Microbial dose response modeling: past, present, and future

Multilevel model for airborne transmission of foot-and-mouth disease applied to Swedish livestock

The foot-and-mouth disease is an ever-present hazard to the livestock industry due to the huge economic consequences following an outbreak that necessitates culling of possibly infected animals in vast numbers. The disease is highly contagious and previous epizootics have shown that it spreads by many routes. One such route is airborne transmission, which has been investigated in this study by means of a detailed multilevel model that includes all scales of an outbreak. Local spread within an infected farm is described by a stochastic compartment model while the spread between farms is quantified by atmospheric dispersion simulations using a network representation of the set of farms. The model was applied to the Swedish livestock industry and the risk for an epizootic outbreak in Sweden was estimated using the basic reproduction number of each individual livestock-holding farm as the endpoint metric. The study was based on comprehensive official data sets for both the current livestock holdings and regional meteorological conditions. Three species of farm animals are susceptible to the disease and are present in large numbers: cattle, pigs and sheep. These species are all included in this study using their individual responses and consequences to the disease. It was concluded that some parts of southern Sweden are indeed preconditioned to harbor an airborne epizootic, while the sparse farm population of the north renders such events unlikely to occur there. The distribution of the basic reproduction number spans over several orders of magnitudes with low risk of disease spread from the majority of the farms while some farms may act as very strong disease transmitters. The results may serve as basic data in the planning of the national preparedness for this type of events.

Pathogen transmission from vaccinated hosts can cause dose-dependent reduction in virulence

Many livestock and human vaccines are leaky because they block symptoms but do not prevent infection or onward transmission. This leakiness is concerning because it increases vaccination coverage required to prevent disease spread and can promote evolution of increased pathogen virulence. Despite leakiness, vaccination may reduce pathogen load, affecting disease transmission dynamics. However, the impacts on post-transmission disease development and infectiousness in contact individuals are unknown. Here, we use transmission experiments involving Marek disease virus (MDV) in chickens to show that vaccination with a leaky vaccine substantially reduces viral load in both vaccinated individuals and unvaccinated contact individuals they infect. Consequently, contact birds are less likely to develop disease symptoms or die, show less severe symptoms, and shed less infectious virus themselves, when infected by vaccinated birds. These results highlight that even partial vaccination with a leaky vaccine can have unforeseen positive consequences in controlling the spread and symptoms of disease.

A Systematic Review of Beef Meat Quantitative Microbial Risk Assessment Models

Each year in Europe, meat is associated with 2.3 million foodborne illnesses, with a high contribution from beef meat. Many of these illnesses are attributed to pathogenic bacterial contamination and inadequate operations leading to growth and/or insufficient inactivation occurring along the whole farm-to-fork chain. To ensure consumer health, decision-making processes in food safety rely on Quantitative Microbiological Risk Assessment (QMRA) with many applications in recent decades. The present study aims to conduct a critical analysis of beef QMRAs and to identify future challenges. A systematic approach, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, was used to collate beef QMRA models, identify steps of the farm-to-fork chain considered, and analyze inputs and outputs included as well as modelling methods. A total of 2343 articles were collected and 67 were selected. These studies focused mainly on western countries and considered Escherichia coli (EHEC) and Salmonella spp. pathogens. Future challenges were identified and included the need of whole-chain assessments, centralization of data collection processes, and improvement of model interoperability through harmonization. The present analysis can serve as a source of data and information to inform QMRA framework for beef meat and will help the scientific community and food safety authorities to identify specific monitoring and research needs.

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

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.

A quantitative risk assessment method for synthetic biology products in the environment

