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.

A Quantitative Risk Estimation Platform for Indoor Aerosol Transmission of COVID-19.

Aerosol transmission has played a significant role in the transmission of COVID-19 disease worldwide. We developed a COVID-19 aerosol transmission risk estimation model to better understand how key parameters associated with indoor spaces and infector emissions affect inhaled deposited dose of aerosol particles that convey the SARS-CoV-2 virus. The model calculates the concentration of size-resolved, virus-laden aerosol particles in well-mixed indoor air challenged by emissions from an index case(s). The model uses a mechanistic approach, accounting for particle emission dynamics, particle deposition to indoor surfaces, ventilation rate, and single-zone filtration. The novelty of this model relates to the concept of "inhaled & deposited dose" in the respiratory system of receptors linked to a dose-response curve for human coronavirus HCoV-229E. We estimated the volume of inhaled & deposited dose of particles in the 0.5-4 µm range expressed in picoliters (pL) in a well-documented COVID-19 outbreak in restaurant X in Guangzhou China. We anchored the attack rate with the dose-response curve of HCoV-229E which provides a preliminary estimate of the average SARS-CoV-2 dose per person, expressed in plaque forming units (PFUs). For a reasonable emission scenario, we estimate approximately three PFU per pL deposited, yielding roughly 10 PFUs deposited in the respiratory system of those infected in restaurant X. To explore the model's utility, we tested it with four COVID-19 outbreaks. The risk estimates from the model fit reasonably well with the reported number of confirmed cases given available metadata from the outbreaks and uncertainties associated with model assumptions.

Action Levels for SARS-CoV-2 in Air: Preliminary Approach.

Quantitative microbial risk assessment has been used to develop criteria for exposure to many microorganisms. In this article, the dose-response curve for Coronavirus 229E is used to develop preliminary risk-based exposure criteria for SARS-CoV-2 via the respiratory portals of entry.

Legionnaires' disease in dental offices: Quantifying aerosol risks to dental workers and patients.

Legionella pneumophila is an opportunistic bacterial respiratory pathogen that is one of the leading causes of drinking water outbreaks in the United States. Dental offices pose a potential risk for inhalation or aspiration of L. pneumophila due to the high surface area to volume ratio of dental unit water lines-a feature that is conducive to biofilm growth. This is coupled with the use of high-pressure water devices (e.g., ultrasonic scalers) that produce fine aerosols within the breathing zone. Prior research confirms that L. pneumophila occurs in dental unit water lines, but the associated human health risks have not been assessed. We aimed to: (1) synthesize the evidence for transmission and management of Legionnaires' disease in dental offices; (2) create a quantitative modeling framework for predicting associated L. pneumophila infection risk; and (3) highlight influential parameters and research gaps requiring further study. We reviewed outbreaks, management guidance, and exposure studies and used these data to parameterize a quantitative microbial risk assessment (QMRA) model for L. pneumophila in dental applications. Probabilities of infection for dental hygienists and patients were assessed on a per-exposure and annual basis. We also assessed the impact of varying ventilation rates and the use of personal protective equipment (PPE). Following an instrument purge (i.e., flush) and with a ventilation rate of 1.2 air changes per hour, the median per-exposure probability of infection for dental hygienists and patients exceeded a 1-in-10,000 infection risk benchmark. Per-exposure risks for workers during a purge and annual risks for workers wearing N95 masks did not exceed the benchmark. Increasing air change rates in the treatment room from 1.2 to 10 would achieve an ∼85% risk reduction, while utilization of N95 respirators would reduce risks by ∼95%. The concentration of L. pneumophila in dental unit water lines was a dominant parameter in the model and driver of risk. Future risk assessment efforts and refinement of microbiological control protocols would benefit from expanded occurrence datasets for L. pneumophila in dental applications.

Quantitative Microbial Risk Assessment and Molecular Biology: Paths to Integration.

