Microbial Risk Analysis最新文献

In the consumer goods sector, there is a rapid increase in launches of products that affect the human microbiome. Whilst more and more studies and product claims focus on the health benefits of the manipulation of microbiomes, ensuring that perturbations of the microbiome by the application of beauty and personal care products do not have potential unwanted consequences on the health of consumers is less well described in the scientific literature. There is currently no agreement on approaches to assess the possible impacts on consumer safety nor quantitatively defined endpoints of concern. We propose a 3-tier framework to qualitatively assess the potential impact of skin and oral microbiome perturbations on consumer health. The framework is established in accordance with the next generation risk assessment principles used in toxicology and avoids the use of animal testing. It was developed using a collaborative consultation including oral and skin microbiome experts, bioinformaticians and microbiological risk assessors. The first tier is based on a “history of safe use” concept, where the efficacy of a bioactive of interest is benchmarked against formulations generally regarded as safe because of their long history of consumer use. One of the endpoints identified during the development of the approach is that the microbiome's resilience is not compromised, that is its capacity to respond to challenges without going to dysbiosis. Therefore, the second tier is based on the notion of microbiome stability and its resilience to short term perturbations. The third tier aims to utilise next generation sequencing data and relate these to health status. Whilst 16S rRNA data have brought unprecedented resolution in determining the species present in microbiomes, we illustrate the challenges associated with predicting potential consequences for consumer health and disease from this type of data in a case study. With the development of whole genome sequencing technology and progress with integration of -omics data, we propose that the active functions of the microbiome rather than taxonomic classification should be the basis of a safety assessment. We suggest a research strategy to define the potential endpoints of concern quantitatively, based on a concomitant development of in vitro 3D tissue models in which the host response can be assessed, in silico approaches to describe the microbiome and longitudinal human studies to validate learnings in situ.

Lumpy skin disease (LSD) is a transboundary disease affecting bovine animals, which may result in severe economic implications. Ukraine is considered particularly vulnerable to LSD due to its proximity to regions where the virus is circulating. In addition, its ecological and environmental parameters can sustain, in summer, the spread of the disease in case it entered the country.

This qualitative risk assessment aimed to investigate the probability that LSD virus is introduced to Ukraine and, if introduced, what would be the probability of onward transmission in the country within the next year. The risk assessment followed the OIE import risk analysis for animals and animal products guidelines and was undertaken with the support of local experts via an expert elicitation workshop. A modified Delphi approach was used to gather experts inputs.

The illegally traded cattle was the pathway considered to have the highest probability of LSD introduction; however the probability was estimated to be low. When assessing the probability of an animal being exposed to the virus and further onward transmission in Ukraine, the highest probability estimate was related to flying vectors (high probability). During the expert opinion workshop, the Delphi approach helped to increase the agreement between experts and to assess the uncertainty related to some of the probability estimates.

Throughout the risk assessment, some data gaps were identified and highlighted. The lack of reliable data on animal movements and biosecurity in Ukraine were emphasized. Based on the elicited probability estimates, the local experts generated recommendations for risk management practices. To our knowledge, this is the first risk assessment performed on LSDV in Eastern Europe and the conceptual framework adopted can help other countries willing to do a risk assessment in a similar data scarce environment.

Populations in Arctic Canada are strongly connected to, and draw sustenance from, the physical environment. Recreation and food harvesting locations, however, may be impacted by the basic wastewater treatment and disposal processes used in the region. Within these mixed socio-ecological systems, people may unknowingly be exposed to wastewater pathogens, either by direct contact or indirectly through activities resulting in exposure to contaminated locally harvested food. The objectives of this research are to estimate microbial health risks attributable to wastewater effluent exposure in Arctic Canada and evaluate potential mitigation options. A participatory quantitative microbial risk assessment (QMRA) approach was used. Specifically, community knowledge and information describing human activity patterns in wastewater-impacted environments was used with microbial water quality data to model a range of exposure scenarios and risk mitigation options. In several exposure scenario results, estimated individual annual risk of acute gastrointestinal illness exceeds a proposed tolerable target of 10⁻³. These scenarios include shore recreation and consumption of shellfish harvested near primary mechanical treatment plants at low tide, as well as travel in wetland portions of passive treatment sites during spring freshet. These results suggest that wastewater effluent exposures may be contributing to gastrointestinal illness in some Arctic communities. Mitigation strategies, including improved treatment and interventions aimed at deterring access to disposal areas reduce risk estimates across scenarios to varying degrees. Overall, well-designed passive systems appear to be the most effective wastewater treatment option for Arctic Canada in terms of limiting and managing associated microbial health risks. This research demonstrates a novel application of QMRA and provides science-based evidence to support public health, water, and sanitation decisions and investment in Arctic regions.