The need to prevent possible adverse environmental health impacts resulting from synthetic biology (SynBio) products is widely acknowledged in both the SynBio risk literature and the global regulatory community. To-date, however, discussions of potential risks of SynBio products have been largely speculative, and the limited attempts to characterize the risks of SynBio products have been non-uniform and entirely qualitative. As the SynBio discipline continues to accelerate and bring forth novel, highly-engineered life forms, a standardized risk assessment framework will become critical for ensuring that the environmental risks of these products are characterized in a consistent, reliable, and objective manner that incorporates all SynBio-unique risk factors. In their current forms, established risk assessment frameworks - including those that address traditional genetically modified organisms - fall short of the features required of this standard framework. To address this gap, we propose the Quantitative Risk Assessment Method for Synthetic Biology Products (QRA-SynBio) - an incremental build on established risk assessment methodologies that supplements traditional paradigms with the SynBio risk factors that are currently absent, and necessitates quantitative analysis for more transparent and objective risk characterizations. We demonstrate through a hypothetical case study that the proposed framework facilitates defensible quantification of the environmental risks of SynBio products in both foreseeable and hypothetical use scenarios. Additionally, we show how the quantitative nature of the proposed method can promote increased experimental investigation into the true likelihood of hazard and exposure parameters and highlight the most sensitive parameters where uncertainty should be reduced, ultimately leading to more targeted SynBio risk research and yielding more precise characterizations of risk.

A general method for multiscale modelling of vector-borne disease systems

The inability to develop multiscale models which can describe vector-borne disease systems in terms of the complete pathogen life cycle which represents multiple targets for control has hindered progress in our efforts to control, eliminate and even eradicate these multi-host infections. This is because it is currently not easy to determine precisely where and how in the life cycles of vector-borne disease systems the key constrains which are regarded as crucial in regulating pathogen population dynamics in both the vertebrate host and vector host operate. In this article, we present a general method for development of multiscale models of vector-borne disease systems which integrate the within-host and between-host scales for the two hosts (a vertebrate host and a vector host) that are implicated in vector-borne disease dynamics. The general multiscale modelling method is an extension of our previous work on multiscale models of infectious disease systems which established a basic science and accompanying theory of how pathogen population dynamics at within-host scale scales up to between-host scale and in turn how it scales down from between-host scale to within-host scale. Further, the general method is applied to multiscale modelling of human onchocerciasis-a vector-borne disease system which is sometimes called river blindness as a case study.

A novel screening tool for the health risk in recreational waters near estuary: The Carrying Capacity indicator

The present study aims to provide a conceptual framework to help practitioners to improve the quality of recreational waters near estuary, which may be affected by untreated wastewater from Combined Sewer Overflows (CSOs). When CSOs are activated, the concentration of bacteria (e.g., Enterococci and E. coli) in estuary increases, thereby resulting in a potential health threat to swimmers. Here, the bacterial exposure is evaluated using physically-based stochastic model for contaminant transport, while human health risk is determined by Quantitative Microbial Risk Assessment (QMRA). Based on human health risk framework, we quantify the Carrying Capacity (CC) of the recreational water body. Such an indicator is defined as the number of swimming individuals that can be sustained in a beach resort with an acceptable risk threshold. The CC increases by dilution processes and by reduction of the source concentration, which in turn depends on the improvements in the sewage system. The presented approach can be a useful screening tool for policy-makers and other stakeholders, thereby providing a potential solution to the trade-off between economic development and the sustainable ecosystem in coastal areas.

Construction of A New Dose-Response Model for Staphylococcus aureus Considering Growth and Decay Kinetics on Skin

In order to determine the relationship between an exposure dose of Staphylococcus aureus (S. aureus) on the skin and the risk of infection, an understanding of the bacterial growth and decay kinetics is very important. Models are essential tools for understanding and predicting bacterial kinetics and are necessary to predict the dose of organisms post-exposure that results in a skin infection. One of the challenges in modeling bacterial kinetics is the estimation of model parameters, which can be addressed using an inverse problem approach. The objective of this study is to construct a microbial kinetic model of S. aureus on human skin and use the model to predict concentrations of S. aureus that result in human infection. In order to model the growth and decay of S. aureus on skin, a Gompertz inactivation model was coupled with a Gompertz growth model. A series of analyses, including ordinary least squares regression, scaled sensitivity coefficient analysis, residual analysis, and parameter correlation analysis were conducted to estimate the parameters and to describe the model uncertainty. Based on these analyses, the proposed model parameters were estimated with high accuracy. The model was then used to develop a new dose-response model for S. aureus using the exponential dose–response model. The new S. aureus model has an optimized k parameter equivalent to 8.05 × 10⁻⁸ with 95^th percentile confidence intervals between 6.46 × 10⁻⁸ and 1.00 × 10⁻⁷.