Quantitative microbial risk assessment (QMRA) has now been in use for over 35 years and has formed the basis for developing criteria for ensuring public health related to water, food, and remediation, to name a few areas. The initial data for QMRA (both in exposure assessment and in dose response assessment) came from measurements using assays for viability, such as plate counts, plaque assays, or animal infectivity. With the increasing use of molecular methods for the measurement of microorganisms in the environment, it has become important to assess how to use such data to estimate infectious disease risks. The limitations to the use of such data and needs to resolve the limitations will be addressed.

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.

Risk-Based Critical Concentrations of Legionella pneumophila for Indoor Residential Water Uses.

Legionella spp. is a key contributor to the United States waterborne disease burden. Despite potentially widespread exposure, human disease is relatively uncommon, except under circumstances where pathogen concentrations are high, host immunity is low, or exposure to small-diameter aerosols occurs. Water quality guidance values for Legionella are available for building managers but are generally not based on technical criteria. To address this gap, a quantitative microbial risk assessment (QMRA) was conducted using target risk values in order to calculate corresponding critical concentrations on a per-fixture and aggregate (multiple fixture exposure) basis. Showers were the driving indoor exposure risk compared to sinks and toilets. Critical concentrations depended on the dose response model (infection vs clinical severity infection, CSI), risk target used (infection risk vs disability adjusted life years [DALY] on a per-exposure or annual basis), and fixture type (conventional vs water efficient or "green"). Median critical concentrations based on exposure to a combination of toilet, faucet, and shower aerosols ranged from ∼10-2 to ∼100 CFU per L and ∼101 to ∼103 CFU per L for infection and CSI dose response models, respectively. As infection model results for critical L. pneumophila concentrations were often below a feasible detection limit for culture-based assays, the use of CSI model results for nonhealthcare water systems with a 10-6 DALY pppy target (the more conservative target) would result in an estimate of 12.3 CFU per L (arithmetic mean of samples across multiple fixtures and/or over time). Single sample critical concentrations with a per-exposure-corrected DALY target at each conventional fixture would be 1.06 × 103 CFU per L (faucets), 8.84 × 103 CFU per L (toilets), and 14.4 CFU per L (showers). Using a 10-4 annual infection risk target would give a 1.20 × 103 CFU per L mean for multiple fixtures and single sample critical concentrations of 1.02 × 105, 8.59 × 105, and 1.40 × 103 CFU per L for faucets, toilets, and showers, respectively. Annual infection risk-based target estimates are in line with most current guidance documents of less than 1000 CFU per L, while DALY-based guidance suggests lower critical concentrations might be warranted in some cases. Furthermore, approximately <10 CFU per mL L. pneumophila may be appropriate for healthcare or susceptible population settings. This analysis underscores the importance of the choice of risk target as well as sampling program considerations when choosing the most appropriate critical concentration for use in public health guidance.

A Case Study Evaluating the Risk of Infection from Middle Eastern Respiratory Syndrome Coronavirus (MERS-CoV) in a Hospital Setting Through Bioaerosols.

Middle Eastern respiratory syndrome, an emerging viral infection with a global case fatality rate of 35.5%, caused major outbreaks first in 2012 and 2015, though new cases are continuously reported around the world. Transmission is believed to mainly occur in healthcare settings through aerosolized particles. This study uses Quantitative Microbial Risk Assessment to develop a generalizable model that can assist with interpreting reported outbreak data or predict risk of infection with or without the recommended strategies. The exposure scenario includes a single index patient emitting virus-containing aerosols into the air by coughing, leading to short- and long-range airborne exposures for other patients in the same room, nurses, healthcare workers, and family visitors. Aerosol transport modeling was coupled with Monte Carlo simulation to evaluate the risk of MERS illness for the exposed population. Results from a typical scenario show the daily mean risk of infection to be the highest for the nurses and healthcare workers (8.49 × 10-4 and 7.91 × 10-4 , respectively), and the lowest for family visitors and patients staying in the same room (3.12 × 10-4 and 1.29 × 10-4 , respectively). Sensitivity analysis indicates that more than 90% of the uncertainty in the risk characterization is due to the viral concentration in saliva. Assessment of risk interventions showed that respiratory masks were found to have a greater effect in reducing the risks for all the groups evaluated (>90% risk reduction), while increasing the air exchange was effective for the other patients in the same room only (up to 58% risk reduction).

