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.

Development and application of a dose-response model for Elizabethkingia spp.

Elizabethkingia spp. are common environmental pathogens responsible for infections in more vulnerable populations. Although the exposure routes of concern are not well understood, some hospital-associated outbreaks have indicated possible waterborne transmission. In order to facilitate quantitative microbial risk assessment (QMRA) for Elizabethkingia spp., this study fit dose-response models to frog and mice datasets that evaluated intramuscular and intraperitoneal exposure to Elizabethkingia spp. The frog datasets could be pooled, and the exact beta-Poisson model was the best fitting model with optimized parameters α  = 0.52 and ß = 86,351. Using the exact beta-Poisson model, the dose of Elizabethkingia miricola resulting in a 50% morbidity response (LD50 ) was estimated to be approximately 237,000 CFU. The model developed herein was used to estimate the probability of infection for a hospital patient under a modeled exposure scenario involving a contaminated medical device and reported Elizabethkingia spp. concentrations isolated from hospital sinks after an outbreak. The median exposure dose was approximately 3 CFU/insertion event, and the corresponding median risk of infection was 3.4E-05. The median risk estimated in this case study was lower than the 3% attack rate observed in a previous outbreak, however, there are noted gaps pertaining to the possible concentrations of Elizabethkingia spp. in tap water and the most likely exposure routes. This is the first dose-response model developed for Elizabethkingia spp. thus enabling future risk assessments to help determine levels of risk and potential effective risk management strategies.

Meta-Analysis Addressing the Implications of Model Uncertainty in Understanding the Persistence of Indicators and Pathogens in Natural Surface Waters.

This study evaluates the impact persistence model selection has on the prediction of persistence values of interest and the identification of influential water quality and environmental factors for microorganisms in natural surface waters. Five persistence models representing first-order decay and nonlinear decay profiles were fit to a comprehensive database of 629 data sets for fecal indicator bacteria (FIB), bacteriophages, bacteria, viruses, and protozoa mined from the literature. Initial periods of minimal decay and decay rates tapering off over time were often observed, and a two-parameter model, based on the logistic probability distribution, provided the best fit to the data most frequently. First-order decay kinetics provided the best fit to less than 20% of the analyzed data. Using the best fitting models in this analysis, T90 and T99 metrics were calculated for each data set and used as the dependent variable in a variety of exploratory factor analyses. Random forest methods identified temperature and predation as some of the most important water quality factors influencing persistence, and the protozoa target type differed the most from FIB. This analysis further confirmed the interactions between temperature and predation and suggests that pH and turbidity be more frequently documented in persistence studies to further elucidate their impact on target persistence. The findings from this analysis and the calculated persistence metrics can be used to better inform quantitative microbial risk assessments and may lead to improved predictions of human health risks and water management decisions.

Identifying water quality and environmental factors that influence indicator and pathogen decay in natural surface waters.

Biphasic decay has been observed for indicators and pathogens in bench-scale and in-situ water experiments for decades, however, first-order decay kinetics continue to be applied to persistence data because of their simplicity and ease of application. Model uncertainty introduced by broadly applying first-order decay kinetics to persistence data may lead to erroneous decision making in the fields of water management and protection. As surface waters are exposed to highly variable environmental and water quality factors that influence microbial and viral persistence, it is expected that first-order decay kinetics are not representative of most of the persistence literature for indicators and pathogens in surface water matrices. This review compiled the methods and results of 61 studies that conducted experiments evaluating the persistence of fecal indicator bacteria (FIB), bacteriophages, pathogenic bacteria, viruses, and protozoa in natural surface water matrices. The goals of this review were trifold: (1) collate studies in the literature with data available for future persistence modeling, (2) present the current state of knowledge with regards to the environmental and water quality factors affecting persistence in natural surface waters, and (3) identify recurrent evidence for interactions between the frequently studied factors to inform future factor analyses. Comparing the methods and results across the 61 studies suggest potential interactions between sunlight and water type; sunlight and method of detection; predation and water type; predation and temperature; and water type and method of detection. The majority of the identified literature evaluated FIB or bacteria persistence; future experiments are needed that focus on protozoa, brackish or marine water types, and molecular-based methods of detection.

Persistence of poliovirus types 2 and 3 in waste-impacted water and sediment.