Mechanistic dose-response modelling of animal challenge data shows that intact skin is a crucial barrier to leptospiral infection

Leptospirosis is a widespread and potentially life-threatening zoonotic disease caused by spirochaetes of the genus Leptospira. Humans become infected primarily via contact with environmental reservoirs contaminated by the urine of shedding mammalian hosts. Populations in high transmission settings, such as urban slums and subsistence farming communities, are exposed to low doses of Leptospira on a daily basis. Under these conditions, numerous factors determine whether infection occurs, including the route of exposure and inoculum dose. Skin wounds and abrasions are risk factors for leptospirosis, but it is not known whether broken skin is necessary for spillover, or if low-dose exposures to intact skin and mucous membranes can also cause infection. To establish a quantitative relationship between dose, route and probability of infection, we performed challenge experiments in hamsters and rats, developed mechanistic dose-response models representing the spatial dynamics of within-host infection and persistence, and fitted models to experimental data. Results show intact skin is a strong barrier against infection, and that broken skin is the predominant route by which low-dose environmental exposures cause infection. These results identify skin integrity as a bottleneck to spillover of Leptospira and underscore the importance of barrier interventions in the prevention of leptospirosis. This article is part of the theme issue 'Dynamic and integrative approaches to understanding pathogen spillover'.

Safely Managed Hygiene: A Risk-Based Assessment of Handwashing Water Quality

Sustainable Development Goal (SDG) Indicator 6.2.1 requires household handwashing facilities to have soap and water, but there are no guidelines for handwashing water quality. In contrast, drinking water quality guidelines are defined: water must be "free from contamination" to be defined as "safely managed" (SDG Indicator 6.1.1). We modeled the hypothesized mechanism of infection due to contaminated handwashing water to inform risk-based guidelines for microbial quality of handwashing water. We defined two scenarios that should not occur: (1) if handwashing caused fecal contamination, indicated using Escherichia coli, on a person's hands to increase rather than decrease and (2) if hand-to-mouth contacts following handwashing caused an infection risk greater than an acceptable threshold. We found water containing <1000 E. coli colony-forming units (CFU) per 100 mL removes E. coli from hands with>99.9% probability. However, for the annual probability of infection to be <1:1000, handwashing water must contain <2 × 10^-6 focus-forming units of rotavirus, <1 × 10^-4 CFU of Vibrio cholerae, and <9 × 10^-6 Cryptosporidium oocysts per 100 mL. Our model suggests that handwashing with nonpotable water will generally reduce fecal contamination on hands but may be unable to lower the annual probability of infection risks from hand-to-mouth contacts below 1:1000.

Banked Human Milk and Quantitative Risk Assessment of Bacillus cereus Infection in Premature Infants: A Simulation Study

Background

Banked human milk (BHM) offers potential health benefits to premature babies. BHM is pasteurized to mitigate infectious risks, but pasteurization is ineffective against sporulating bacteria such as Bacillus cereus. Sepsis related to Bacillus cereus in premature infants is severe and can often be fatal. Even if a causal link has never been established, BHM has been suggested as a potential source of infection in premature infants.

Objective

Our aim was to estimate the potential risk of Bacillus cereus infection in preterm infants caused by the ingestion of contaminated pasteurized BHM using different post-pasteurization release criteria (i.e., 9 sampling of 100 microliters versus the HMBANA guideline of 1 sampling of 100 microliters per pool).

Methods

In the absence of scientific evidence regarding the risk of Bacillus cereus infection by the ingestion of BHM in premature infants, risk assessment using Monte Carlo simulation with the exponential dose-response model was performed. Three scenarios of infectious risk (annual incidence rate of 0.01%, 0.13%, and 0.2%) with 18 variations of the B. cereus virulent dose (from 0.5 CFU/ml to 200 CFU/ml) were simulated.

Results

The mean risk differential between the two methods of post-pasteurization bacteriological control for realistic infectious doses of 30 to 200 CFU/ml ranges from 0.036 to 0.0054, 0.47 to 0.070, and 0.72 to 0.11 per million servings, for each of the three scenarios.

Conclusion

Simulation highlights the very small risk of Bacillus cereus infection following the ingestion of pasteurized BHM, even in the worst case scenarios, and suggests that a 100-microliter sample for post-pasteurization culture is sufficient.

