Topics in Microbial Risk Assessment: Dynamic Flow Tree Process

Microbial risk assessment is emerging as a new discipline in risk assessment. A systematic approach to microbial risk assessment is presented that employs data analysis for developing parsimonious models and accounts formally for the variability and uncertainty of model inputs using analysis of variance and Monte Carlo simulation. The purpose of the paper is to raise and examine issues in conducting microbial risk assessments. The enteric pathogen Escherichia coli O157:H7 was selected as an example for this study due to its significance to public health. The framework for our work is consistent with the risk assessment components described by the National Research Council in 1983 (hazard identification; exposure assessment; dose-response assessment; and risk characterization). Exposure assessment focuses on hamburgers, cooked a range of temperatures from rare to well done, the latter typical for fast food restaurants. Features of the model include predictive microbiology components that account for random stochastic growth and death of organisms in hamburger. For dose-response modeling, Shigella data from human feeding studies were used as a surrogate for E. coli O157:H7. Risks were calculated using a threshold model and an alternative nonthreshold model. The 95% probability intervals for risk of illness for product cooked to a given internal temperature spanned five orders of magnitude for these models. The existence of even a small threshold has a dramatic impact on the estimated risk.     

Quantitative risk assessment-epidemic curve prediction model for leafy green outbreak investigation

The objective of this study was to leverage quantitative risk assessment to investigate possible root cause(s) of foodborne illness outbreaks related to Shiga toxin-producing Escherichia coli O157:H7 (STEC O157) infections in leafy greens in the United States. To this end, we developed the FDA leafy green quantitative risk assessment epidemic curve prediction model (FDA-LG QRA-EC) that simulated the lettuce supply chain. The model was used to predict the number of reported illnesses and the epidemic curve associated with lettuce contaminated with STEC O157 for a wide range of scenarios representing various contamination conditions and facility processing/sanitation practices. Model predictions were generated for fresh-cut and whole lettuce, quantifying the differing impacts of facility processing and home preparation on predicted illnesses. Our model revealed that the timespan (i.e., number of days with at least one reported illness) and the peak (i.e., day with the most predicted number of reported illnesses) of the epidemic curve of a STEC O157-lettuce outbreak were not strongly influenced by facility processing/sanitation practices and were indications of contamination pattern among incoming lettuce batches received by the facility or distribution center. Through comparisons with observed number of illnesses from recent STEC O157-lettuce outbreaks, the model identified contamination conditions on incoming lettuce heads that could result in an outbreak of similar size, which can be used to narrow down potential root cause hypotheses.     

A Predictive Model for Survival of Escherichia coli O157:H7 and Generic E. coli in Soil Amended with Untreated Animal Manure

This study aimed at developing a predictive model that captures the influences of a variety of agricultural and environmental variables and is able to predict the concentrations of enteric bacteria in soil amended with untreated Biological Soil Amendments of Animal Origin (BSAAO) under dynamic conditions. We developed and validated a Random Forest model using data from a longitudinal field study conducted in mid-Atlantic United States investigating the survival of Escherichia coli O157:H7 and generic E. coli in soils amended with untreated dairy manure, horse manure, or poultry litter. Amendment type, days of rain since the previous sampling day, and soil moisture content were identified as the most influential agricultural and environmental variables impacting concentrations of viable E. coli O157:H7 and generic E. coli recovered from amended soils. Our model results also indicated that E. coli O157:H7 and generic E. coli declined at similar rates in amended soils under dynamic field conditions.The Random Forest model accurately predicted changes in viable E. coli concentrations over time under different agricultural and environmental conditions. Our model also accurately characterized the variability of E. coli concentration in amended soil over time by providing upper and lower prediction bound estimates. Cross-validation results indicated that our model can be potentially generalized to other geographic regions and incorporated into a risk assessment for evaluating the risks associated with application of untreated BSAAO. Our model can be validated for other regions and predictive performance also can be enhanced when data sets from additional geographic regions become available.     