Microfluidic quantification of multiple enteric and opportunistic bacterial pathogens in roof-harvested rainwater tank samples.

Potable and non-potable uses of roof-harvested rainwater (RHRW) are increasing due to water shortages. To protect human health risks, it is important to identify and quantify disease-causing pathogens in RHRW so that appropriate treatment options can be implemented. We used a microfluidic quantitative PCR (MFQPCR) system for the quantitative detection of a wide array of fecal indicator bacteria (FIB) and pathogens in RHRW tank samples along with culturable FIB and conventional qPCR analysis of selected pathogens. Among the nine pathogenic bacteria and their associated genes tested with the MFQPCR, 4.86 and 2.77% samples were positive for Legionella pneumophila and Shigella spp., respectively. The remaining seven pathogens were absent. MFQPCR and conventional qPCR results showed good agreement. Therefore, direct pathogen quantification by MFQPCR systems may be advantageous for circumstances where a thorough microbial analysis is required to assess the public health risks from multiple pathogens that occur simultaneously in the target water source.

Seasonal Assessment of Opportunistic Premise Plumbing Pathogens in Roof-Harvested Rainwater Tanks.

A seasonal study on the occurrence of six opportunistic premise plumbing pathogens (OPPPs) in 24 roof-harvested rainwater (RHRW) tanks repeatedly sampled over six monthly sampling events (n = 144) from August 2015 to March 2016 was conducted using quantitative qPCR. Fecal indicator bacteria (FIB) Escherichia coli (E. coli) and Enterococcus spp. were enumerated using culture-based methods. All tank water samples over the six events were positive for at least one OPPP (Legionella spp., Legionella pneumophila, Mycobacterium avium, Mycobacterium intracellulare, Pseudmonas aeruginosa, or Acanthamoeba spp.) during the entire course of the study. FIB were positively but weakly correlated with P. aeruginosa (E. coli vs P. aeruginosa τ = 0.090, p = 0.027; Enterococcus spp. vs P. aeruginosa τ = 0.126, p = 0.002), but not the other OPPPs. FIBs were more prevalent during the wet season than the dry season, and L. pneumophila was only observed during the wet season. However, concentrations of Legionella spp., M. intracellulare, Acanthamoeba spp., and M. avium peaked during the dry season. Correlations were assessed between FIB and OPPPs with meteorological variables, and it was determined that P. aeruginosa was the only OPPP positively associated with an increased antecedent dry period, suggesting stagnation time may play a role for the occurrence of this OPPP in tank water. Infection risks may exceed commonly cited benchmarks for uses reported in the rainwater usage survey such as pool top-up, and warrant further exploration through quantitative microbial risk assessment (QMRA).

A method for incorporating a time-dose-response model into a Giardia lamblia outbreak.

Experimental time-to-infection data is a useful, but often underutilized, material for examining the mechanics of in vivo pathogen growth. In this paper, the authors attempt to incorporate a time-dose-response (TDR) equation into a model which predicts the number of ill persons per day in a Giardia lamblia epidemic using data collected from a Pittsfield, Massachusetts outbreak. To this end, dose-response and TDR models were generated for Giardia exposure to beaver and human volunteers, and a maximum likelihood estimation approach was used to ensure that the models provided acceptable fits. The TDR equation that best-fit the human data was the beta-Poisson with exponential-reciprocal dependency model, and this was chosen to be incorporated into the outbreak model. The outbreak model is an expanded probability model that convolutes an assumed incubation distribution of the infectious agent with an exposure distribution. Since the beta-Poisson with exponential-reciprocal dependency models the time-to-infection density distribution, it is input as the incubation distribution. Several density functions, including the Weibull, lognormal, gamma, and uniform functions served as exposure distributions. The convolution of the time-dependent probability distribution with the lognormal distribution yielded the best-fit for the outbreak model.

Incorporating Time-Dose-Response into Legionella Outbreak Models.

