As part of a larger quantitative microbial risk assessment (QMRA) study, the raw water intakes of 8 different drinking water treatment plants in Ontario were sampled for rotavirus. Group A rotavirus was detected and semi-quantified via RT-qPCR. Rotavirus was detected in 6 of 8 drinking water treatment plant raw water intakes at various sampling times during a 2 year period at estimated quantities of 0 – 513 viral genome copies/L water. As hypothesized, the virus counts showed a seasonal tendency with significant detection most likely to occur during the spring months and a correlation with turbidity measurements. To our knowledge this is the first study exploring the presence of rotavirus in Ontario source waters. With new proposed changes to the Health Canada guidelines regarding the viruses in drinking water, data on the presence of rotavirus in source waters is required for assessment of risk to public health. / Kingsclear First Nation

Saudi Arabia produces a majority of its fruits and vegetables locally in small-scale production farms. These farms utilize groundwater as the main source of irrigation water. The water-regulating authorities in Saudi Arabia rely on traditional culturing methods to monitor coliforms as indicators of microbial contamination. These methods are time-consuming, do not address the sources of contamination, and do not permit assessment on the associated health risk. To address these knowledge gaps, the study investigates the sources of contamination in eight wells northeast of Mecca, Saudi Arabia. The study focuses on the potential impact on groundwater quality due to a nearby chicken farm and urban runoffs from human residential areas. Besides performing conventional methods to determine nutrient content and to enumerate coliforms, quantitative PCR using four host-associated primer sets were used to distinguish microbial contamination from humans and livestock. High-throughput sequencing was also performed to determine the relative abundance of several genera associated with opportunistic pathogens. Bacterial isolates were cultivated from the vegetable samples harvested from these farms, and were characterized for their phylogenetic identities. Lastly, the study collates the information to perform quantitative microbial risk assessment due to ingesting antibiotic-resistant Listeria monocytogenes, Pseudomonas aeruginosa and Enterococcus faecalis in these vegetable samples.

Parameters associated with the detection and quantification of microorganisms (or discrete particles) in water such as the analytical recovery of an enumeration method, the concentration of the microorganisms or particles in the water, the log-reduction achieved using a treatment process, and the sensitivity of a detection method cannot be measured exactly. There are unavoidable random errors in the enumeration process that make estimates of these parameters imprecise and possibly also inaccurate. For example, the number of microorganisms observed divided by the volume of water analyzed is commonly used as an estimate of concentration, but there are random errors in sample collection and sample processing that make these estimates imprecise. Moreover, this estimate is inaccurate if poor analytical recovery results in observation of a different number of microorganisms than what was actually present in the sample. In this thesis, a statistical framework (using probabilistic modelling and Bayes’ theorem) is developed to enable appropriate analysis of microorganism concentration estimates given information about analytical recovery and knowledge of how various random errors in the enumeration process affect count data. Similar models are developed to enable analysis of recovery data given information about the seed dose. This statistical framework is used to address several problems: (1) estimation of parameters that describe random sample-to-sample variability in the analytical recovery of an enumeration method, (2) estimation of concentration, and quantification of the uncertainty therein, from single or replicate data (which may include non-detect samples), (3) estimation of the log-reduction of a treatment process (and the uncertainty therein) from pre- and post-treatment concentration estimates, (4) quantification of random concentration variability over time, and (5) estimation of the sensitivity of enumeration processes given knowledge about analytical recovery. The developed models are also used to investigate alternative strategies that may enable collection of more precise data. The concepts presented in this thesis are used to enhance analysis of pathogen concentration data in Quantitative Microbial Risk Assessment so that computed risk estimates are more predictive. Drinking water research and prudent management of treatment systems depend upon collection of reliable data and appropriate interpretation of the data that are available.