Spatial dynamic assessment of health risks for urban river cruises

River cruising ships move along river courses, and thus health risks to passengers may vary spatially due to the accidental exposure of river fecal pollution. This study performed a spatial dynamic assessment of health risks for river cruises in the highly urbanized Tamsui River Basin. First, the spatial distributions of river Escherichia coli (E. coli) were probabilistically characterized using indicator kriging (IK). Moreover, the current river cruise information was surveyed to obtain cruise routes and transit times. Then, to explore the parametric uncertainty of quantitative microbial risk assessment (QMRA), the ingestion rate (IR) for boating was determined using Monte Carlo simulation (MCS). Moreover, river E. coli distributions were estimated using nonparametric MCS according to multi-threshold IK estimates. Eventually, after combining the distribution of the joint probability of the IR and E. coli in QMRA, the β-Poisson dose-response function was adopted to analyze risks to river cruise passengers at discretized segments of cruise routes. Health risks to river cruise passengers were integrated at the discretized segments to explore suitable recreational strategies for river cruises. The research results indicate that all health risks do not exceed a daily target level of 8 illnesses per 1000 exposures for single-trip cruise routes. However, health risks to passengers can exceed this level for round-trip cruise routes along highly polluted urban river courses.

Microbiota and Dose Response: Evolving Paradigm of Health Triangle

SRA Dose-Response and Microbial Risk Analysis Specialty Groups jointly sponsored symposia that addressed the intersections between the "microbiome revolution" and dose response. Invited speakers presented on innovations and advances in gut and nasal microbiota (normal microbial communities) in the first decade after the Human Microbiome Project began. The microbiota and their metabolites are now known to influence health and disease directly and indirectly, through modulation of innate and adaptive immune systems and barrier function. Disruption of healthy microbiota is often associated with changes in abundance and diversity of core microbial species (dysbiosis), caused by stressors including antibiotics, chemotherapy, and disease. Nucleic-acid-based metagenomic methods demonstrated that the dysbiotic host microbiota no longer provide normal colonization resistance to pathogens, a critical component of innate immunity of the superorganism. Diverse pathogens, probiotics, and prebiotics were considered in human and animal models (in vivo and in vitro). Discussion included approaches for design of future microbial dose-response studies to account for the presence of the indigenous microbiota that provide normal colonization resistance, and the absence of the protective microbiota in dysbiosis. As NextGen risk analysis methodology advances with the "microbiome revolution," a proposed new framework, the Health Triangle, may replace the old paradigm based on the Disease Triangle (focused on host, pathogen, and environment) and germophobia. Collaborative experimental designs are needed for testing hypotheses about causality in dose-response relationships for pathogens present in our environments that clearly compete in complex ecosystems with thousands of bacterial species dominating the healthy superorganism.

Advancing risk assessment: mechanistic dose-response modelling of Listeria monocytogenes infection in human populations

The utility of characterizing the effects of strain variation and individual/subgroup susceptibility on dose-response outcomes has motivated the search for new approaches beyond the popular use of the exponential dose-response model for listeriosis. While descriptive models can account for such variation, they have limited power to extrapolate beyond the details of particular outbreaks. By contrast, this study exhibits dose-response relationships from a mechanistic basis, quantifying key biological factors involved in pathogen-host dynamics. An efficient computational algorithm and geometric interpretation of the infection pathway are developed to connect dose-response relationships with the underlying bistable dynamics of the model. Relying on in vitro experiments as well as outbreak data, we estimate plausible parameters for the human context. Despite the presence of uncertainty in such parameters, sensitivity analysis reveals that the host response is most influenced by the pathogen-immune system interaction. In particular, we show how variation in this interaction across a subgroup of the population dictates the shape of dose-response curves. Finally, in terms of future experimentation, our model results provide guidelines and highlight vital aspects of the interplay between immune cells and particular strains of Listeria monocytogenes that should be examined.

