The ongoing global pandemic of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been a Public Health Emergency of International Concern, ...which was officially declared by the World Health Organization. SARS-CoV-2 is a member of the family Coronaviridae that consists of a group of enveloped viruses with single-stranded RNA genome, which cause diseases ranging from common colds to acute respiratory distress syndrome. Although the major transmission routes of SARS-CoV-2 are inhalation of aerosol/droplet and person-to-person contact, currently available evidence indicates that the viral RNA is present in wastewater, suggesting the need to better understand wastewater as potential sources of epidemiological data and human health risks. Here, we review the current knowledge related to the potential of wastewater surveillance to understand the epidemiology of COVID-19, methodologies for the detection and quantification of SARS-CoV-2 in wastewater, and information relevant for human health risk assessment of SARS-CoV-2. There has been growing evidence of gastrointestinal symptoms caused by SARS-CoV-2 infections and the presence of viral RNA not only in feces of infected individuals but also in wastewater. One of the major challenges in SARS-CoV-2 detection/quantification in wastewater samples is the lack of an optimized and standardized protocol. Currently available data are also limited for conducting a quantitative microbial risk assessment (QMRA) for SARS-CoV-2 exposure pathways. However, modeling-based approaches have a potential role to play in reducing the impact of the ongoing COVID-19 outbreak. Furthermore, QMRA parameters obtained from previous studies on relevant respiratory viruses help to inform risk assessments of SARS-CoV-2. Our understanding on the potential role of wastewater in SARS-CoV-2 transmission is largely limited by knowledge gaps in its occurrence, persistence, and removal in wastewater. There is an urgent need for further research to establish methodologies for wastewater surveillance and understand the implications of the presence of SARS-CoV-2 in wastewater.
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•Presence of SARS-CoV-2 RNA in wastewater has been reported.•SARS-CoV-2 RNA in wastewater can be used to monitor COVID-19 in a community.•Effective concentration method is needed for recovery of SARS-CoV-2 from wastewater.•Surrogate coronavirus data help to predict survival of SARS-CoV-2 in wastewater.•Data on the infectivity of SARS-CoV-2 in wastewater for risk assessment are limited.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Highlights ► We review recent advances in molecular tools for detecting pathogens in water. ► Microarrays, qPCR and pyrosequencing are emerging rapidly as tools of pathogen detection. ► Challenges ...remain in applying molecular tools for waterborne pathogen detection on a routine basis.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
•A metagenomic approach for the characterization of diverse viruses in wastewater is described.•The sewage virome was dominated by bacteriophages.•Human adenoviruses, polyomaviruses and enteroviruses ...were detected using metagenomics.
Genomic-based molecular techniques are emerging as powerful tools that allow a comprehensive characterization of water and wastewater microbiomes. Most recently, next generation sequencing (NGS) technologies which produce large amounts of sequence data are beginning to impact the field of environmental virology. In this study, NGS and bioinformatics have been employed for the direct detection and characterization of viruses in wastewater and of viruses isolated after cell culture. Viral particles were concentrated and purified from sewage samples by polyethylene glycol precipitation. Viral nucleic acid was extracted and randomly amplified prior to sequencing using Illumina technology, yielding a total of 18 million sequence reads. Most of the viral sequences detected could not be characterized, indicating the great viral diversity that is yet to be discovered. This sewage virome was dominated by bacteriophages and contained sequences related to known human pathogenic viruses such as adenoviruses (species B, C and F), polyomaviruses JC and BK and enteroviruses (type B). An array of other animal viruses was also found, suggesting unknown zoonotic viruses. This study demonstrated the feasibility of metagenomic approaches to characterize viruses in complex environmental water samples.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
The pathogen concentration in human excreta needs to be managed appropriately, but a predictive approach has yet to be implemented due to a lack of kinetics models for pathogen inactivation that are ...available under varied environmental conditions. Our goals were to develop inactivation kinetics models of microorganisms applicable under varied environmental conditions of excreta matrices and to identify the appropriate indicators that can be monitored during disinfection processes. We conducted a systematic review targeting previous studies that presented time-course decay of a microorganism and environmental conditions of matrices. Defined as a function of measurable factors including treatment time, pH, temperature, ammonia concentration and moisture content, the kinetic model parameters were statistically estimated using hierarchical Bayesian modeling. The inactivation kinetics models were constructed for Escherichia coli, Salmonella, Enterococcus, Ascaris eggs, bacteriophage MS2, enterobacteria phage phiX174 and adenovirus. The inactivation rates of a microorganism were predicted using the established model. Ascaris eggs were identified as the most tolerant microorganisms, followed by bacteriophage MS2 and Enterococcus. Ammonia concentration, temperature and moisture content were the critical factors for the Ascaris inactivation. Our model predictions coincided with the current WHO guidelines. The developed inactivation kinetics models enable us to predict microbial concentration in excreta matrices under varied environmental conditions, which is essential for microbiological risk management in emerging resource recovery practices from human excreta.