Parameters associated with the detection and quantification of microorganisms (or discrete particles) in water such as the analytical recovery of an enumeration method, the concentration of the microorganisms or particles in the water, the log-reduction achieved using a treatment process, and the sensitivity of a detection method cannot be measured exactly. There are unavoidable random errors in the enumeration process that make estimates of these parameters imprecise and possibly also inaccurate. For example, the number of microorganisms observed divided by the volume of water analyzed is commonly used as an estimate of concentration, but there are random errors in sample collection and sample processing that make these estimates imprecise. Moreover, this estimate is inaccurate if poor analytical recovery results in observation of a different number of microorganisms than what was actually present in the sample. In this thesis, a statistical framework (using probabilistic modelling and Bayes’ theorem) is developed to enable appropriate analysis of microorganism concentration estimates given information about analytical recovery and knowledge of how various random errors in the enumeration process affect count data. Similar models are developed to enable analysis of recovery data given information about the seed dose. This statistical framework is used to address several problems: (1) estimation of parameters that describe random sample-to-sample variability in the analytical recovery of an enumeration method, (2) estimation of concentration, and quantification of the uncertainty therein, from single or replicate data (which may include non-detect samples), (3) estimation of the log-reduction of a treatment process (and the uncertainty therein) from pre- and post-treatment concentration estimates, (4) quantification of random concentration variability over time, and (5) estimation of the sensitivity of enumeration processes given knowledge about analytical recovery. The developed models are also used to investigate alternative strategies that may enable collection of more precise data. The concepts presented in this thesis are used to enhance analysis of pathogen concentration data in Quantitative Microbial Risk Assessment so that computed risk estimates are more predictive. Drinking water research and prudent management of treatment systems depend upon collection of reliable data and appropriate interpretation of the data that are available.

No description available.

As the demand for reliable and safe water supplies increases, both water quality and available quantity are being challenged by population growth and climate change. Greywater reuse is becoming a common practice worldwide; however, in remote locations of limited water supply, such as those encountered in military installations, it is desirable to expand its classification to include dishwashing water to maximize the conservation of fresh water. Given that no standards for dishwashing greywater reuse by the military are currently available, the current study determined a specific set of water quality standards for dishwater recycling systems for U.S military field operations. A tentative water reuse standard for dishwashing water was developed based on federal and state regulations and guidelines for non-potable water, and the developed standard was cross-evaluated by monitoring water quality data from a full-scale dishwashing water recycling system using an innovative electrocoagulation and ultrafiltration process. A quantitative microbial risk assessment (QMRA) was also performed based on exposure scenarios derived from literature data. As a result, a specific set of dishwashing water reuse standards for field analysis (simple, but accurate) was finalized as follows: turbidity (< 1 NTU), E. coli (< 50 cfu mL-1), and pH (6–9). UV254 was recommended as a surrogate for organic contaminants (e.g., BOD5), but requires further calibration steps for validation. The developed specific water standard is the first for dishwashing water reuse and will be expected to ensure that water quality is safe for field operations, but not so stringent that design complexity, cost, and operational and maintenance requirements will not be feasible for field use. In addition the parameters can be monitored using simple equipment in a field setting with only modest training requirements and real-time or rapid sample turn-around. This standard may prove useful in future development of civilian guidelines.

No description available.