A Mathematical Model for Pathogen Cross‐Contamination Dynamics during the Postharvest Processing of Leafy Greens

We developed a probabilistic mathematical model for the postharvest processing of leafy greens focusing on Escherichia coli O157:H7 contamination of fresh‐cut romaine lettuce as the case study. Our model can (i) support the investigation of cross‐contamination scenarios, and (ii) evaluate and compare different risk mitigation options. We used an agent‐based modeling framework to predict the pathogen prevalence and levels in bags of fresh‐cut lettuce and quantify spread of E. coli O157:H7 from contaminated lettuce to surface areas of processing equipment. Using an unbalanced factorial design, we were able to propagate combinations of random values assigned to model inputs through different processing steps and ranked statistically significant inputs with respect to their impacts on selected model outputs. Results indicated that whether contamination originated on incoming lettuce heads or on the surface areas of processing equipment, pathogen prevalence among bags of fresh‐cut lettuce and batches was most significantly impacted by the level of free chlorine in the flume tank and frequency of replacing the wash water inside the tank. Pathogen levels in bags of fresh‐cut lettuce were most significantly influenced by the initial levels of contamination on incoming lettuce heads or surface areas of processing equipment. The influence of surface contamination on pathogen prevalence or levels in fresh‐cut bags depended on the location of that surface relative to the flume tank. This study demonstrates that developing a flexible yet mathematically rigorous modeling tool, a “virtual laboratory,” can provide valuable insights into the effectiveness of individual and combined risk mitigation options.     

Multi‐Exposure Pathway Model to Compare Escherichia coli O157 Risks and Interventions

The relative contributions of exposure pathways associated with cattle‐manure‐borne Escherichia coli O157:H7 on public health have yet to be fully characterized. A stochastic, quantitative microbial risk assessment (QMRA) model was developed to describe a hypothetical cattle farm in order to compare the relative importance of five routes of exposure, including aquatic recreation downstream of the farm, consumption of contaminated ground beef processed with limited interventions, consumption of leafy greens, direct animal contact, and the recreational use of a cattle pasture. To accommodate diverse environmental and hydrological pathways, existing QMRAs were integrated with novel and simplistic climate and field‐level submodels. The model indicated that direct animal contact presents the greatest risk of illness per exposure event during the high pathogen shedding period. However, when accounting for the frequency of exposure, using a high‐risk exposure‐receptor profile, consumption of ground beef was associated with the greatest risk of illness. Additionally, the model was used to evaluate the efficacy of hypothetical interventions affecting one or more exposure routes; concurrent evaluation of multiple routes allowed for the assessment of the combined effect of preharvest interventions across exposure pathways—which may have been previously underestimated—as well as the assessment of the effect of additional downstream interventions. This analysis represents a step towards a full evaluation of the risks associated with multiple exposure pathways; future incorporation of variability associated with environmental parameters and human behaviors would allow for a comprehensive assessment of the relative contribution of exposure pathways at the population level.     

Risk Assessment of Escherichia coli O157 Illness from Consumption of Hamburgers in the United States Made from Australian Manufacturing Beef

We analyze the risk of contracting illness due to the consumption in the United States of hamburgers contaminated with enterohemorrhagic Escherichia coli (EHEC) of serogroup O157 produced from manufacturing beef imported from Australia. We have used a novel approach for estimating risk by using the prevalence and concentration estimates of E. coli O157 in lots of beef that were withdrawn from the export chain following detection of the pathogen. For the purpose of the present assessment an assumption was that no product is removed from the supply chain following testing. This, together with a number of additional conservative assumptions, leads to an overestimation of E. coli O157‐associated illness attributable to the consumption of ground beef patties manufactured only from Australian beef. We predict 49.6 illnesses (95%: 0.0–148.6) from the 2.46 billion hamburgers made from 155,000 t of Australian manufacturing beef exported to the United States in 2012. All these illness were due to undercooking in the home and less than one illness is predicted from consumption of hamburgers cooked to a temperature of 68 °C in quick‐service restaurants.     

Quantitative Risk Assessment of Haemolytic and Uremic Syndrome Linked to O157:H7 and Non‐O157:H7 Shiga‐Toxin Producing Escherichia coli Strains in Raw Milk Soft Cheeses

Shiga‐toxin producing Escherichia coli (STEC) strains may cause human infections ranging from simple diarrhea to Haemolytic Uremic Syndrome (HUS). The five main pathogenic serotypes of STEC (MPS‐STEC) identified thus far in Europe are O157:H7, O26:H11, O103:H2, O111:H8, and O145:H28. Because STEC strains can survive or grow during cheese making, particularly in soft cheeses, a stochastic quantitative microbial risk assessment model was developed to assess the risk of HUS associated with the five MPS‐STEC in raw milk soft cheeses. A baseline scenario represents a theoretical worst‐case scenario where no intervention was considered throughout the farm‐to‐fork continuum. The risk level assessed with this baseline scenario is the risk‐based level. The impact of seven preharvest scenarios (vaccines, probiotic, milk farm sorting) on the risk‐based level was expressed in terms of risk reduction. Impact of the preharvest intervention ranges from 76% to 98% of risk reduction with highest values predicted with scenarios combining a decrease of the number of cow shedding STEC and of the STEC concentration in feces. The impact of postharvest interventions on the risk‐based level was also tested by applying five microbiological criteria (MC) at the end of ripening. The five MCs differ in terms of sample size, the number of samples that may yield a value larger than the microbiological limit, and the analysis methods. The risk reduction predicted varies from 25% to 96% by applying MCs without preharvest interventions and from 1% to 96% with combination of pre‐ and postharvest interventions.     