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•Hierarchical Bayesian modeling was used to predict pathogen inactivation kinetics.•The persistence of seven microorganisms in excreta matrices was predicted.•The key predictors in inactivation kinetics varied depending on microorganisms.•Predictive “environmental” microbiology realizes a safe use of human excreta.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•Indicator organisms do not tell the whole story for the safety of water and sanitation systems.•Considering only fecal indicator groups may provide a falsely reduced sense of risk.•Consideration of ...pathogens matters for meeting SDG6.
Water and wastewater utilities, water and sanitation hygiene (WASH) practitioners, and regulating bodies, particularly in developing nations, rely heavily on indicator microorganisms, as opposed to pathogens, for much of their regulatory decisions. This commentary illustrates the importance of considering pathogens and not relying only on indicator organisms when making decisions regarding water and sanitation, especially with respect to meeting the current targets of the Sustainable Development Goal (SDG) 6. We use quantitative microbial risk assessment (QMRA) to present three common scenarios that WASH and public health practitioners encounter to illustrate our point. These include 1) chlorination of surface water for drinking, 2) land application of latrine waste as a fertilizer, and 3) recreation/domestic use of surface waters impacted by wastewater discharge. We show that the calculated probabilities of risk of infection are statistically significantly higher when using treatment/survival information for pathogens versus using indicator species data. Thus, demonstrating that relying solely on indicators for sanitation decision making is inadequate if we truly want to achieve the SDG6 targets of safely managed water and sanitation services.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Wastewater-based epidemiology (WBE) has gained increasing attention as a complementary tool to conventional surveillance methods with potential for significant resource and labour savings when used ...for public health monitoring. Using WBE datasets to train machine learning algorithms and develop predictive models may also facilitate early warnings for the spread of outbreaks. The challenges associated with using machine learning for the analysis of WBE datasets and timeseries forecasting of COVID-19 were explored by running Random Forest (RF) algorithms on WBE datasets across 108 sites in five regions: Scotland, Catalonia, Ohio, the Netherlands, and Switzerland. This method uses measurements of SARS-CoV-2 RNA fragment concentration in samples taken at the inlets of wastewater treatment plants, providing insight into the prevalence of infection in upstream wastewater catchment populations. RF's forecasting performance at each site was quantitatively evaluated by determining mean absolute percentage error (MAPE) values, which was used to highlight challenges affecting future implementations of RF for WBE forecasting efforts. Performance was generally poor using WBE datasets from Catalonia, Scotland, and Ohio with ‘reasonable’ or better forecasts constituting 0 %, 5 %, and 0 % of these regions' forecasts, respectively. RF's performance was much stronger with WBE data from the Netherlands and Switzerland, which provided 55 % and 45 % ‘reasonable’ or better forecasts respectively. Sampling frequency and training set size were identified as key factors contributing to accuracy, while inclusion of too many unnecessary variables (or e.g., flow data) was identified as a contributing factor to poor performance. The contribution of catchment population on forecast accuracy was more ambiguous. This study determined that the factors governing RF's forecast performance are complicated and interrelated, which presents challenges for further work in this space. A sufficiently accurate further iteration of the tool discussed within this study would provide significant but varying value for public health departments for monitoring future, or ongoing outbreaks, assisting the implementation of on-time health response measures.
