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 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.

Wastewater treatment plant effluent introduces recoverable shifts in microbial community composition in receiving streams.

Through a combined approach using analytical chemistry, real-time quantitative polymerase chain reaction (qPCR), and targeted amplicon sequencing, we studied the impact of wastewater treatment plant effluent sources at six sites on two sampling dates on the chemical and microbial population regimes within the Wissahickon Creek, and its tributary, Sandy Run, in Montgomery County, Pennsylvania, USA. These water bodies contribute flow to the Schuylkill River, one of the major drinking water sources for Philadelphia, Pennsylvania. Effluent was observed to be a significant source of nutrients, human and non-specific fecal associated taxa. There was an observed increase in the alpha diversity at locations immediately below effluent outflows, which contributed many taxa involved in wastewater treatment processes and nutrient cycling to the stream's microbial community. Unexpectedly, modeling of microbial community shifts along the stream was not controlled by concentrations of measured nutrients. Furthermore, partial recovery, in the form of decreasing abundances of bacteria and nutrients associated with wastewater treatment plant processes, nutrient cycling bacteria, and taxa associated with fecal and sewage sources, was observed between effluent sources, which we hypothesize is controlled by distance from effluent source. Antecedent moisture conditions were observed to impact overall microbial community diversity, with higher diversity occurring after rainfall. Finally, the efficacy of using a subset of the microbial community including the orders of Bifidobacteriales, Bacteroidales, and Clostridiales to estimate the degree of influence due to sewage and fecal sources was explored and verified.

Application of quantitative microbial risk assessment for selection of microbial reduction targets for hard surface disinfectants.

BACKGROUND: This quantitative microbial risk assessment (QMRA) included problem formulation for fomites and hazard identification for 7 microorganisms, including pathogenic Escherichia coli and E coli 0157:H7, Listeria monocytogenes, norovirus, Pseudomonas spp, Salmonella spp, and Staphylococcus aureus. The goal was to address a risk-based process for choosing the log10 reduction recommendations, in contrast to the current US Environmental Protection Agency requirements. METHOD: For each microbe evaluated, the QMRA model included specific dose-response models, occurrence determination of aerobic bacteria and specific organisms on fomites, exposure assessment, risk characterization, and risk reduction. Risk estimates were determined for a simple scenario using a single touch of a contaminated surface and self-inoculation. A comparative analysis of log10 reductions, as suggested by the US Environmental Protection Agency, and the risks based on this QMRA approach was also undertaken. RESULTS: The literature review and meta-analysis showed that aerobic bacteria were the most commonly studied on fomites, averaging 100 colony-forming units (CFU)/cm(2). Pseudomonas aeruginosa was found at a level of 3.3 × 10(-1) CFU/cm(2); methicillin-resistant S aureus (MRSA), at 6.4 × 10(-1) CFU/cm(2). Risk estimates per contact event ranged from a high of 10(-3) for norovirus to a low of 10(-9) for S aureus. CONCLUSION: This QMRA analysis suggests that a reduction in bacterial numbers on a fomite by 99% (2 logs) most often will reduce the risk of infection from a single contact to less than 1 in 1 million.