Risk assessment of noroviruses and human adenoviruses in recreational surface waters

Vergara, Genevieve Gabrielle Rose; Rose, Joan B.; Gin, Karina Yew‐Hoong

doi:10.1016/j.watres.2016.07.048

Cited by 62 publications

(27 citation statements)

References 31 publications

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“…The data of virus transfer from liquid-to-skin were adequately described by a linear regression model, as shown with R 2 values ranging from 0.63 to 0.93 for adsorbed or unadsorbed viruses (eq 6 and 7, Table 2). Specifically: (6) Which can be log-transformed: = + n m C b log log 10 10 (7) where n is the number of viruses transferred per unit surface area [viruses/cm 2 ], C is the concentration of viruses in the liquid [viruses/cm 3 ], and m and b are the slope and intercept estimated using linear regression of the log 10 transformed data. This regression model (eq 6) is equivalent to the Freundlich isotherm, which is a relationship commonly applied to virus adsorption.…”

Section: ■ Resultsmentioning

confidence: 99%

Virus Transfer at the Skin–Liquid Interface

Pitol

Bischel

Kohn

et al. 2017

Environ. Sci. Technol.

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Understanding virus transfer between liquid and skin is necessary to estimate transmission during water-related activities. Here, we modeled virus transfer from liquid-to-skin and skin-to-liquid. We performed human subject studies using three bacteriophages as pathogenic virus surrogates: nonenveloped MS2 and Qβ and enveloped Φ6. Our study shows that transfer from liquid-to-skin is describable by a single model based on (1) virus concentration and (2) volume of liquid remaining on skin. Contact times (0.1-30 min), and virus species had little-to-no influence on virus transfer. Likewise, liquid conditions (pH 6-9, ionic strength 10-550 mM) had no influence on transfer as shown for MS2. The model accounts for both, virus adsorbed onto the skin, and virus in the liquid retained on skin. In comparison, virus transfer from skin-to-liquid was influenced by the wetness of the skin and by liquid type (water, saliva). 90 ± 19% of the virus inoculated on the skin are transferred to the water when the skin remains wet compared to 30 ± 17% when the skin is dry. The transfer from skin-to-liquid was 41% higher when the recipient liquid was water as compared with saliva. This study quantifies virus transfer between liquid and skin and guides risk assessments of water-related activities.

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Section: ■ Resultsmentioning

confidence: 99%

Virus Transfer at the Skin–Liquid Interface

Pitol

Bischel

Kohn

et al. 2017

Environ. Sci. Technol.

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“…could not be detected with viral metagenomics in our study, past studies have shown that they are prevalent in tributaries using qPCR and thus, need to be carefully monitored for quantifying risk assessment and providing guidelines for water recreational activities (Aw and Gin, 2011;Aw et al, 2009). Vergara et al (2016) quantified illness risks of norovirus in an urban catchment in Singapore by using quantitative microbial risk assessment. The finding reported mean probability of illness associated with norovirus were 0.0061 and 0.0089 in the scenario of primary contact recreation of adults and children, which is below the USEPA guideline value of 0.036 (USEPA, 2012).…”

Section: Human/plant Related Viruses and Correlations Between Qpcr/ Rmentioning

confidence: 99%

Geospatial distribution of viromes in tropical freshwater ecosystems

Tay

et al. 2018

Water Research

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This study seeks to understand the general distribution of virome abundance and diversity in tropical freshwater ecosystems in Singapore and the geospatial distribution of the virome under different landuse patterns. Correlations between diversity, environmental parameters and land use patterns were analyzed and significant correlations were highlighted. Overall, the majority (65.5%) of the annotated virome belonged to bacteriophages. The percentage of Caudovirales was higher in reservoirs whereas the percentages of Dicistroviridae, Microviridae and Circoviridae were higher in tributaries. Reservoirs showed a higher Shannon-index virome diversity compared to upstream tributaries. Land use (urbanized, agriculture and parkland areas) influenced the characteristics of the virome distribution pattern. Dicistroviridae and Microviridae were enriched in urbanized tributaries while Mimiviridae, Phycodnaviridae, Siphoviridae and Podoviridae were enriched in parkland reservoirs. Several sequences closely related to the emerging zoonotic virus, cyclovirus, and the human-related virus (human picobirnavirus), were also detected. In addition, the relative abundance of PMMoV (pepper mild mottle virus) sequences was significantly correlated with RT-qPCR measurements (0.588 < r < 0.879, p < 0.05). This study shows that spatial factors (e.g., reservoirs/tributaries, land use) are the main drivers of the viral community structure in tropical freshwater ecosystems.

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“…In QMRA modeling, to account for within-group heterogeneity, a group-specific exposure volume-for example, elderly inhalation volume for elderly hospital patients-is used to make the exposure model specific to that group. This method has been employed the most in recreational water QMRA models since children's recreational risks are often used to prioritize intervention options [28,[32][33][34]. Therefore, for host-age heterogeneity in L. pneumophila exposure modeling, inhalation volumes for children, adults, and the elderly can be chosen from the EPA exposure factors handbook [35] to make the exposure model specific to the population's age heterogeneity.…”

Section: Current Qmra Modeling Methods Are Inadequate For L Pneumophilamentioning

confidence: 99%

An Advanced Risk Modeling Method to Estimate Legionellosis Risks Within a Diverse Population

Weir

Mraz

Mitchell

2019

Water

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Quantitative microbial risk assessment (QMRA) is a computational science leveraged to optimize infectious disease controls at both population and individual levels. Often, diverse populations will have different health risks based on a population's susceptibility or outcome severity due to heterogeneity within the host. Unfortunately, due to a host homogeneity assumption in the microbial dose-response models' derivation, the current QMRA method of modeling exposure volume heterogeneity is not an accurate method for pathogens such as Legionella pneumophila. Therefore, a new method to model within-group heterogeneity is needed. The method developed in this research uses USA national incidence rates from the Centers for Disease Control and Prevention (CDC) to calculate proxies for the morbidity ratio that are descriptive of the within-group variability. From these proxies, an example QMRA model is developed to demonstrate their use. This method makes the QMRA results more representative of clinical outcomes and increases population-specific precision. Further, the risks estimated demonstrate a significant difference between demographic groups known to have heterogeneous health outcomes after infection. The method both improves fidelity to the real health impacts resulting from L. pneumophila infection and allows for the estimation of severe disability-adjusted life years (DALYs) for Legionnaires' disease, moderate DALYs for Pontiac fever, and post-acute DALYs for sequela after recovering from Legionnaires' disease.

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Risk assessment of noroviruses and human adenoviruses in recreational surface waters

Cited by 62 publications

References 31 publications

Virus Transfer at the Skin–Liquid Interface

Virus Transfer at the Skin–Liquid Interface

Geospatial distribution of viromes in tropical freshwater ecosystems

An Advanced Risk Modeling Method to Estimate Legionellosis Risks Within a Diverse Population

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