Public health costs of foodborne campylobacteriosis are estimated to cost more than a billion dollars in the United States annually. This pathogen has been primarily associated with chicken production and processing which is a ~$33 billion industry. To further identify practices that could reduce Campylobacter presence, concentration, and persistence in chicken prior to consumption a quantitative microbial risk assessment (QMRA) was conducted for boneless, skinless chicken breast meals prepared and consumed domestically in the contiguous United States. This QMRA model was developed with @RISK software (Palisade Corp., Ithaca, NY) and data inputs can be easily modified and updated. QMRA is a powerful analytic method that can be utilized to model the dynamics between the food pathogen, food commodity, and ingestion. It provides insight into the impacts of the process in its interaction and its surrounding environment. The baseline model determined that consumption of this product resulted in annual means of infections: 328,257, illnesses: 108,174, hospitalizations: 27,754, deaths: 37, cases of Campylobacter-associated Guillain-Barré Syndrome (GBS): 1,373, and cases of Campylobacter-associated Irritable Bowel Syndrome (IBS): 9,501. The associated annual economic burdens were ~$192 million for acute campylobacteriosis, ~$77 million for GBS, and ~$96 million for IBS. The effects of targeting and modifying the baseline model's inputs within the farm-to-fork process were studied as follows: Post grow-out (1) prevalence and (2) concentration of Campylobacter on chickens at the farm prior to transport, (3) transport crate cleaning frequency prior to loading, (4) temperature storage conditions at post processing/ pre-retail, (5) the frequency of handwashing in the preparation and handling of a chicken meal, and (6) the combination Campylobacter mitigation models using the inputs from (2) and (4). Mean yearly illnesses are estimated to decrease by approximately half if on-farm Campylobacter prevalence was lowered to 35-50% from the baseline level of 76%. An additional ~50,000 illnesses can be expected if the proportion of home preparers who do not wash their hands increases from 8.3% (baseline) to 20%. The combined effects of reducing on-farm Campylobacter concentrations and increasing the proportion of product frozen in-plant have greater impacts on reducing yearly campylobacteriosis and associated costs than either intervention alone. / Doctor of Philosophy / Campylobacter is a major foodborne bacterial pathogen that is not well known by the general public, although public health costs are estimated to cost more than a billion dollars in the United States annually. Chicken production is a ~$33 billion industry that is affected by the contamination of this pathogen. In this study, a quantitative microbial risk assessment (QMRA) was performed to study the impact of Campylobacter on chicken meals prepared in the home. Societal and economic burdens were evaluated as well. The models developed provide comprehensive simulations that describe the spread, persistence, and the concentration of Campylobacter throughout the farm to fork process in the annual consumption of boneless, skinless chicken breast meals prepared and consumed domestically in the contiguous United States. Additional simulation models were created to compare methods for reducing Campylobacter along the food chain that could lead to fewer cases of campylobacteriosis in the United States, or what could happen if there was a breakdown in a hygiene step in the preparation of the chicken meal.

A Quantitative Microbial Risk Assessment (QMRA) has been undertaken to utilize research on Shiga-toxin Escherichia coli (STEC) contamination in beef for the benefit of public health. The QMRA operates as a 2nd order Monte Carlo simulation to create stochastic mathematical models that incorporate all of the key components of STEC contamination from farm to fork. The resulting model is able to identify knowledge gaps, public health risks, and simulate theoretical changes in the beef system. However, high variability in processing plant intervention literature has prompted a meta-analysis to determine informed estimates of intervention effectiveness for QMRA parameterization. Meta-analysis derived least-squares means bacterial log reductions for acetic acid, lactic acid, steam vacuum, and water wash interventions on carcass surfaces (n=249) were 1.44 [95% CI: 0.73 – 2.15], 2.07 [1.48 – 2.65], 3.09 [2.46 – 3.73], and 1.90 [1.33 – 2.47] log CFU/cm2, respectively. Least-squares means log reductions for acetic acid, lactic acid, sodium hydroxide, and water wash on hide surfaces (n=47) were 2.21 [1.36 – 3.05], 3.02 [2.16 – 3.88], 3.66 [2.60 – 4.72], and 0.08 [-0.94 – 1.11] log CFU/cm2, respectively. Meta-regressions showed that temperature, duration of application, microbial starting concentration, extra water washes, inoculation type, organism type, sample method, surface type, and antimicrobial concentrations were all significant predictors of intervention effectiveness. Finally, after observing authors use substituted values for samples found below a detection limit in primary plant intervention literature, simulations were run to assess the impact of substitution on a random-effects meta-analysis. Simulation results show that substitution practices artificially decrease effectiveness estimates and increase heterogeneity. / Master of Science