Impact of Microbial Ecology of Meat and Poultry Products on Predictions from Exposure Assessment Scenarios for Refrigerated Storage

A novel extension of traditional growth models for exposure assessment of food-borne microbial pathogens was developed to address the complex interactions of competing microbial populations in foods. Scenarios were designed for baseline refrigeration and mild abuse of servings of chicken broiler and ground beef Our approach employed high-quality data for microbiology of foods at production, refrigerated storage temperatures, and growth kinetics of microbial populations in culture media. Simple parallel models were developed for exponential growth of multiple pathogens and the abundant and ubiquitous nonpathogenic indigenous microbiota. Monte Carlo simulations were run for unconstrained growth and growth with the density-dependent constraint based on the "Jameson effect," inhibition of pathogen growth when the indigenous microbiota reached 10(9) counts per serving. The modes for unconstrained growth of the indigenous microbiota were 10(8), 10(10), and 10(11) counts per serving for chicken broilers, and 10(7), 10(9) and 10(11) counts per serving for ground beef at respective sites for backroom, meat case, and home refrigeration. Contamination rates and likelihoods of reaching temperatures supporting growth of the pathogens in the baseline refrigeration scenario were rare events. The unconstrained exponential growth models appeared to overestimate L. monocytogenes growth maxima for the baseline refrigeration scenario by 1500-7233% (10(6)-10(7) counts/serving) when the inhibitory effects of the indigenous microbiota are ignored. The extreme tails of the distributions for the constrained models appeared to overestimate growth maxima 110% (10(4)-10(5) counts/serving) for Salmonella spp. and 108% (6 x 10(3) counts/serving) for E. coli O157:H7 relative to the extremes of the unconstrained models. The approach of incorporating parallel models for pathogens and the indigenous microbiota into exposure assessment modeling motivates the design of validation studies to test the modeling assumptions, consistent with the analytical-deliberative process of risk analysis.     

The Economic Efficiency of Sampling Size: The Case of Beef Trim Revisited

A recent paper by Ferrier and Buzby provides a framework for selecting the sample size when testing a lot of beef trim for Escherichia coli O157:H7 that equates the averted costs of recalls and health damages from contaminated meats sold to consumers with the increased costs of testing while allowing for uncertainty about the underlying prevalence of contamination. Ferrier and Buzby conclude that the optimal sample size is larger than the current sample size. However, Ferrier and Buzby's optimization model has a number of errors, and their simulations failed to consider available evidence about the likelihood of the scenarios explored under the model. After correctly modeling microbial prevalence as dependent on portion size and selecting model inputs based on available evidence, the model suggests that the optimal sample size is zero under most plausible scenarios. It does not follow, however, that sampling beef trim for E. coli O157:H7, or food safety sampling more generally, should be abandoned. Sampling is not generally cost effective as a direct consumer safety control measure due to the extremely large sample sizes required to provide a high degree of confidence of detecting very low acceptable defect levels. Food safety verification sampling creates economic incentives for food producing firms to develop, implement, and maintain effective control measures that limit the probability and degree of noncompliance with regulatory limits or private contract specifications.     

Attribution of Human VTEC O157 Infection from Meat Products: A Quantitative Risk Assessment Approach

To address the risk posed to human health by the consumption of VTEC O157 within contaminated pork, lamb, and beef products within Great Britain, a quantitative risk assessment model has been developed. This model aims to simulate the prevalence and amount of VTEC O157 in different meat products at consumption within a single model framework by adapting previously developed models. The model is stochastic in nature, enabling both variability (natural variation between animals, carcasses, products) and uncertainty (lack of knowledge) about the input parameters to be modeled. Based on the model assumptions and data, it is concluded that the prevalence of VTEC O157 in meat products (joints and mince) at consumption is low (i.e., <0.04%). Beef products, particularly beef burgers, present the highest estimated risk with an estimated eight out of 100,000 servings on average resulting in human infection with VTEC O157.     