A novel method was used to incorporate in vivo host-pathogen dynamics into a new robust outbreak model for legionellosis. Dose-response and time-dose-response (TDR) models were generated for Legionella longbeachae exposure to mice via the intratracheal route using a maximum likelihood estimation approach. The best-fit TDR model was then incorporated into two L. pneumophila outbreak models: an outbreak that occurred at a spa in Japan, and one that occurred in a Melbourne aquarium. The best-fit TDR from the murine dosing study was the beta-Poisson with exponential-reciprocal dependency model, which had a minimized deviance of 32.9. This model was tested against other incubation distributions in the Japan outbreak, and performed consistently well, with reported deviances ranging from 32 to 35. In the case of the Melbourne outbreak, the exponential model with exponential dependency was tested against non-time-dependent distributions to explore the performance of the time-dependent model with the lowest number of parameters. This model reported low minimized deviances around 8 for the Weibull, gamma, and lognormal exposure distribution cases. This work shows that the incorporation of a time factor into outbreak distributions provides models with acceptable fits that can provide insight into the in vivo dynamics of the host-pathogen system.

Risk of Illness with Salmonella due to Consumption of Raw Unwashed Vegetables Irrigated with Water from the Bogotá River.

The Bogotá River receives untreated wastewater from the city of Bogotá and many other towns. Downstream from Bogotá, water from the river is used for irrigation of crops. Concentrations of indicator organisms in the river are high, which is consistent with fecal contamination. To investigate the probability of illness due to exposure to enteric pathogens from the river, specifically Salmonella, we took water samples from the Bogotá River at six sampling locations in an area where untreated water from the river is used for irrigation of lettuce, broccoli, and cabbage. Salmonella concentrations were quantified by direct isolation and qPCR. Concentrations differed, depending on the quantification technique used, ranging between 107.7 and 109.9 number of copies of gene invA per L and 105.3 and 108.4 CFU/L, for qPCR and direct isolation, respectively. A quantitative microbial risk assessment model that estimates the daily risk of illness with Salmonella resulting from consuming raw unwashed vegetables irrigated with water from the Bogotá River was constructed using the Salmonella concentration data. The daily probability of illness from eating raw unwashed vegetables ranged between 0.62 and 0.85, 0.64 and 0.86, and 0.64 and 0.85 based on concentrations estimated by qPCR (0.47-0.85, 0.47-0.86, and 0.41-0.85 based on concentrations estimated by direct isolation) for lettuce, cabbage, and broccoli, respectively, which are all above the commonly propounded benchmark of 10-4 per year. Results obtained in this study highlight the necessity for appropriate wastewater treatment in the region, and emphasize the importance of postharvest practices, such as washing, disinfecting, and cooking.

Risks from Ebolavirus Discharge from Hospitals to Sewer Workers.

Current World Health Organization and Centers for Disease Control and Prevention guidance for the disposal of liquid waste from patients undergoing treatment for Ebola virus disease at hospitals in the U.S. is to manage patient excreta as ordinary wastewater without pretreatment. The potential for Ebolavirus transmission via liquid waste discharged into the wastewater environment is currently unknown, however. Possible worker inhalation exposure to Ebolavirus-contaminated aerosols in the sewer continues to be a concern within the wastewater treatment community. In this study, a quantitative microbial risk assessment was carried out to assess a sewer worker's potential risk of developing Ebola virus disease from inhalation exposure when performing standard occupational activities in a sewer line serving a hospital receiving Ebola patients where there is no pretreatment of the waste prior to discharge. Risk projections were estimated for four scenarios that considered the infectivity of viral particles and the degree of worker compliance with personal protective equipment guidelines. Under the least-favorable scenario, the median potential risk of developing Ebola virus disease from inhalation exposure to Ebolavirus-contaminated aerosols in the sewer is approximately 10-5.77 (with a first to third quartile range of 10-7.06 to 10-4.65), a value higher than many risk managers may be willing to accept. Although further data gathering efforts are necessary to improve the precision of the risk projections presented here, the results suggest that the potential risk that sewer workers face when operating in a wastewater collection system downstream from a hospital receiving Ebola patients warrants further attention, and that current authoritative guidance for Ebolavirus liquid waste disposal-to dispose in the sanitary sewer without further treatment-may be insufficiently protective of sewer worker safety.