Human Dose-Response Data for Francisella tularensis and a Dose- and Time-Dependent Mathematical Model of Early-Phase Fever Associated with Tularemia After Inhalation Exposure

Military health risk assessors, medical planners, operational planners, and defense system developers require knowledge of human responses to doses of biothreat agents to support force health protection and chemical, biological, radiological, nuclear (CBRN) defense missions. This article reviews extensive data from 118 human volunteers administered aerosols of the bacterial agent Francisella tularensis, strain Schu S4, which causes tularemia. The data set includes incidence of early-phase febrile illness following administration of well-characterized inhaled doses of F. tularensis. Supplemental data on human body temperature profiles over time available from de-identified case reports is also presented. A unified, logically consistent model of early-phase febrile illness is described as a lognormal dose-response function for febrile illness linked with a stochastic time profile of fever. Three parameters are estimated from the human data to describe the time profile: incubation period or onset time for fever; rise time of fever; and near-maximum body temperature. Inhaled dose-dependence and variability are characterized for each of the three parameters. These parameters enable a stochastic model for the response of an exposed population through incorporation of individual-by-individual variability by drawing random samples from the statistical distributions of these three parameters for each individual. This model provides risk assessors and medical decisionmakers reliable representations of the predicted health impacts of early-phase febrile illness for as long as one week after aerosol exposures of human populations to F. tularensis.

Characterizing health risks associated with recreational swimming at Taiwanese beaches by using quantitative microbial risk assessment

Taiwan is surrounded by oceans, and therefore numerous pleasure beaches attract millions of tourists annually to participate in recreational swimming activities. However, impaired water quality because of fecal pollution poses a potential threat to the tourists' health. This study probabilistically characterized the health risks associated with recreational swimming engendered by waterborne enterococci at 13 Taiwanese beaches by using quantitative microbial risk assessment. First, data on enterococci concentrations at coastal beaches monitored by the Taiwan Environmental Protection Administration were reproduced using nonparametric Monte Carlo simulation (MCS). The ingestion volumes of recreational swimming based on uniform and gamma distributions were subsequently determined using MCS. Finally, after the distribution combination of the two parameters, the beta-Poisson dose-response function was employed to quantitatively estimate health risks to recreational swimmers. Moreover, various levels of risk to recreational swimmers were classified and spatially mapped to explore feasible recreational and environmental management strategies at the beaches. The study results revealed that although the health risks associated with recreational swimming did not exceed an acceptable benchmark of 0.019 illnesses daily at all beaches, they approached to this benchmark at certain beaches. Beaches with relatively high risks are located in Northwestern Taiwan owing to the current movements.

Assessment and risk modeling of airborne enteric viruses emitted from wastewater reused for irrigation

Reclamation of wastewater (WW) for irrigation, after treatment represents a challenge that could alleviate pressure on water resources and address the increasing demand for agriculture. However, the risks to human health must be assessed, particularly those related to human enteric viruses that resist standard treatments in most wastewater treatment plants (WWTP). The risks associated with exposure to viral bioaerosols near WWTP and near agricultural plots irrigated with WW are poorly documented. The objectives of this study were to 1) better characterize human enteric viruses found in bioaerosols near a "standard WWTP" and over fields irrigated with treated WW and 2) propose a numeric model to assess the health risk to populations located close to the irrigated areas, with particular attention to norovirus, which is responsible for most viral gastroenteritis in France. Water and air samples were collected at various locations in the largest French WW-irrigated site near Clermont-Ferrand, at the WWTP entrance and after treatment, in the air above activated sludge basins, and above fields irrigated with WW. Various enteric viruses were found in the water samples collected both before and after treatment. Norovirus was the most abundant with >10e4 genome copies/l (GC/L) before treatment and ~10e3 GC/L after treatment. Low quantities (<10e3GC/m³) were detected in the air above active sludge pools and irrigated plots. Hepatitis E virus was detected in all sampled compartments. A quantitative microbial risk assessment (QMRA) approach, including a simplified atmospheric dispersion model, allowed assessment of norovirus infection risk. The Bayesian QMRA approach considered wind speed measurements over 21years, and the variability and uncertainty of all measurements throughout the chain up to the risk. The probability of infection within one year for the most exposed WWTP employees was >10e-4 for strong wind speed (≥3m/s) and a constant emission rate of 8e3 GC/m³/s. This probability decreases by 3 log when the distance to the emission source is doubled. This information can aid development of safe water reuse policies in terms of local setback distance and wind conditions for wastewater reuse.