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•Explored the challenges associated with using machine learning algorithms for analysis of WBE datasets•Evaluated the performance and accuracy of Random Forest algorithm for short-term predictions based on WBE datasets•Sampling frequency and training set size were identified as key factors contributing to accuracy.•Contribution of catchment population on forecast accuracy was more ambiguous.•Determined that the factors governing Random Forest forecast performance are complicated and interrelated
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Studies of marine viromes (viral metagenomes) have revealed that DNA viruses are highly diverse and exhibit biogeographic patterns. However, little is known about the diversity of RNA viruses, which ...are mostly composed of eukaryotic viruses, and their biogeographic patterns in the oceans. A growth in global commerce and maritime traffic may accelerate spread of diverse and non-cosmopolitan DNA viruses and potentially RNA viruses from one part of the world to another. Here, we demonstrated through metagenomic analyses that failure to comply with mid-ocean ballast water exchange regulation could result in movement of viromes including both DNA viruses and RNA viruses (including potential viral pathogens) unique to geographic and environmental niches. Furthermore, our results showed that virus richness (known and unknown viruses) in ballast water is associated with distance between ballast water exchange location and its nearest shoreline as well as length of water storage time in ballast tanks (voyage duration). However, richness of only known viruses is governed by local environmental conditions and different viral groups have different responses to environmental variation. Overall, these results identified ballast water as a factor contributing to ocean virome transport and potentially increased exposure of the aquatic bioshpere to viral invasion.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Linking fecal indicator bacteria concentrations in large mixed-use watersheds back to diffuse human sources, such as septic systems, has met limited success. In this study, 64 rivers that drain 84% ...of Michigan’s Lower Peninsula were sampled under baseflow conditions forEscherichia coli, Bacteroides thetaiotaomicron(a human source-tracking marker), landscape characteristics, and geochemical and hydrologic variables.E. coliandB. thetaiotaomicronwere routinely detected in sampled rivers and anE. colireference level was defined (1.4 log10most probable number·100 mL⁻¹). Using classification and regression tree analysis and demographic estimates of wastewater treatments per watershed, septic systems seem to be the primary driver of fecal bacteria levels. In particular, watersheds with more than 1,621 septic systems exhibited significantly higher concentrations ofB. thetaiotaomicron. This information is vital for evaluating water quality and health implications, determining the impacts of septic systems on watersheds, and improving management decisions for locating, constructing, and maintaining on-site wastewater treatment systems.
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BFBNIB, NMLJ, NUK, PNG, SAZU, UL, UM, UPUK
The emergence of culture- and sequence-independent metagenomic methods has not only provided great insight into the microbial community structure in a wide range of clinical and environmental samples ...but has also proven to be powerful tools for pathogen detection. Recent studies of the food microbiome have revealed the vast genetic diversity of bacteria associated with fresh produce. However, no work has been done to apply metagenomic methods to tackle viruses associated with fresh produce for addressing food safety. Thus, there is a little knowledge about the presence and diversity of viruses associated with fresh produce from farm-to-fork. To address this knowledge gap, we assessed viruses on commercial romaine and iceberg lettuces in fields and a produce distribution center using a shotgun metagenomic sequencing targeting both RNA and DNA viruses. Commercial lettuce harbors an immense assemblage of viruses that infect a wide range of hosts. As expected, plant pathogenic viruses dominated these communities. Sequences of rotaviruses and picobirnaviruses were also identified in both field-harvest and retail lettuce samples, suggesting an emerging foodborne transmission threat that has yet to be fully recognized. The identification of human and animal viruses in lettuce samples in the field emphasizes the importance of preventing viral contamination on leafy greens starting at the field. Although there are still some inherent experimental and bioinformatics challenges in applying viral metagenomic approaches for food safety testing, this work will facilitate further application of this unprecedented deep sequencing method to food samples.
•We used metagenomics to characterize viruses in field-grown and retail lettuce.•Human and animal viruses were found in lettuce prior and following field harvest.•Human and animal viruses were found in lettuce from produce distribution center.•Rotaviruses and picobirnaviruses were detected using metagenomics.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
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