FIB) and pathogenic bacteria persist similarly in surface waters, but median virus and protozoa persistence metrics may be 2–3 times greater than FIB in similar conditions. The two-parameter model underpinning the approach was used to identify drivers of these differences. Virus decay rates were shown to taper off more quickly than FIB, whereas protozoa were associated with longer initial periods of minimal decay. Despite the low accuracy of the hierarchical model compared to models fit to individual datasets, this approach addresses a critical gap for water management decision-making as site-specific and pathogen-specific persistence data are uncommon in water monitoring practices. • Indicators and pathogens persist longer at colder temperatures. • Decay of viruses and protozoa may be more than 3x slower than that of FIB. • A generalizable persistence model can support surface water management. [ABSTRACT FROM AUTHOR]

Microbial dose response modelling is vital to a well-characterized microbial risk estimate. Dose response modelling is an inherently multidisciplinary field, which collates knowledge and data from disparate scientific fields. This multidisciplinary nature presents a key challenge to the expansion of microbial dose response modelling into new groups of researchers and modelers. This research employs a dose response optimization R code used in 18 peer-reviewed research studies to develop a multi-functional dose response software. The underlying R code performs an optimization of the two primary dose response models using the MLE method and outputs statistical analyses of the fits and bootstrapped uncertainty information for the models. VizDR (Visual Dose Response) was developed to provide microbial dose response modelling capabilities to a larger audience. VizDR is programmed in JavaScript with underlying Python scripts for intercommunication with Rserve. VizDR allows for dose response model visualization and optimization of a user's own experimental data. [ABSTRACT FROM AUTHOR]

• Temperature, sunlight and predation were the factors most frequently documented. • Minimal data available for protozoa, water type comparisons, and molecular methods. • Interactions identified between factors and sunlight, predation and method. Biphasic decay has been observed for indicators and pathogens in bench-scale and in-situ water experiments for decades, however, first-order decay kinetics continue to be applied to persistence data because of their simplicity and ease of application. Model uncertainty introduced by broadly applying first-order decay kinetics to persistence data may lead to erroneous decision making in the fields of water management and protection. As surface waters are exposed to highly variable environmental and water quality factors that influence microbial and viral persistence, it is expected that first-order decay kinetics are not representative of most of the persistence literature for indicators and pathogens in surface water matrices. This review compiled the methods and results of 61 studies that conducted experiments evaluating the persistence of fecal indicator bacteria (FIB), bacteriophages, pathogenic bacteria, viruses, and protozoa in natural surface water matrices. The goals of this review were trifold: (1) collate studies in the literature with data available for future persistence modeling, (2) present the current state of knowledge with regards to the environmental and water quality factors affecting persistence in natural surface waters, and (3) identify recurrent evidence for interactions between the frequently studied factors to inform future factor analyses. Comparing the methods and results across the 61 studies suggest potential interactions between sunlight and water type; sunlight and method of detection; predation and water type; predation and temperature; and water type and method of detection. The majority of the identified literature evaluated FIB or bacteria persistence; future experiments are needed that focus on protozoa, brackish or marine water types, and molecular-based methods of detection. [ABSTRACT FROM AUTHOR]

Abstract: While only limited data are available to characterize the potential toxicity of over 8million commercially available chemical substances, there is even less information available on the exposure and use-scenarios that are required to link potential toxicity to human and ecological health outcomes. Recent improvements and advances such as high throughput data gathering, high performance computational capabilities, and predictive chemical inherency methodology make this an opportune time to develop an exposure-based prioritization approach that can systematically utilize and link the asymmetrical bodies of knowledge for hazard and exposure. In response to the US EPA's need to develop novel approaches and tools for rapidly prioritizing chemicals, a “Challenge” was issued to several exposure model developers to aid the understanding of current systems in a broader sense and to assist the US EPA's effort to develop an approach comparable to other international efforts. A common set of chemicals were prioritized under each current approach. The results are presented herein along with a comparative analysis of the rankings of the chemicals based on metrics of exposure potential or actual exposure estimates. The analysis illustrates the similarities and differences across the domains of information incorporated in each modeling approach. The overall findings indicate a need to reconcile exposures from diffuse, indirect sources (far-field) with exposures from directly, applied chemicals in consumer products or resulting from the presence of a chemical in a microenvironment like a home or vehicle. Additionally, the exposure scenario, including the mode of entry into the environment (i.e. through air, water or sediment) appears to be an important determinant of the level of agreement between modeling approaches. [Copyright &y& Elsevier]

