Graphical models and Bayesian domains in risk modelling: application in microbiological risk assessment

Water, Water Everywhere, but Every Drop Unique: Challenges in the Science to Understand the Role of Contaminants of Emerging Concern in the Management of Drinking Water Supplies

The protection and management of water resources continues to be challenged by multiple and ongoing factors such as shifts in demographic, social, economic, and public health requirements. Physical limitations placed on access to potable supplies include natural and human-caused factors such as aquifer depletion, aging infrastructure, saltwater intrusion, floods, and drought. These factors, although varying in magnitude, spatial extent, and timing, can exacerbate the potential for contaminants of concern (CECs) to be present in sources of drinking water, infrastructure, premise plumbing and associated tap water. This monograph examines how current and emerging scientific efforts and technologies increase our understanding of the range of CECs and drinking water issues facing current and future populations. It is not intended to be read in one sitting, but is instead a starting point for scientists wanting to learn more about the issues surrounding CECs. This text discusses the topical evolution CECs over time (Section 1), improvements in measuring chemical and microbial CECs, through both analysis of concentration and toxicity (Section 2) and modeling CEC exposure and fate (Section 3), forms of treatment effective at removing chemical and microbial CECs (Section 4), and potential for human health impacts from exposure to CECs (Section 5). The paper concludes with how changes to water quantity, both scarcity and surpluses, could affect water quality (Section 6). Taken together, these sections document the past 25 years of CEC research and the regulatory response to these contaminants, the current work to identify and monitor CECs and mitigate exposure, and the challenges facing the future.

Listeria monocytogenes risk assessment on cold smoked and salt-cured fishery products in Finland - A repeated exposure model

Listeria monocytogenes causes severe consequences especially for persons belonging to risk groups. Finland is among the countries with highest number of listeriosis cases in the European Union. Although most reported cases appear to be sporadic and the maximum bacterial concentration of 100 cfu/g is not usually exceeded at retail, cold smoked and salt-cured fish products have been noted as those products with great risk especially for the elderly. In order to investigate the listeriosis risk more carefully, an exposure assessment was developed, and laboratory results for cold smoked and salt-cured salmon products were exploited. L. monocytogenes exposure was modeled for consumers in two age groups, the elderly population as a risk group and the working-age population as a reference. Incidence was assessed by estimating bacterial growth in the food products at three temperatures. Bayesian estimation of the risk was based on bacterial occurrence and product consumption data and epidemiological population data. The model builds on a two-state Markov chain describing repeated consumption on consecutive days. The cumulative exposure is probabilistically governed by the daily decreasing likelihood of continued consumption and the increasing bacterial concentrations due to growth. The population risk was then predicted with a Poisson distribution accounting for the daily probabilities of purchasing a contaminated product and the cumulative total probability of infection from its use. According to the model presented in this article, elderly Finns are at a greater risk of acquiring listeriosis than healthy adults. The risk for the elderly does not fully diminish even if the products have been stored at the recommended temperature (between 0 and 3 °C). It can be concluded that the stage after retail, i.e. food handling and storage by consumer or professional kitchens, is essential to protection against listeriosis. The estimation model provides means for assessing the joint impacts of these effects.

Quantitative risk assessment of Listeria monocytogenes in bulk cooked meat from production to consumption in China: a Bayesian approach

BACKGROUND

To estimate the public health risk related to cooked meat in bulk products contaminated with Listeria monocytogenes, a generic Bayesian network (BN) risk-assessment model was developed to simulate influencing factors and processes of products from the industry level to the consumer level. To quantify the model, parameter values of prior distributions were acquired from the literature, websites, and expert opinions. Using the Markov chain Monte Carlo (MCMC) simulation approach, posterior probability distributions were calculated according to the incorporated evidence, which allowed us to predict various risks affected by processing variability from production to consumption.

RESULTS

The average risks of listeriosis from consuming cooked meat in bulk products are 8.40 × 10^-7 , 2.58 × 10^-8 , 8.24 × 10^-7 , and 1.05 × 10^-6 per meal for children, young people, elderly people, and pregnant women, respectively. The estimated mean number of listeriosis cases is 5 per 100 000 people per year in China.