Does the use of tubular digesters to treat livestock waste lower the risk of infection from Cryptosporidium parvum and Giardia lamblia?

Worldwide, high incidences of cryptosporidiosis and giardiasis are attributed to livestock waste. Quantitative microbial risk assessment can be used to estimate the risk of livestock related infections from Cryptosporidium parvum and Giardia lamblia. The objective of this paper was to assess the occupational and public health risks associated with management of raw and anaerobically digested livestock waste in two rural communities in Costa Rica based on fomite, soil and crop contamination and livestock waste management exposure pathways. Risks related to cattle waste were greater than swine waste due to cattle shedding more (oo)cysts. Cryptosporidium parvum also posed a greater risk than Giardia lamblia in all exposure pathways due to livestock shedding high loads of Cryptosporidium parvum oocysts and oocysts' lower inactivation rates during anaerobic digestion compared with Giardia lamblia cysts. The risk of infection from exposure to contaminated soil and crops was significantly lower for a community using tubular anaerobic digesters to treat livestock waste compared to a community where the untreated waste was applied to soil. The results indicate that treatment of livestock waste in small-scale tubular anaerobic digesters has the potential to significantly decrease the risk of infection below the World Health Organization's acceptable individual annual risk of infection (10-4).

Reproducible Risk Assessment.

Reproducible research is a concept that has emerged in data and computationally intensive sciences in which the code used to conduct all analyses, including generation of publication quality figures, is directly available, and preferably in open source manner. This perspective outlines the processes and attributes, and illustrates the execution of reproducible research via a simple exposure assessment of air pollutants in metropolitan Philadelphia.

Microbial dose response modeling: past, present, and future.

The understanding of the risk to humans from exposure to pathogens has been firmly put into a risk assessment framework. A key element of applying this approach is the understanding of the relationship between dose and response for particular pathogens. This understanding has progressed from early use of threshold concepts ("minimal infectious dose") thru multiple generations of models. Generation 1 models describe probability of response to exposed dose. Generation 2 models incorporate host factors (e.g., age) and/or pathogen factors (e.g., particle size of inhaled agents). Generation 3 models describe the rate at which effects develop, i.e. the epidemic curve. These (generation 1 through three models) have been developed and used in multiple contexts. Beyond Generation 3 lies an opportunity for the deep incorporation of in vivo physiological responses and the coupling of the individual host dynamics to the dynamics of spread of contagious diseases in the population. This would enable more direct extrapolation from controlled dosing studies to estimate population level effects. There remain also needs to understand broader categories of infectious agents, including pathogenic amoebae and fungi. More advanced models need to be validated against well-characterized human outbreak data.

Nondeterministic computational fluid dynamics modeling of Escherichia coli inactivation by peracetic acid in municipal wastewater contact tanks.

Wastewater disinfection processes are typically designed according to heuristics derived from batch experiments in which the interaction among wastewater quality, reactor hydraulics, and inactivation kinetics is often neglected. In this paper, a computational fluid dynamics (CFD) study was conducted in a nondeterministic (ND) modeling framework to predict the Escherichia coli inactivation by peracetic acid (PAA) in municipal contact tanks fed by secondary settled wastewater effluent. The extent and variability associated with the observed inactivation kinetics were both satisfactorily predicted by the stochastic inactivation model at a 95% confidence level. Moreover, it was found that (a) the process variability induced by reactor hydraulics is negligible when compared to the one caused by inactivation kinetics, (b) the PAA dose required for meeting regulations is dictated equally by the fixed limit of the microbial concentration as well as its probability of occurrence, and (c) neglecting the probability of occurrence during process sizing could lead to an underestimation of the PAA dose required by as much as 100%. Finally, the ND-CFD model was used to generate sizing information in the form of probabilistic disinfection curves relating E. coli inactivation and probability of occurrence with the average PAA dose and PAA residual concentration at the outlet of the contact tank.