Quantitative Microbial Risk Assessment Based on Whole Genome Sequencing Data: Case of Listeria monocytogenes

The application of high-throughput DNA sequencing technologies (WGS) data remain an increasingly discussed but vastly unexplored resource in the public health domain of quantitative microbial risk assessment (QMRA). This is due to challenges including high dimensionality of WGS data and heterogeneity of microbial growth phenotype data. This study provides an innovative approach for modeling the impact of population heterogeneity in microbial phenotypic stress response and integrates this into predictive models inputting a high-dimensional WGS data for increased precision exposure assessment using an example of Listeria monocytogenes. Finite mixture models were used to distinguish the number of sub-populations for each of the stress phenotypes, acid, cold, salt and desiccation. Machine learning predictive models were selected from six algorithms by inputting WGS data to predict the sub-population membership of new strains with unknown stress response data. An example QMRA was conducted for cultured milk products using the strains of unknown stress phenotype to illustrate the significance of the findings of this study. Increased resistance to stress conditions leads to increased growth, the likelihood of higher exposure and probability of illness. Neglecting within-species genetic and phenotypic heterogeneity in microbial stress response may over or underestimate microbial exposure and eventual risk during QMRA.

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Quantitative Microbial Risk Assessment for Workers Exposed to Bioaerosol in Wastewater Treatment Plants Aimed at the Choice and Setup of Safety Measures

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph15071490 ◽

2018 ◽

Vol 15 (7) ◽

pp. 1490 ◽

Cited By ~ 21

Author(s):

Annalaura Carducci ◽

Gabriele Donzelli ◽

Lorenzo Cioni ◽

Ileana Federigi ◽

Roberto Lombardi ◽

...

Keyword(s):

Risk Assessment ◽

Sensitivity Analysis ◽

Wastewater Treatment ◽

Health Risk ◽

Average Risk ◽

Quantitative Microbial Risk Assessment ◽

Exposure Limits ◽

Microbial Risk Assessment ◽

Microbial Risk ◽

The Impact

Biological risk assessment in occupational settings currently is based on either qualitative or semiquantitative analysis. In this study, a quantitative microbial risk assessment (QMRA) has been applied to estimate the human adenovirus (HAdV) health risk due to bioaerosol exposure in a wastewater treatment plant (WWTP). A stochastic QMRA model was developed considering HAdV as the index pathogen, using its concentrations in different areas and published dose–response relationship for inhalation. A sensitivity analysis was employed to examine the impact of input parameters on health risk. The QMRA estimated a higher average risk in sewage influent and biological oxidation tanks (15.64% and 12.73% for an exposure of 3 min). Sensitivity analysis indicated HAdV concentration as a predominant factor in the estimated risk. QMRA results were used to calculate the exposure limits considering four different risk levels (one illness case per 100, 1.000, 10.000, and 100.000 workers): for 3 min exposures, we obtained 565, 170, 54, and 6 GC/m3 of HAdV. We also calculated the maximum time of exposure for each level for different areas. Our findings can be useful to better define the effectiveness of control measures, which would thus reduce the virus concentration or the exposure time.

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Multiple-Strain Approach and Probabilistic Modeling of Consumer Habits in Quantitative Microbial Risk Assessment: A Quantitative Assessment of Exposure to Staphylococcal Enterotoxin A in Raw Milk

Journal of Food Protection ◽

10.4315/0362-028x.jfp-15-235 ◽

2016 ◽

Vol 79 (3) ◽

pp. 432-441 ◽

Cited By ~ 2

Author(s):

MATTEO CROTTA ◽

RITA RIZZI ◽

GIORGIO VARISCO ◽

PAOLO DAMINELLI ◽

ELENA COSCIANI CUNICO ◽

...

Keyword(s):

Risk Assessment ◽

Consumer Behavior ◽

Raw Milk ◽

Staphylococcal Enterotoxin ◽

Quantitative Microbial Risk Assessment ◽

Staphylococcal Enterotoxin A ◽

Microbial Risk Assessment ◽

Microbial Risk ◽

Household Level ◽

The Impact

ABSTRACT Quantitative microbial risk assessment (QMRA) models are extensively applied to inform management of a broad range of food safety risks. Inevitably, QMRA modeling involves an element of simplification of the biological process of interest. Two features that are frequently simplified or disregarded are the pathogenicity of multiple strains of a single pathogen and consumer behavior at the household level. In this study, we developed a QMRA model with a multiple-strain approach and a consumer phase module (CPM) based on uncertainty distributions fitted from field data. We modeled exposure to staphylococcal enterotoxin A in raw milk in Lombardy; a specific enterotoxin production module was thus included. The model is adaptable and could be used to assess the risk related to other pathogens in raw milk as well as other staphylococcal enterotoxins. The multiple-strain approach, implemented as a multinomial process, allowed the inclusion of variability and uncertainty with regard to pathogenicity at the bacterial level. Data from 301 questionnaires submitted to raw milk consumers were used to obtain uncertainty distributions for the CPM. The distributions were modeled to be easily updatable with further data or evidence. The sources of uncertainty due to the multiple-strain approach and the CPM were identified, and their impact on the output was assessed by comparing specific scenarios to the baseline. When the distributions reflecting the uncertainty in consumer behavior were fixed to the 95th percentile, the risk of exposure increased up to 160 times. This reflects the importance of taking into consideration the diversity of consumers' habits at the household level and the impact that the lack of knowledge about variables in the CPM can have on the final QMRA estimates. The multiple-strain approach lends itself to use in other food matrices besides raw milk and allows the model to better capture the complexity of the real world and to be capable of geographical specificity.