Globally a major health concern according to the World health organization (WHO, 2011) is gastro-intestinal infections caused by fecally contaminated water. The access to drinking water has increased due to international efforts, however the long-term sustainability and safety of the water accessed have gained criticism, and many water sources have proven to be both contaminated (UN, 2016) and badly managed (WHO, 2016a).  This thesis aims to design a monitoring program for small-scale water treatment in order to make the water supply sustainable in terms of providing safe water in a long-term perspective. A case-study was conducted for three treatment systems under constructed in rural Ecuador. The monitoring program design was based on a literature review and conducting a quantitative microbial risk assessment (QMRA). QMRA is a tool for estimating microbial risks, by using quantitative data on microbial contamination and estimating health risks. Data for the QMRA was gathered from literature and in field, and the reference pathogens used in the QMRA were E.coli O157:H7, Rotavirus and Giardia. In order to estimate infection risk from drinking water consumption for the community a QMRA-model called MRA, developed by Abrahamsson et al. (2009) was used.  Observations of the catchment areas and measurement of water quality regarding aspects other than microbial contamination indicated that the main risk was microbial contamination from fecal contaminations in the catchment area. The results from the QMRA indicated that the treatment using chlorination reduces E.coli O157:H7 under the acceptable risk level of 1/1000 infections per person and year, while the systems using biosand filters (BSF) are more effective in reducing rotavirus and Giardia. If the BSF are combined with chlorination the annual probability of infection caused by consumption of the treated water per year and person was 0.42/1000 for E.coli O157:H7, 570/1000 for Rotavirus and 25/1000 for Giardia.  The resulting monitoring program was divided into two parts: one part aimed to prevent contamination and one part designed to measure pH, temperature, conductivity, turbidity on a weekly basis and microbial indicator tests using a presence/absence method monthly. Additional testing is to be done in case of events of such character that the water quality could be effected, for example an extreme weather event.  It was concluded that the designed monitoring program could help improve the water quality in a long-term perspective, but it is dependent on the possibilities to get the necessary support, especially in the implementation phase. Recommended further studies includes collection of more site-specific data to make the QMRA results more representative, and evaluation of the monitoring program design by implementing it and optimizing it in the communities. / Runt om i världen skapar en otillräcklig tillgång på rent vatten och sanitet mycket lidande. Enlig världshälsoorganisationen WHO (2011) är ett av de ledande världshälsoproblem mag- och tarminfektioner som orsakats av vattenburna fekala patogener. Trots att antalet människor med tillgång till en dricksvattenkälla har ökat till följd av internationella ansträngningar, är hållbarheten och säkerheten för vattenkvalitén problematisk. Många dricksvattenkällor har visat sig vara både förorenade (UN, 2016) och undermåligt skötta (WHO, 2016a). Målet med denna studie är att ta fram ett vattenkvalitetsövervakningsprogram för tre småskaliga vattenreningsverk, för att dessa ska producera säkert vatten i ett långsiktigt perspektiv.  En fallstudie utfördes i byar på landsbygden i Ecuador där systemen planerats. Metoden för att ta fram ett kvalitetsövervakningsprogram var litteraturstudie och mikrobiell riskanalys. Den mikrobiella riskanalysen genomfördes med en metod som kallas Kvantitativ Mikrobiell Risk Analys (QMRA). I QMRA kan hälsorisker från mikrobiell kontamination estimeras med kvantitativdata på mikrobiell förorening. Data för att genomföra QMRA samlades från litteraturen och fältbesök. För att estimera hälsorisker i byarna i fallstudien användes en QMRA-modell som heter MRA framtaget av Abrahamsson et.al. (2009).  Observationer i fält och data på ingående vatten tydde på att de största riskerna för vattenkvalitén var fekal kontamination från djur och människor. Resultaten från QMRA:n visade att reningsverket med klorering reducerade E.coli O157:H7 till en nivå under den accepterade risknivå, satt till 1/1000 infekterade per år och person. Reningsverken med biosandfilter (BSF) var mer effektiva i reduktionen av rotavirus och Giardia. Då klor kombinerades med BSF i modellen blev den årliga infektionsnivån per person 570/1000 för Rotavirus och 25/1000 för Giardia.  Vattenkvalitetsövervakningsprogrammet delades in två delar: en kontaminationsförebyggande och en för att mäta pH, temperatur, konduktivitet och turbiditet veckovis, samt mikrobiella indikatortest med en metod som noterar förekomst av bakteriekolonier (presence/absence metod) månadsvis. Extra tester ska även göras vid sådan händelse som kan komma att påverka vattenkvalitén avsevärt, exempelvis en kraftig storm.  Slutsatsen är att det framtagna vattenkvalitetsövervakningsprogrammet kan göra att vatten-källan blir mer säker och hållbar i ett långsiktigt perspektiv, men att framgången är beroende av att rätt hjälp finns tillhanda speciellt i implementeringsfasen. Fortsatta studier behövs för att göra resultaten från QMRA:n mer representativa, exempelvis genom att samla mer områdesspecifikdata. Vidare skulle det vara intressant att implementera kvalitetsövervakningsprogrammet för att utvärdera och optimera det.