Modeling the Public Health System Response to a Terrorist Event in the Food Supply

We have developed a simulation model to quantify and characterize the response of the public health system and the impact of public health advisories in the event of an intentional contamination of the food supply. The model has three components: (1) definition of individual exposure over time and the outcomes of exposure, (2) definition of the geographical dispersal of exposures, and (3) response of the public health authorities to symptomatic individuals. The model explicitly considers the variation in the multiple interrelated facets of the response system, including differences among individuals' responses to exposure, variation between health care providers, and the subsequent processing of samples and confirmation of cases. To illustrate use of the model, case studies with  Escherichia coli  O157:H7 and  Salmonella  spp. in three categories of food vehicle were compared. The level of detail required to run the public health component of the model is not trivial. While some data may not be available for hazards of particular interest in potential bioterrorism events, the application of expert judgment permits comparisons between different agents, different system reactions, and other assumptions within the system. The model provides the capacity to study the impact of system changes, to compare scenarios and to quantify the benefits of improvement in terms of averted exposures and risk reduction, and constitutes a significant aid to understanding and managing these threats. Essentially, the model provides an explicit valuation of time saved in the identification and intervention in terrorist events in the food supply.     

What to Do at Low Doses: A Bounding Approach for Economic Analysis

To quantify the health benefits of environmental policies, economists generally require estimates of the reduced probability of illness or death. For policies that reduce exposure to carcinogenic substances, these estimates traditionally have been obtained through the linear extrapolation of experimental dose-response data to low-exposure scenarios as described in the U.S. Environmental Protection Agency's Guidelines for Carcinogen Risk Assessment (1986). In response to evolving scientific knowledge, EPA proposed revisions to the guidelines in 1996. Under the proposed revisions, dose-response relationships would not be estimated for carcinogens thought to exhibit nonlinear modes of action. Such a change in cancer-risk assessment methods and outputs will likely have serious consequences for how benefit-cost analyses of policies aimed at reducing cancer risks are conducted. Any tendency for reduced quantification of effects in environmental risk assessments, such as those contemplated in the revisions to EPA's cancer-risk assessment guidelines, impedes the ability of economic analysts to respond to increasing calls for benefit-cost analysis. This article examines the implications for benefit-cost analysis of carcinogenic exposures of the proposed changes to the 1986 Guidelines and proposes an approach for bounding dose-response relationships when no biologically based models are available. In spite of the more limited quantitative information provided in a carcinogen risk assessment under the proposed revisions to the guidelines, we argue that reasonable bounds on dose-response relationships can be estimated for low-level exposures to nonlinear carcinogens. This approach yields estimates of reduced illness for use in a benefit-cost analysis while incorporating evidence of nonlinearities in the dose-response relationship. As an illustration, the bounding approach is applied to the case of chloroform exposure.     

Dose Response for Infectivity of Several Strains of Campylobacter jejuni in Chickens

Although some major risk studies have been done for Campylobacter jejuni, its dose response is not well characterized. Only a single human study is available, providing dose-response information for only a single isolate. As substantial heterogeneity in infectivity has been acknowledged for other pathogens, it remains unknown how well this single study represents the dose-response relation for this pathogen. As future human challenge studies with Campylobacter are unlikely, we have to find other means of studying its infectivity. Several dose-response studies have been done using chickens as host organisms. These studies may be used to obtain quantitative information on the variation in infectivity among different isolates of this pathogen. A hierarchical Bayesian model is well suited to describe heterogeneity, and we demonstrate how the beta-Poisson model of microbial infection may be adapted to allow for within- and between-isolate variation. Isolates tested in chickens can be categorized into two distinct groups: lab-adapted and fresh isolates, and we show how the hierarchical dose-response model can be used to quantitatively describe their differences. Fresh isolates show higher colonization potential and less within-isolate variation than lab isolates. The results indicate that Campylobacter jejuni is highly infectious in chickens. Different isolates show great variation in infectivity, especially between lab and fresh isolates, indicating that human clinical (volunteer) studies on infectivity must be interpreted cautiously.     