CONCLUSION

Although only a conceptual BN model is given, it manifests the principles and characteristics of mathematical methods. The BN model can also provide significant benefits for quantitative risk assessment by incorporating all available data and by updating beliefs. © 2018 Society of Chemical Industry.

Bayesian model for tracing Salmonella contamination in the pig feed chain

Salmonella infections in pigs are in most cases asymptomatic, posing a risk of salmonellosis for pork consumers. Salmonella can transmit to pigs from various sources, including contaminated feed. We present an approach for quantifying the risk to pigs from contaminations in the feed chain, based on a Bayesian model. The model relies on Salmonella surveillance data and other information from surveys, reports, registries, statistics, legislation and literature regarding feed production and pig farming. Uncertainties were probabilistically quantified by synthesizing evidence from the available information over a categorically structured flow chain of ingredients mixed for feeds served to pigs. Model based probability for infection from feeds together with Salmonella subtyping data, were used to estimate the proportion of Salmonella infections in pigs attributable to feed. The results can be further used in assessments considering the human health risk linked to animal feed via livestock. The presented methods can be used to predict the effect of changes in the feed chain, and they are generally applicable to other animals and pathogens.

Campylobacter QMRA: A Bayesian Estimation of Prevalence and Concentration in Retail Foods Under Clustering and Heavy Censoring

A Bayesian statistical temporal-prevalence-concentration model (TPCM) was built to assess the prevalence and concentration of pathogenic campylobacter species in batches of fresh chicken and turkey meat at retail. The data set was collected from Finnish grocery stores in all the seasons of the year. Observations at low concentration levels are often censored due to the limit of determination of the microbiological methods. This model utilized the potential of Bayesian methods to borrow strength from related samples in order to perform under heavy censoring. In this extreme case the majority of the observed batch-specific concentrations was below the limit of determination. The hierarchical structure was included in the model in order to take into account the within-batch and between-batch variability, which may have a significant impact on the sample outcome depending on the sampling plan. Temporal changes in the prevalence of campylobacter were modeled using a Markovian time series. The proposed model is adaptable for other pathogens if the same type of data set is available. The computation of the model was performed using OpenBUGS software.

Utility of Bayesian networks in QMRA-based evaluation of risk reduction options for recycled water

BACKGROUND

Quantitative microbial risk assessment (QMRA), the current method of choice for evaluating human health risks associated with disease-causing microorganisms, is often constrained by issues such as availability of required data, and inability to incorporate the multitude of factors influencing risk. Bayesian networks (BNs), with their ability to handle data paucity, combine quantitative and qualitative information including expert opinions, and ability to offer a systems approach to characterisation of complexity, are increasingly recognised as a powerful, flexible tool that overcomes these limitations.

OBJECTIVES

We present a QMRA expressed as a Bayesian network (BN) in a wastewater reuse context, with the objective of demonstrating the utility of the BN method in health risk assessments, particularly for evaluating a range of exposure and risk mitigation scenarios. As a case study, we examine the risk of norovirus infection associated with wastewater-irrigated lettuce.

METHODS

A Bayesian network was developed following a QMRA approach, using published data, and reviewed by domain experts using a participatory process.

DISCUSSION

Employment of a BN facilitated rapid scenario evaluations, risk minimisation, and predictive comparisons. The BN supported exploration of conditions required for optimal outcomes, as well as investigation of the effect on the reporting nodes of changes in 'upstream' conditions. A significant finding was the indication that if maximum post-treatment risk mitigation measures were implemented, there was a high probability (0.84) of a low risk of infection regardless of fluctuations in other variables, including norovirus concentration in treated wastewater.

CONCLUSION

BNs are useful in situations where insufficient empirical data exist to satisfy QMRA requirements and they are exceptionally suited to the integration of risk assessment and risk management in the QMRA context. They allow a comprehensive visual appraisal of major influences in exposure pathways, and rapid interactive risk assessment in multifaceted water reuse scenarios.