To evaluate the effect of Hospital Admission Risk Program (HARP) on unplanned hospitalization, bed days, and mortality of enrolled individuals and to evaluate the cost-effectiveness of HARP. A retrospective longitudinal analysis of hospital administrative data. Individuals at risk of hospitalization were provided with multidisciplinary, community-based care support managed by care coordinators including integrated care planning, education, monitoring, service linkages, and general practitioner liaison over 6-9 months. Individuals who were enrolled into 1 of 8 HARP chronic disease management programs between July 1, 2017, and June 30, 2018, at the Royal Melbourne Hospital, Australia. Hospital admissions between 18 months before and 18 months after HARP enrollment were analyzed. Total hospital costs were compared between 18 months before and 12 months after HARP enrollment. A total of 1553 individuals with a median age of 71 years (interquartile range 60-81), 63.4% males, were admitted to HARP. Both unplanned hospitalizations and bed days were reduced during the HARP intervention compared to within 3 months before enrollment in each of the HARP management programs. After the HARP intervention, cardiac coach, cardiac heart failure, chronic respiratory, diabetes comanagement, and medication management programs had higher hospitalizations and bed days than individuals' baseline of at least 3 months before HARP enrollment. Individuals in cardiac heart failure and chronic respiratory management programs had a higher mortality rate than other HARP chronic disease management programs. Individuals in cardiac coach, diabetes comanagement, and medication management programs had lower hospital costs during the HARP intervention compared to within 3 months before HARP enrollment. HARP reduced unplanned hospitalization and bed days but did not return individuals' hospital use to baseline before the intervention. The variations in mortality between HARP chronic disease management programs implies that condition-specific goals between programs is preferable. [ABSTRACT FROM AUTHOR]

Residential plumbing is critical for the health and safety of populations worldwide. A case study was conducted to understand fixture water use, drinking water quality and their possible link, in a newly plumbed residential green building. Water use and water quality were monitored at four in-building locations from September 2015 through December 2015. Once the home was fully inhabited average water stagnation periods were shortest at the 2nd floor hot fixture (90 percentile of 0.6–1.2 h). The maximum water stagnation time was 72.0 h. Bacteria and organic carbon levels increased inside the plumbing system compared to the municipal tap water entering the building. A greater amount of bacteria was detected in hot water samples (6–74,002 gene copy number/mL) compared to cold water (2–597 gene copy number/mL). This suggested that hot water plumbing promoted greater microbial growth. The basement fixture brass needle valve may have caused maximum Zn (5.9 mg/L), Fe (4.1 mg/L), and Pb (23 μg/L) levels compared to other fixture water samples (Zn ≤ 2.1 mg/L, Fe ≤ 0.5 mg/L and Pb ≤ 8 μg/L). At the basement fixture, where the least amount of water use events occurred (cold: 60–105, hot: 21–69 event/month) compared to the other fixtures in the building (cold: 145–856, hot: 326–2230 event/month), greater organic carbon, bacteria, and heavy metal levels were detected. Different fixture use patterns resulted in disparate water quality within a single-family home. The greatest drinking water quality changes were detected at the least frequently used fixture. [ABSTRACT FROM AUTHOR]

Addressing the persistence of bacterial indicators using qPCR and their respective DNA targets under various conditions is a critical part of risk assessment for water quality monitoring. The goal of this study was to examine the persistence of fecal indicator bacteria (FIB) via Escherichia coli uidA , enterococci 23S rDNA and Bacteroides thetataiotaomicron 1,6 alpha mannanase from cells attached to a solid matrix and in suspension. Raw sewage (10% vol/vol) was seeded into autoclaved river water with half of the sample volume in suspension and the other half was filtered onto membranes and stored at 4°, 27° and 37°C for up to 28 days. At various time points, DNA from cells was extracted, markers were quantified, and were fit to linear and non-linear models (first order exponential, biphasic (double) exponential, two-staged, log-logistic, and Gompertz 3-parameter). First order and biphasic exponential models fit 73% of the experimental data. Persistence increased significantly when the cells were stored in an attached state (p < 0.001). Increasing temperature had an inverse effect on persistence for the cells in suspension. Bacterial cells could be stored on a solid matrix at 4°, 27° and 37 °C for up to 27, 18, and 3 days, respectively, with <90% decay. The least stable indicator at 4°, 27° and 37 °C was B. thetataiotaomicron in suspension with T 90 = 9.6, 1.8, and 1.1 days, respectively. The most persistent indicator was enterococci, with T 90 > 28 days in an attached state at all temperatures. [ABSTRACT FROM AUTHOR]

• Virus persistence in water has inactivation kinetics not fully captured by linear models. • Three nonlinear models best fit viral persistence data most frequently. • Classification trees provide a novel framework for persistence model selection. Several factors can affect virus behavior and persistence in water sources. Historically linear models have been used to describe persistence over time; however, these models do not consider all of the factors that can affect inactivation kinetics or the observed patterns of decay. Meanwhile, applying the appropriate persistence model is critical for ensuring that decision makers are minimizing human health risk in the event of contamination and exposure to contaminated groundwater. Therefore, to address uncertainty in predictions of decay or virus concentrations over time, this study fit seventeen different linear and nonlinear mathematical models to persistence data from a previously conducted sampling study on drinking water wells throughout the United States. The models were fit using Maximum Likelihood Estimation and the best fitting models were determined by the Bayesian Information Criterion. The purpose of the study was to identify the best model for estimating decay of viruses in groundwater and to determine if model uncertainty contributes to erroneous predictions of viral contamination when only conventional models are considered. For the datasets analyzed in this study, the Juneja and Marks models and the exponential damped model were more representative of the persistence of viruses in groundwater than the traditionally used linear models. The results from this study were then evaluated with classification trees in order to identify more relevant modeling methodology for future research. The classification trees aid in narrowing the scope of appropriate persistence models based on characteristics of the experimental conditions and water sampled. [ABSTRACT FROM AUTHOR]