Development of a microbial dose response visualization and modelling application for QMRA modelers and educators

Research gaps and priorities for quantitative microbial risk assessment (QMRA)

The coronavirus disease 2019 pandemic highlighted the need for more rapid and routine application of modeling approaches such as quantitative microbial risk assessment (QMRA) for protecting public health. QMRA is a transdisciplinary science dedicated to understanding, predicting, and mitigating infectious disease risks. To better equip QMRA researchers to inform policy and public health management, an Advances in Research for QMRA workshop was held to synthesize a path forward for QMRA research. We summarize insights from 41 QMRA researchers and experts to clarify the role of QMRA in risk analysis by (1) identifying key research needs, (2) highlighting emerging applications of QMRA; and (3) describing data needs and key scientific efforts to improve the science of QMRA. Key identified research priorities included using molecular tools in QMRA, advancing dose-response methodology, addressing needed exposure assessments, harmonizing environmental monitoring for QMRA, unifying a divide between disease transmission and QMRA models, calibrating and/or validating QMRA models, modeling co-exposures and mixtures, and standardizing practices for incorporating variability and uncertainty throughout the source-to-outcome continuum. Cross-cutting needs identified were to: develop a community of research and practice, integrate QMRA with other scientific approaches, increase QMRA translation and impacts, build communication strategies, and encourage sustainable funding mechanisms. Ultimately, a vision for advancing the science of QMRA is outlined for informing national to global health assessments, controls, and policies.

Using mixture density networks to emulate a stochastic within-host model of Francisella tularensis infection

For stochastic models with large numbers of states, analytical techniques are often impractical, and simulations time-consuming and computationally demanding. This limitation can hinder the practical implementation of such models. In this study, we demonstrate how neural networks can be used to develop emulators for two outputs of a stochastic within-host model of Francisella tularensis infection: the dose-dependent probability of illness and the incubation period. Once the emulators are constructed, we employ Markov Chain Monte Carlo sampling methods to parameterize the within-host model using records of human infection. This inference is only possible through the use of a mixture density network to emulate the incubation period, providing accurate approximations of the corresponding probability distribution. Notably, these estimates improve upon previous approaches that relied on bacterial counts from the lungs of macaques. Our findings reveal a 50% infectious dose of approximately 10 colony-forming units and we estimate that the incubation period can last for up to 11 days following low dose exposure.

Quantitative microbial risk assessment of outdoor aerosolized pathogens in cities with poor sanitation

The aeromicrobiological transmission pathway of enteric pathogens in places with unsafe sanitation services is poorly understood. In an attempt to partly fill this knowledge gap, we assessed the potential public health impact of bioaerosols near open waste canals (OWCs) using Quantitative Microbial Risk Assessment (QMRA). We used data acquired in La Paz, Bolivia to characterize the risk of disease that aerosolized enteric pathogens may pose through food, fomites and inhalation (all followed by ingestion). Three reference pathogens were selected to conduct the assessment: enterotoxigenic Escherichia coli (ETEC), Shigella flexneri, and Campylobacter jejuni. Inhalation followed by ingestion had the highest median infection risk per event i.e. 3 × 10^-5 (3 infections for every 100,000 exposures), compared to contaminated food e.g. 5 × 10^-6 and fomites e.g. 2 × 10^-7, all for C. jejuni infections. Our sensitivity analysis showed that bacterial fluxes from the air were the most influential factor on risk. Our results suggest that fecal bacterial aerosols from OWCs present non-negligible risks of infection in La Paz, with median annual infection risks by C. jejuni being 18 (food), and 100 (inhalation) times greater than the EPA's standard for drinking water (1 × 10^-4). We included two of the QMRA models presented here in a novel web application we developed for user-specified application in different contexts.