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A quantitative microbial risk assessment model for Listeria monocytogenes in RTE sandwiches

Microbial Risk Analysis ◽

10.1016/j.mran.2018.04.003 ◽

2018 ◽

Vol 9 ◽

pp. 11-21 ◽

Cited By ~ 6

Author(s):

E. Tirloni ◽

S. Stella ◽

L.V. de Knegt ◽

G. Gandolfi ◽

C. Bernardi ◽

...

Keyword(s):

Risk Assessment ◽

Listeria Monocytogenes ◽

Assessment Model ◽

Quantitative Microbial Risk Assessment ◽

Risk Assessment Model ◽

Microbial Risk Assessment ◽

Microbial Risk

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Potential discharge, attenuation and exposure risk of SARS-CoV-2 in natural water bodies receiving treated wastewater

npj Clean Water ◽

10.1038/s41545-021-00098-2 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Manish Kumar ◽

Md. Alamin ◽

Keisuke Kuroda ◽

Kiran Dhangar ◽

Akihiko Hata ◽

...

Keyword(s):

Risk Assessment ◽

Potential Risk ◽

Natural Water ◽

Water Bodies ◽

Treated Wastewater ◽

Quantitative Microbial Risk Assessment ◽

Microbial Risk Assessment ◽

Microbial Risk ◽

The Impact ◽

Natural Water Bodies

AbstractRecently reported detection of SARS-CoV-2 in wastewater around the world has led to emerging concerns on potential risk in water bodies receiving treated wastewater effluent. This review aims to provide an up-to-date state of key knowledge on the impact of SARS-CoV-2 in natural water bodies receiving treated wastewater. In this review, SARS-CoV-2 concentrations in wastewater, expected removal in WWTPs, and possible dilution and decay in water bodies are reviewed based on past studies on SARS-CoV-2 and related enveloped viruses. We suggest a quantitative microbial risk assessment (QMRA) framework to estimate the potential risk of SARS-CoV-2 in natural water bodies through various water activities. Dose–response model of SARS-CoV and Poisson’s distribution is employed to estimate possible viral ingestion and the annual chance of infection through several water activities in natural water bodies. Finally, future perspectives and research needs have been addressed to overcome the limitations and uncertainty in the risk assessment of SARS-CoV-2 in natural water bodies.

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Estimating Listeria monocytogenes Growth in Ready‐to‐Eat Chicken Salad Using a Challenge Test for Quantitative Microbial Risk Assessment

Risk Analysis ◽

10.1111/risa.13546 ◽

2020 ◽

Vol 40 (11) ◽

pp. 2427-2441

Author(s):

Rita Bernardo ◽

António Salvador Barreto ◽

Telmo Nunes ◽

Ana Rita Henriques

Keyword(s):

Risk Assessment ◽

Listeria Monocytogenes ◽

Challenge Test ◽

Quantitative Microbial Risk Assessment ◽

Microbial Risk Assessment ◽

Microbial Risk

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Retrospective Analysis of a Listeria monocytogenes Contamination Episode in Raw Milk Goat Cheese Using Quantitative Microbial Risk Assessment Tools

Journal of Food Protection ◽

10.4315/0362-028x.jfp-12-074 ◽

2012 ◽

Vol 75 (12) ◽

pp. 2122-2135 ◽

Cited By ~ 3

Author(s):

L. DELHALLE ◽

M. ELLOUZE ◽

M. YDE ◽

A. CLINQUART ◽

G. DAUBE ◽

...

Keyword(s):

Risk Assessment ◽

Listeria Monocytogenes ◽

Assessment Tools ◽

Quantitative Microbial Risk Assessment ◽

Production Chain ◽

Microbial Risk Assessment ◽

Microbial Risk ◽

Median Increase ◽

Goat’S Milk ◽

Risk Assessment Tools

In 2005, the Belgian authorities reported a Listeria monocytogenes contamination episode in cheese made from raw goat's milk. The presence of an asymptomatic shedder goat in the herd caused this contamination. On the basis of data collected at the time of the episode, a retrospective study was performed using an exposure assessment model covering the production chain from the milking of goats up to delivery of cheese to the market. Predictive microbiology models were used to simulate the growth of L. monocytogenes during the cheese process in relation with temperature, pH, and water activity. The model showed significant growth of L. monocytogenes during chilling and storage of the milk collected the day before the cheese production (median increase of 2.2 log CFU/ml) and during the addition of starter and rennet to milk (median increase of 1.2 log CFU/ml). The L. monocytogenes concentration in the fresh unripened cheese was estimated to be 3.8 log CFU/g (median). This result is consistent with the number of L. monocytogenes in the fresh cheese (3.6 log CFU/g) reported during the cheese contamination episode. A variance-based method sensitivity analysis identified the most important factors impacting the cheese contamination, and a scenario analysis then evaluated several options for risk mitigation. Thus, by using quantitative microbial risk assessment tools, this study provides reliable information to identify and control critical steps in a local production chain of cheese made from raw goat's milk.

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