Modeling Rabbit Responses to Single and Multiple Aerosol Exposures of Bacillus anthracis Spores

Survival models are developed to predict response and time-to-response for mortality in rabbits following exposures to single or multiple aerosol doses of Bacillus anthracis spores. Hazard function models were developed for a multiple-dose data set to predict the probability of death through specifying functions of dose response and the time between exposure and the time-to-death (TTD). Among the models developed, the best-fitting survival model (baseline model) is an exponential dose-response model with a Weibull TTD distribution. Alternative models assessed use different underlying dose-response functions and use the assumption that, in a multiple-dose scenario, earlier doses affect the hazard functions of each subsequent dose. In addition, published mechanistic models are analyzed and compared with models developed in this article. None of the alternative models that were assessed provided a statistically significant improvement in fit over the baseline model. The general approach utilizes simple empirical data analysis to develop parsimonious models with limited reliance on mechanistic assumptions. The baseline model predicts TTDs consistent with reported results from three independent high-dose rabbit data sets. More accurate survival models depend upon future development of dose-response data sets specifically designed to assess potential multiple-dose effects on response and time-to-response. The process used in this article to develop the best-fitting survival model for exposure of rabbits to multiple aerosol doses of B. anthracis spores should have broad applicability to other host-pathogen systems and dosing schedules because the empirical modeling approach is based upon pathogen-specific empirically-derived parameters.

Simulation of enteric pathogen concentrations in locally-collected greywater and wastewater for microbial risk assessments

As decentralized water reuse continues to gain popularity, risk-based treatment guidance is increasingly sought for the protection of public health. However, effort s to evaluate pathogen risks and log-reduction requirements have been hindered by an incomplete understanding of pathogen occurrence and densities in locally-collected wastewaters (i.e., from decentralized collection systems). Of particular interest is the potentially high enteric pathogen concentration in small systems with an active infected excreter, but generally lower frequency of pathogen occurrences in smaller systems compared to those with several hundred contributors. Such variability, coupled with low concentrations in many source streams (e.g., sink, shower/bath, and laundry waters), has limited direct measurement of pathogens. This study presents an approach to modeling pathogen concentrations in variously sized greywater and combined wastewater collection systems based on epidemiological pathogen incidence rates, user population size, and fecal loadings to various residential wastewater sources. Pathogen infections were modeled within various population sizes (5-, 100-, and 1,000-person) for seven reference pathogens (viruses: adenoviruses, Norovirus, and Rotavirus; bacteria: Campylobacter and Salmonella spp.; and protozoa: Cryptosporidium and Giardia spp.) on each day of 10,000 possible years, accounting for intermittent infection and overlap of infection periods within the population. Fecal contamination of fresh greywaters from bathroom sinks, showers/baths, and laundry, as well as combined greywater and local combined wastewater (i.e., including toilets), was modeled based on reported fecal indicators in the various sources. Simulated daily infections and models of fecal contamination were coupled with pathogen shedding characteristics to generate distributions of pathogen densities in the various waters. The predicted frequency of pathogen occurrences in local wastewaters was generally low due to low infection incidence within small cohort groups, but increased with collection scale (population size) and infection incidence rate (e.g., Norovirus). When pathogens did occur, a decrease in concentrations from 5- to 100- and from 100- to 1,000-person systems was observed; nonetheless, overall mean concentrations (i.e., including non-occurrences) remained the same due to the increased number of occurrences. This highlights value of the model for characterizing scaling effects over averaging methods, which overestimate the frequency of pathogen occurrence in small systems while underestimating concentration peaks that likely drive risk periods. Results of this work will inform development of risk-based pathogen reduction requirements for decentralized water reuse.

Dose response models and a quantitative microbial risk assessment framework for the Mycobacterium avium complex that account for recent developments in molecular biology, taxonomy, and epidemiology