Vero-Cytotoxigenic Escherichia coli O157 in Pasteurized Milk Containers at the Point of Retail: A Qualitative Approach to Exposure Assessment

Dairies within the United Kingdom are classified into two groups, namely, off-farm and on-farm dairies (the latter often being small scale). We propose a model for the probability of milk sold as pasteurized reaching the point of retail contaminated with Vero-cytotoxigenic Escherichia coli (VTEC) O157 from each of these two pathways. We evaluate qualitatively the exposures inherent in each, and compare and contrast the two situations. The model framework is generic, in that it can in principle be used, with the relevant data modifications, to provide a qualitative assessment of the likely exposure from milk sold as pasteurized to any potentially milk-borne pathogenic organism. Furthermore, the methodological approaches presented are widely applicable in the microbial risk assessment field. The specific example presented will be of particular interest to the UK dairy and public health communities: we conclude that the exposure potential per liter consumed from milk processed in off-farm dairies is negligible, whereas the exposure potential per liter consumed from milk processed on-farm is low, but not sufficiently small to be regarded as negligible. We also identify areas of data sparsity, which need to be addressed for quantitative risk assessment to proceed, and highlight the critical points in the pasteurized milk production chain, which, in the event of a breakdown, have the potential to increase the risk to the consumer.     

Model Averaging Using the Kullback Information Criterion in Estimating Effective Doses for Microbial Infection and Illness

Since the National Food Safety Initiative of 1997, risk assessment has been an important issue in food safety areas. Microbial risk assessment is a systematic process for describing and quantifying a potential to cause adverse health effects associated with exposure to microorganisms. Various dose-response models for estimating microbial risks have been investigated. We have considered four two-parameter models and four three-parameter models in order to evaluate variability among the models for microbial risk assessment using infectivity and illness data from studies with human volunteers exposed to a variety of microbial pathogens. Model variability is measured in terms of estimated ED01s and ED10s, with the view that these effective dose levels correspond to the lower and upper limits of the 1% to 10% risk range generally recommended for establishing benchmark doses in risk assessment. Parameters of the statistical models are estimated using the maximum likelihood method. In this article a weighted average of effective dose estimates from eight two- and three-parameter dose-response models, with weights determined by the Kullback information criterion, is proposed to address model uncertainties in microbial risk assessment. The proposed procedures for incorporating model uncertainties and making inferences are illustrated with human infection/illness dose-response data sets.     

Predictive Bayesian Microbial Dose-Response Assessment Based on Suggested Self-Organization in Primary Illness Response: Cryptosporidium parvum

The probability of illness caused by very low doses of pathogens cannot generally be tested due to the numbers of subjects that would be needed, though such assessments of illness dose response are needed to evaluate drinking water standards. A predictive Bayesian dose-response assessment method was proposed previously to assess the unconditional probability of illness from available information and avoid the inconsistencies of confidence-based approaches. However, the method uses knowledge of the conditional dose-response form, and this form is not well established for the illness endpoint. A conditional parametric dose-response function for gastroenteric illness is proposed here based on simple numerical models of self-organized host-pathogen systems and probabilistic arguments. In the models, illnesses terminate when the host evolves by processes of natural selection to a self-organized critical value of wellness. A generalized beta-Poisson illness dose-response form emerges for the population as a whole. Use of this form is demonstrated in a predictive Bayesian dose-response assessment for cryptosporidiosis. Results suggest that a maximum allowable dose of 5.0 x 10(-7) oocysts/exposure (e.g., 2.5 x 10(-7) oocysts/L water) would correspond with the original goals of the U.S. Environmental Protection Agency Surface Water Treatment Rule, considering only primary illnesses resulting from Poisson-distributed pathogen counts. This estimate should be revised to account for non-Poisson distributions of Cryptosporidium parvum in drinking water and total response, considering secondary illness propagation in the population.     

Application of a Risk Analysis Tool to Middle East Respiratory Syndrome Coronavirus (MERS-CoV) Outbreak in Saudi Arabia

The Grunow–Finke assessment tool (GFT) is an accepted scoring system for determining likelihood of an outbreak being unnatural in origin. Considering its high specificity but low sensitivity, a modified Grunow–Finke tool (mGFT) has been developed with improved sensitivity. The mGFT has been validated against some past disease outbreaks, but it has not been applied to ongoing outbreaks. This study is aimed to score the outbreak of Middle East respiratory syndrome coronavirus (MERS-CoV) in Saudi Arabia using both the original GFT and mGFT. The publicly available data on human cases of MERS-CoV infections reported in Saudi Arabia (2012–2018) were sourced from the FluTrackers, World Health Organization, Saudi Ministry of Health, and published literature associated with MERS outbreaks investigations. The risk assessment of MERS-CoV in Saudi Arabia was analyzed using the original GFT and mGFT criteria, algorithms, and thresholds. The scoring points for each criterion were determined by three researchers to minimize the subjectivity. The results showed 40 points of total possible 54 points using the original GFT (likelihood: 74%), and 40 points of a total possible 60 points (likelihood: 67%) using the mGFT, both tools indicating a high likelihood that human MERS-CoV in Saudi Arabia is unnatural in origin. The findings simply flag unusual patterns in this outbreak, but do not prove unnatural etiology. Proof of bioattacks can only be obtained by law enforcement and intelligence agencies. This study demonstrated the value and flexibility of the mGFT in assessing and predicting the risk for an ongoing outbreak with simple criteria.     