Campylobacter Species, Microbiological Source Tracking and Risk Assessment of Bacterial pathogens

Campylobacter species continue to remain critical pathogens of public health interest. They are responsible for approximately 500 million cases of gastroenteritis per year worldwide. Infection occurs through the consumption of contaminated food and water. Microbial risk assessment and source tracking are crucial epidemiological strategies to monitor the outbreak of campylobacteriosis effectively. Various methods have been proposed for microbial source tracking and risk assessment, most of which rely on conventional microbiological techniques such as detecting fecal indicator organisms and other novel microbial source tracking methods, including library-dependent microbial source tracking and library-independent source tracking approaches. However, both the traditional and novel methods have their setbacks. For example, while the conventional techniques are associated with a poor correlation between indicator organism and pathogen presence, on the other hand, it is impractical to interpret qPCR-generated markers to establish the exact human health risks even though it can give information regarding the potential source and relative human risk. Therefore, this article provides up-to-date information on campylobacteriosis, various approaches for source attribution, and risk assessment of bacterial pathogens, including next-generation sequencing approaches such as shotgun metagenomics, which effectively answer the questions of potential pathogens are there and in what quantities.

Time to revisit the endpoint dilution assay and to replace the TCID50 as a measure of a virus sample's infection concentration

The endpoint dilution assay's output, the 50% infectious dose (ID50), is calculated using the Reed-Muench or Spearman-Kärber mathematical approximations, which are biased and often miscalculated. We introduce a replacement for the ID50 that we call Specific INfection (SIN) along with a free and open-source web-application, midSIN (https://midsin.physics.ryerson.ca) to calculate it. midSIN computes a virus sample's SIN concentration using Bayesian inference based on the results of a standard endpoint dilution assay, and requires no changes to current experimental protocols. We analyzed influenza and respiratory syncytial virus samples using midSIN and demonstrated that the SIN/mL reliably corresponds to the number of infections a sample will cause per mL. It can therefore be used directly to achieve a desired multiplicity of infection, similarly to how plaque or focus forming units (PFU, FFU) are used. midSIN's estimates are shown to be more accurate and robust than the Reed-Muench and Spearman-Kärber approximations. The impact of endpoint dilution plate design choices (dilution factor, replicates per dilution) on measurement accuracy is also explored. The simplicity of SIN as a measure and the greater accuracy provided by midSIN make them an easy and superior replacement for the TCID50 and other in vitro culture ID50 measures. We hope to see their universal adoption to measure the infectivity of virus samples.

Modeling the Dose Response Relationship of Waterborne Acanthamoeba

This study developed dose response models for determining the probability of eye or central nervous system infections from previously conducted studies using different strains of Acanthamoeba spp. The data were a result of animal experiments using mice and rats exposed corneally and intranasally to the pathogens. The corneal inoculations of Acanthamoeba isolate Ac 118 included varied amounts of Corynebacterium xerosis and were best fit by the exponential model. Virulence increased with higher levels of C. xerosis. The Acanthamoeba culbertsoni intranasal study with death as an endpoint of response was best fit by the beta-Poisson model. The HN-3 strain of A. castellanii was studied with an intranasal exposure and three different endpoints of response. For all three studies, the exponential model was the best fit. A model based on pooling data sets of the intranasal exposure and death endpoint resulted in an LD₅₀ of 19,357 amebae. The dose response models developed in this study are an important step towards characterizing the risk associated with free-living amoeba like Acanthamoeba in drinking water distribution systems. Understanding the human health risk posed by free-living amoeba will allow for quantitative microbial risk assessments that support building design decisions to minimize opportunities for pathogen growth and survival.