Modelling microbial health risk of wastewater reuse: A systems perspective

There is a widespread need for the use of quantitative microbial risk assessment (QMRA) to determine reclaimed water quality for specific uses, however neither faecal indicator levels nor pathogen concentrations alone are adequate for assessing exposure health risk. The aim of this study was to build a conceptual model representing factors contributing to the microbiological health risks of reusing water treated in maturation ponds. This paper describes the development of an unparameterised model that provides a visual representation of theoretical constructs and variables of interest. Information was collected from the peer-reviewed literature and through consultation with experts from regulatory authorities and academic disciplines. In this paper we explore how, considering microbial risk as a modular system, following the QMRA framework enables incorporation of the many factors influencing human exposure and dose response, to better characterise likely human health impacts. By using and expanding upon the QMRA framework we deliver new insights into this important field of environmental exposures. We present a conceptual model of health risk of microbial exposure which can be used for maturation ponds and, more importantly, as a generic tool to assess health risk in diverse wastewater reuse scenarios.

Beyond QMRA: Modelling microbial health risk as a complex system using Bayesian networks

BACKGROUND

Quantitative microbial risk assessment (QMRA) is the current method of choice for determining the risk to human health from exposure to microorganisms of concern. However, current approaches are often constrained by the availability of required data, and may not be able to incorporate the many varied factors that influence this risk. Systems models, based on Bayesian networks (BNs), are emerging as an effective complementary approach that overcomes these limitations.

OBJECTIVES

This article aims to provide a comparative evaluation of the capabilities and challenges of current QMRA methods and BN models, and a scoping review of recent published articles that adopt the latter for microbial risk assessment. Pros and cons of systems approaches in this context are distilled and discussed.

METHODS

A search of the peer-reviewed literature revealed 15 articles describing BNs used in the context of QMRAs for foodborne and waterborne pathogens. These studies were analysed in terms of their application, uses and benefits in QMRA.

DISCUSSION

The applications were notable in their diversity. BNs were used to make predictions, for scenario assessment, risk minimisation, to reduce uncertainty and to separate uncertainty and variability. Most studies focused on a segment of the exposure pathway, indicating the broad potential for the method in other QMRA steps. BNs offer a number of useful features to enhance QMRA, including transparency, and the ability to deal with poor quality data and support causal reasoning.

CONCLUSION

The method has significant untapped potential to describe the complex relationships between microbial environmental exposures and health.

Status and future of Quantitative Microbiological Risk Assessment in China

Since the implementation of the Food Safety Law of the People's Republic of China in 2009 use of Quantitative Microbiological Risk Assessment (QMRA) has increased. QMRA is used to assess the risk posed to consumers by pathogenic bacteria which cause the majority of foodborne outbreaks in China. This review analyses the progress of QMRA research in China from 2000 to 2013 and discusses 3 possible improvements for the future. These improvements include planning and scoping to initiate QMRA, effectiveness of microbial risk assessment utility for risk management decision making, and application of QMRA to establish appropriate Food Safety Objectives.

Confidence limits for prevalence of disease adjusted for estimated sensitivity and specificity

Prevalence of a disease is usually assessed by diagnostic tests that may produce false results. Rogan and Gladen (1978) described a method to estimate the true prevalence correcting for sensitivity and specificity of the diagnostic procedure, and Reiczigel et al. (2010) provided exact confidence intervals for the true prevalence assuming sensitivity and specificity were known. In this paper we propose a new method to construct approximate confidence intervals for the true prevalence when sensitivity and specificity are estimated from independent samples. To improve coverage we applied an adjustment similar to that described in Agresti and Coull (1998). According to an extensive simulation study the new confidence intervals maintain the nominal level fairly well even for sample sizes as small as 30; mini-mum coverage is above 88%, 93%, and 98% at nominal 90%, 95%, and 99%, respectively. We illustrate the advantages of the proposed method with real-life applications.