Mycobacterium avium complex (MAC) is a group of environmentally-transmitted pathogens of great public health importance. This group is known to be harbored, amplified, and selected for more human-virulent characteristics by amoeba species in aquatic biofilms. However, a quantitative microbial risk assessment (QMRA) has not been performed due to the lack of dose response models resulting from significant heterogeneity within even a single species or subspecies of MAC, as well as the range of human susceptibilities to mycobacterial disease. The primary human-relevant species and subspecies responsible for the majority of the human disease burden and present in drinking water, biofilms, and soil are M. avium subsp. hominissuis, M. intracellulare, and M. chimaera. A critical review of the published literature identified important health endpoints, exposure routes, and susceptible populations for MAC risk assessment. In addition, data sets for quantitative dose-response functions were extracted from published in vivo animal dosing experiments. As a result, seven new exponential dose response models for human-relevant species of MAC with endpoints of lung lesions, death, disseminated infection, liver infection, and lymph node lesions are proposed. Although current physical and biochemical tests used in clinical settings do not differentiate between M. avium and M. intracellulare, differentiating between environmental species and subspecies of the MAC can aid in the assessment of health risks and control of MAC sources. A framework is proposed for incorporating the proposed dose response models into susceptible population- and exposure route-specific QMRA models.

Unraveling the dose-response puzzle of L. monocytogenes: A mechanistic approach

Food-borne disease outbreaks caused by Listeria monocytogenes continue to impose heavy burdens on public health in North America and globally. To explore the threat L. monocytogenes presents to the elderly, pregnant woman and immuno-compromised individuals, many studies have focused on in-host infection mechanisms and risk evaluation in terms of dose-response outcomes. However, the connection of these two foci has received little attention, leaving risk prediction with an insufficient mechanistic basis. Consequently, there is a critical need to quantifiably link in-host infection pathways with the dose-response paradigm. To better understand these relationships, we propose a new mathematical model to describe the gastro-intestinal pathway of L. monocytogenes within the host. The model dynamics are shown to be sensitive to inoculation doses and exhibit bi-stability phenomena. Applying the model to guinea pigs, we show how it provides useful tools to identify key parameters and to inform critical values of these parameters that are pivotal in risk evaluation. Our preliminary analysis shows that the effect of gastro-environmental stress, the role of commensal microbiota and immune cells are critical for successful infection of L. monocytogenes and for dictating the shape of the dose-response curves.

Lethal exposure: An integrated approach to pathogen transmission via environmental reservoirs

To mitigate the effects of zoonotic diseases on human and animal populations, it is critical to understand what factors alter transmission dynamics. Here we assess the risk of exposure to lethal concentrations of the anthrax bacterium, Bacillus anthracis, for grazing animals in a natural system over time through different transmission mechanisms. We follow pathogen concentrations at anthrax carcass sites and waterholes for five years and estimate infection risk as a function of grass, soil or water intake, age of carcass sites, and the exposure required for a lethal infection. Grazing, not drinking, seems the dominant transmission route, and transmission is more probable from grazing at carcass sites 1-2 years of age. Unlike most studies of virulent pathogens that are conducted under controlled conditions for extrapolation to real situations, we evaluate exposure risk under field conditions to estimate the probability of a lethal dose, showing that not all reservoirs with detectable pathogens are significant transmission pathways.

A proof-of-concept model for the identification of the key events in the infection process with specific reference to Pseudomonas aeruginosa in corneal infections

BACKGROUND

It is a common medical practice to characterise an infection based on the causative agent and to adopt therapeutic and prevention strategies targeting the agent itself. However, from an epidemiological perspective, exposure to a microbe can be harmless to a host as a result of low-level exposure or due to host immune response, with opportunistic infection only occurring as a result of changes in the host, pathogen, or surrounding environment.

METHODS

We have attempted to review systematically the key host, pathogen, and environmental factors that may significantly impact clinical outcomes of exposure to a pathogen, using Pseudomonas aeruginosa eye infection as a case study.

RESULTS AND DISCUSSION

Extended contact lens wearing and compromised hygiene may predispose users to microbial keratitis, which can be a severe and vision-threatening infection. P. aeruginosa has a wide array of virulence-associated genes and sensing systems to initiate and maintain cell populations at the corneal surface and beyond. We have adapted the well-known concept of the epidemiological triangle in combination with the classic risk assessment framework (hazard identification, characterisation, and exposure) to develop a conceptual pathway-based model that demonstrates the overlapping relationships between the host, the pathogen, and the environment; and to illustrate the key events in P. aeruginosa eye infection.

CONCLUSION

This strategy differs from traditional approaches that consider potential risk factors in isolation, and hopefully will aid the identification of data and models to inform preventive and therapeutic measures in addition to risk assessment. Furthermore, this may facilitate the identification of knowledge gaps to direct research in areas of greatest impact to avert or mitigate adverse outcomes of infection.