Forty Years of Food Safety Risk Assessment: A History and Analysis

Before the founding of the Society for Risk Analysis (SRA) in 1980, food safety in the United States had long been a concern, but there was a lack of systematic methods to assess food-related risks. In 1906, the U.S. Congress passed, and President Roosevelt signed, the Pure Food and Drug Act and the Meat Inspection Act to regulate food safety at the federal level. This Act followed the publication of multiple reports of food contamination, culminating in Upton Sinclair's novel The Jungle, which highlighted food and worker abuses in the meatpacking industry. Later in the 20th century, important developments in agricultural and food technology greatly increased food production. But chemical exposures from agricultural and other practices resulted in major amendments to federal food laws, including the Delaney Clause, aimed specifically at cancer-causing chemicals. Later in the 20th century, when quantitative risk assessment methods were given greater scientific status in a seminal National Research Council report, food safety risk assessment became more systematized. Additionally, in these last 40 years, food safety research has resulted in increased understanding of a range of health effects from foodborne chemicals, and technological developments have improved U.S. food safety from farm to fork by offering new ways to manage risks. We discuss the history of food safety and the role risk analysis has played in its evolution, starting from over a century ago, but focusing on the last 40 years. While we focus on chemical risk assessment in the U.S., we also discuss microbial risk assessment and international food safety.

Microbial Dose-Response Curves and Disinfection Efficacy Models Revisited

The same term “dose-response curve” describes the relationship between the number of ingested microbes or their logarithm, and the probability of acute illness or death (type I), and between a disinfectant’s dose and the targeted microbe’s survival ratio (type II), akin to survival curves in thermal and non-thermal inactivation kinetics. The most common model of type I curves is the cumulative form of the beta-Poisson distribution which is sometimes indistinguishable from the lognormal or Weibull distribution. The most notable survival kinetics models in static disinfection are of the Chick-Watson-Hom’s kind. Their published dynamic versions, however, should be viewed with caution. A microbe population’s type II dose-response curve, static and dynamic, can be viewed as expressing an underlying spectrum of individual vulnerabilities (or resistances) to the particular disinfectant. Therefore, such a curve can be described mathematically by the flexible Weibull distribution, whose scale parameter is a function of the disinfectant’s intensity, temperature, and other factors. But where the survival ratio’s drop is so steep that the static dose-response curve resembles a step function, the Fermi distribution function becomes a suitable substitute. The utility of the CT (or Ct) concept primarily used in water disinfection is challenged on theoretical grounds and its limitations highlighted. It is suggested that stochastic models of microbial inactivation could be used to link the fates of individual viruses or bacteria to their manifestation in the survival curve’s shape. Although the emphasis is on viruses and bacteria, most of the discussion is relevant to fungi, protozoa, and perhaps worms too.

Evaluation of the 50% Infectious Dose of Human Norovirus Cin-2 in Gnotobiotic Pigs: A Comparison of Classical and Contemporary Methods for Endpoint Estimation

Human noroviruses (HuNoVs) are the leading causative agents of epidemic and sporadic acute gastroenteritis that affect people of all ages worldwide. However, very few dose–response studies have been carried out to determine the median infectious dose of HuNoVs. In this study, we evaluated the median infectious dose (ID₅₀) and diarrhea dose (DD₅₀) of the GII.4/2003 variant of HuNoV (Cin-2) in the gnotobiotic pig model of HuNoV infection and disease. Using various mathematical approaches (Reed–Muench, Dragstedt–Behrens, Spearman–Karber, logistic regression, and exponential and approximate beta-Poisson dose–response models), we estimated the ID₅₀ and DD₅₀ to be between 2400–3400 RNA copies, and 21,000–38,000 RNA copies, respectively. Contemporary dose–response models offer greater flexibility and accuracy in estimating ID₅₀. In contrast to classical methods of endpoint estimation, dose–response modelling allows seamless analyses of data that may include inconsistent dilution factors between doses or numbers of subjects per dose group, or small numbers of subjects. Although this investigation is consistent with state-of-the-art ID₅₀ determinations and offers an advancement in clinical data analysis, it is important to underscore that such analyses remain confounded by pathogen aggregation. Regardless, challenging virus strain ID₅₀ determination is crucial for identifying the true infectiousness of HuNoVs and for the accurate evaluation of protective efficacies in pre-clinical studies of therapeutics, vaccines and other prophylactics using this reliable animal model.