Eradication of poliovirus (PV) is a global public health priority, and as clinical cases decrease, the role of environmental surveillance becomes more important. Persistence of PV and the environmental factors that influence it (such as temperature and sample type) are an important part of understanding and interpreting positive environmental surveillance samples. The objective of this study was to evaluate the persistence of poliovirus type 2 (PV2) and type 3 (PV3) in wastewater and sediment. Microcosms containing either 1) influent wastewater or 2) influent wastewater with a sediment matrix were seeded with either PV2 or PV3, and stored for up to 126 days at three temperatures (4°C, room temperature [RT], and 30°C). Active PV in the liquid of (1), and the sediment and liquid portions of (2) were sampled and quantified at up to 10 time points via plaque assay and RT-qPCR. A suite of 17 models were tested for best fit to characterize decay of PV2 and PV3 over time and determine the time points at which >90% (T90) and >99% (T99) reduction was reached. Linear models assessed the influence of experimental factors (matrix, temperature, virus type and method of detection) on the predicted T90 and T99 values. Results showed that when T90 was the dependent variable, virus type, matrix, and temperature significantly affected decay, and there was a clear interaction between the sediment matrix and temperature. When T99 was the dependent variable, only temperature and matrix type significantly influenced the decay metric. This study characterizes the persistence of both active and molecular PV2 and PV3 in relevant environmental conditions, and demonstrates that temperature and sediment both play important roles in PV viability. As eradication nears and clinical cases decrease, environmental surveillance and knowledge of PV persistence will play a key role in understanding the silent circulation in endemic countries.

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.

Ebola Virus Dose Response Model for Aerosolized Exposures: Insights from Primate Data.

This study develops dose-response models for Ebolavirus using previously published data sets from the open literature. Two such articles were identified in which three different species of nonhuman primates were challenged by aerosolized Ebolavirus in order to study pathology and clinical disease progression. Dose groups were combined and pooled across each study in order to facilitate modeling. The endpoint of each experiment was death. The exponential and exact beta-Poisson models were fit to the data using maximum likelihood estimation. The exact beta-Poisson was deemed the recommended model because it more closely approximated the probability of response at low doses though both models provided a good fit. Although transmission is generally considered to be dominated by person-to-person contact, aerosolization is a possible route of exposure. If possible, this route of exposure could be particularly concerning for persons in occupational roles managing contaminated liquid wastes from patients being treated for Ebola infection and the wastewater community responsible for disinfection. Therefore, this study produces a necessary mathematical relationship between exposure dose and risk of death for the inhalation route of exposure that can support quantitative microbial risk assessment aimed at informing risk mitigation strategies including personal protection policies against occupational exposures.

Modeling the persistence of viruses in untreated groundwater.

Several factors can affect virus behavior and persistence in water sources. Historically linear models have been used to describe persistence over time; however, these models do not consider all of the factors that can affect inactivation kinetics or the observed patterns of decay. Meanwhile, applying the appropriate persistence model is critical for ensuring that decision makers are minimizing human health risk in the event of contamination and exposure to contaminated groundwater. Therefore, to address uncertainty in predictions of decay or virus concentrations over time, this study fit seventeen different linear and nonlinear mathematical models to persistence data from a previously conducted sampling study on drinking water wells throughout the United States. The models were fit using Maximum Likelihood Estimation and the best fitting models were determined by the Bayesian Information Criterion. The purpose of the study was to identify the best model for estimating decay of viruses in groundwater and to determine if model uncertainty contributes to erroneous predictions of viral contamination when only conventional models are considered. For the datasets analyzed in this study, the Juneja and Marks models and the exponential damped model were more representative of the persistence of viruses in groundwater than the traditionally used linear models. The results from this study were then evaluated with classification trees in order to identify more relevant modeling methodology for future research. The classification trees aid in narrowing the scope of appropriate persistence models based on characteristics of the experimental conditions and water sampled.

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.

The SURVIVE Community Project: A Family-Based Intervention to Reduce the Impact of Violence Exposures in Urban Youth.

The purpose of this article is to describe the development of a family-based intervention designed to target the harmful effects of exposure to family and community violence on urban youth and their parents. The program, "Supporting Urban Residents to be Violence-Free in a Violent Environment (SURVIVE)," is a 12-week multiple family group (MEG) intervention modeled upon similar children's mental health programs implemented with urban youth of color and their families in several major U.S. cities. The design and implementation of the SURVIVE Community Project were guided by a collaborative partnership between community members, including mental health professionals, teachers, and parents from the Bronx, and an interdisciplinary team of university-based researchers. In order to establish the feasibility and relevance of the program for urban communities, 25 families with children ages 7-11 participated in a pilot test of the curriculum. The description of the SURVIVE Community Project provided here is based on this work, and includes a discussion of facilitation issues. Implications for family-based intervention targeting urban children and families affected by violence are highlighted.