The Beta Poisson Dose-Response Model Is Not a Single-Hit Model

The choice of a dose-response model is decisive for the outcome of quantitative risk assessment. Single-hit models have played a prominent role in dose-response assessment for pathogenic microorganisms, since their introduction. Hit theory models are based on a few simple concepts that are attractive for their clarity and plausibility. These models, in particular the Beta Poisson model, are used for extrapolation of experimental dose-response data to low doses, as are often present in drinking water or food products. Unfortunately, the Beta Poisson model, as it is used throughout the microbial risk literature, is an approximation whose validity is not widely known. The exact functional relation is numerically complex, especially for use in optimization or uncertainty analysis. Here it is shown that although the discrepancy between the Beta Poisson formula and the exact function is not very large for many data sets, the differences are greatest at low doses--the region of interest for many risk applications. Errors may become very large, however, in the results of uncertainty analysis, or when the data contain little low-dose information. One striking property of the exact single-hit model is that it has a maximum risk curve, limiting the upper confidence level of the dose-response relation. This is due to the fact that the risk cannot exceed the probability of exposure, a property that is not retained in the Beta Poisson approximation. This maximum possible response curve is important for uncertainty analysis, and for risk assessment of pathogens with unknown properties.     

Time-Dose-Response Models for Microbial Risk Assessment

While microbial risk assessment (MRA) has been used for over 25 years, traditional dose-response analysis has only predicted the overall risk of adverse consequences from exposure to a given dose. An important issue for consequence assessment from bioterrorist and other microbiological exposure is the distribution of cases over time due to the initial exposure. In this study, the classical exponential and beta-Poisson dose-response models were modified to include exponential-power dependency of time post inoculation (TPI) or its simplified form, exponential-reciprocal dependency of TPI, to quantify the time of onset of an effect presumably associated with the kinetics of in vivo bacterial growth. Using the maximum likelihood estimation approach, the resulting time-dose-response models were found capable of providing statistically acceptable fits to all tested pooled animal survival dose-response data. These new models can consequently describe the development of animal infectious response over time and represent observed responses fairly accurately. This is the first study showing that a time-dose-response model can be developed for describing infections initiated by various pathogens. It provides an advanced approach for future MRA frameworks.     

Recognizing Structural Nonidentifiability: When Experiments Do Not Provide Information About Important Parameters and Misleading Models Can Still Have Great Fit

In the quest to model various phenomena, the foundational importance of parameter identifiability to sound statistical modeling may be less well appreciated than goodness of fit. Identifiability concerns the quality of objective information in data to facilitate estimation of a parameter, while nonidentifiability means there are parameters in a model about which the data provide little or no information. In purely empirical models where parsimonious good fit is the chief concern, nonidentifiability (or parameter redundancy) implies overparameterization of the model. In contrast, nonidentifiability implies underinformativeness of available data in mechanistically derived models where parameters are interpreted as having strong practical meaning. This study explores illustrative examples of structural nonidentifiability and its implications using mechanistically derived models (for repeated presence/absence analyses and dose–response of Escherichia coli O157:H7 and norovirus) drawn from quantitative microbial risk assessment. Following algebraic proof of nonidentifiability in these examples, profile likelihood analysis and Bayesian Markov Chain Monte Carlo with uniform priors are illustrated as tools to help detect model parameters that are not strongly identifiable. It is shown that identifiability should be considered during experimental design and ethics approval to ensure generated data can yield strong objective information about all mechanistic parameters of interest. When Bayesian methods are applied to a nonidentifiable model, the subjective prior effectively fabricates information about any parameters about which the data carry no objective information. Finally, structural nonidentifiability can lead to spurious models that fit data well but can yield severely flawed inferences and predictions when they are interpreted or used inappropriately.