A Data Simulation Method to Optimize a Mechanistic Dose-Response Model for Viral Loads of Hepatitis A

Driven by the quantitative estimate of risk via the dose-response models, quantitative microbial risk assessment has been used successfully for public health interventions. The dose-response models are derived starting from an average exposed dose of infectious particles, this dictates the of dose data units required. Then dose-response data from animal model experiments are used to optimize these mechanistic dose-response models. For hepatitis A (Hep-A), the only available dose-response data use grams of feces for dose units. Therefore, to develop a dose-response model for Hep-A a method of converting these doses in grams of feces into infectious particles, while accounting for the uncertainty of this conversion is needed. This research develops a method to couple data simulation with the likelihood estimation method for model optimization to accomplish this. This adapted method uses data simulation to model the doses as viral particles while accounting for the within-group variability of this simulation. Then these simulated doses, coupled with the original dose-response data, are used to optimize the mechanistic dose-response models. This method results in a more computationally rigorous means of modeling these types of dose-response data. The resulting dose-response model for Hep-A is also more appropriate to use than the current option for Hep-A risk models.

Development of a dose-response model for Naegleria fowleri

This study develops novel dose-response models for Naegleria fowleri from selected peer-reviewed experiments on the virulence based on the intranasal exposure pathway. One data set measured the response of mice intranasally inoculated with the amebae and the other study addressed the response of mice swimming in N. fowleri infected water. The measured response for both studies was death. All experimental data were best fit by the beta-Poisson dose-response model. The three swimming experiments could be pooled, and this is the final recommended model with an LD₅₀ of 13,257 amebae. The results of this study provide a better estimate of the probability of the risk to N. fowleri exposure than the previous models developed based on an intravenous exposure. An accurate dose-response model is the first step in quantifying the risk of free-living amebae like N. fowleri, which pose risks in recreational environments and have been detected in drinking water and premise plumbing systems. A better understanding of this risk will allow for risk management that limits the ability for pathogen growth, proliferation, and exposure.

Translating research to policy at the NCSE 2017 symposium "Microbiology of the Built Environment: Implications for Health and Design"

Here, we summarize a symposium entitled "Microbiology of the Built Environment: Implications for Health and Design" that was presented at the National Council for Science and the Environment (NCSE) 17th National Conference and Global Forum in January 2017. We covered topics including indoor microbial exposures and childhood asthma, the influence of hospital design on neonatal development, the role of the microbiome in our premise (i.e., building) plumbing systems, antibiotic resistance, and quantitative microbial risk assessment. This symposium engaged the broader scientific and policy communities in a discussion to increase awareness of this critical research area and translate findings to practice.

Quantitative microbial risk assessment of Greywater on-site reuse

Recycle domestic greywater for on-site non-potable uses can lessen the demand on potable water and the burden on wastewater treatment plants. However, lack of studies to assess health risk associated with such practices has hindered their popularity. A Quantitative Microbial Risk Assessment was conducted to estimate the public health risks for two greywater reuse scenarios: toilet flushing and food-crop irrigation. Household greywater quality from three sources (bathroom, laundry and kitchen) was analyzed. Mathematical exposure rates of different scenarios were established based on human behavior using Monte-Carlo simulation. The results showed that, greywater from all three household sources could be safely used for toilet flushing after a simple treatment of microfiltration. The median range of annual infection risk was 8.8 × 10-15-8.3 × 10-11 per-person-per-year (pppy); and the median range of disease burden was 7.6 × 10-19-7.3 × 10-15 disability-adjusted life years (DALYs) pppy. In food-crop irrigation scenario, the annual infection risks and disease burdens of treated greywater from bathroom and laundry (2.8 × 10-8, 4.9 × 10-8 pppy; 2.3 × 10-12-4.2 × 10-12 DALYs pppy) were within the acceptable levels of U.S. EPA annual infection risk (≤10-4 pppy) and WHO disease burden (≤10-6 DALYs pppy) benchmarks, while kitchen greywater was not suitable for food-crop irrigation (4.9 × 10-6 pppy; 4.3 × 10-10 DALYs pppy) based on these benchmarks. The model uncertainties were discussed, which suggests that a more accurate risk estimation requires improvements on data collection and model refinement.