To bridge the data gap on food poisoning caused by coagulase-positive staphylococci (CoPS), especially related to mayonnaise sauce served at Algerian pizzerias, this study aimed to assess the concentration of CoPS in mayonnaise and the probability of exceeding a critical concentration of ≥ 5 log CFU/g. The city of Ain Témouchent in West Algeria was taken as a case study.

A probabilistic assessment model was built, taking into account the initial contamination in freshly made mayonnaise and the potential growth before serving. Uncertainty and variability were integrated separately in the model. Uncertainty came from lack of data and model fitting error, variability from natural heterogeneity of biological materials (e.g., microbial strains) and temperature during cold storage.

The second-order Monte Carlo procedure was implemented in R using the mc2d package. The following pieces of data were generated to populate the model: CoPS were enumerated and characterized from 57 samples of mayonnaise served at pizzeria in Ain Témouchent city; challenge tests at 23 °C were performed in mayonnaise using three CoPS isolates. The following existing data were also gathered: meteorological data from Ain Témouchent were analysed to build a realistic scenario of storage, while a set of 43 and 35 values of the minimal and maximal growth limits of CoPS, respectively, were collected from the literature and analysed to inform a secondary predictive model describing the growth rate at various storage temperature conditions. A sensitivity analysis was performed to facilitate the interpretation of the results.

The results revealed a CoPS prevalence in freshly made mayonnaise of 25% [15%; 37%] with concentrations varying from 0.4 [0.3; 0.9] to 2.9 [2.4; 3.0] log CFU/g. The growth rates at 23 °C, based on challenge tests in mayonnaise, had a median value estimated to be 1.41 [1.17; 1.65] h ^− 1.

Concentration levels according to various scenarios of temperature and serving conditions were calculated. For instance, the median contamination of CoPS in mayonnaise after storage in a refrigerated display counter for 14 h during the hottest months of the year was estimated to be zero. However, the 95th percentile was estimated to be 3.6 [2.9; 4.2] log CFU/g. In this scenario, the probability of exceeding a critical concentration of ≥ 5 log CFU/g was estimated to be 1% [0.3%; 2%], which is low but not negligible.

These findings could be used to improve food safety policies and develop a risk management strategy to reduce the food poisoning associated with the consumption of ready-to-use foods in Algerian fast food restaurants.

In food processing, it is essential to guarantee the safety of microbial hazards. Microorganisms exist, transit and continuously grow along the food processing with hybrid evolutionary characteristics and uncertainties. Thus, a particular risk assessment is essential to effectively predict and evaluate the risk of microbial hazards in food processing. For such a purpose, we propose a comprehensive dynamic risk assessment approach based on a stochastic hybrid system (SHS). First, we formulate a dynamic evolution of microorganisms in food processing according to the SHS model. Second, we employ a Monte Carlo simulation to obtain the probability density functions of process characteristic information for microorganisms during processing. Additionally, we design a novel risk indicator of “hazard degree” to quantify the potential risks of microorganisms based on this process information. Finally, we present a case study of wheat flour processing to estimate the risk of mixed mildew on the basis of the SHS approach. Experimental results show that the proposed approach is feasible in modeling the hybrid evolution and handling uncertainties in predictions of microbial hazards. This study should prove to be a valuable reference to ensure food safety for risk management and decision-making departments.

The microbiological quality of fermented products such as salami are highly dependent on the interaction of environmental conditions during its fabrication. These effects may be predicted by mathematical modeling, and this approach has been adopted in several occasions. The aim of this study was to validate the Gamma concept model to predict Salmonella behavior during salami manufacturing in environmental conditions found in small scale Brazilian manufacturer. Furthermore, we simulated the growth and inactivation of Salmonella in salami considering a Brazilian scenario of contamination level of pork. Salami pieces were elaborated with a cocktail of five strains of Salmonella and subjected to maturation. For the fitted model construction, temperatures of 30°C during fermentation and 20°C during drying were used; while 25°C (fermentation) and 18°C (drying) were used in the validation study. Water activity (a_w) and pH were analyzed and Salmonella enumerated during maturation for fitting the curves. Salmonella isolates recovered at the end of the maturation were subjected to macrorestriction profiling (PFGE). The parameters obtained in the fitted Gamma concept model (μ_opt, δ1, δ2, α) were used to predict the bacteria behaviour in the validation study. During the maturation, Salmonella concentration decreased from 7.086 to 3.368 log₁₀ cfu/g (after 941 horas), and from 7.751 to 2.749 log₁₀ cfu/g (after 1121.5 horas) in the fitted model and validation study, respectively. The a_w was determinant for starting the microbial inactivation in the fitted model. Strains belonging to all PFGE-profiles inoculated in the salami pieces were detected in the end of the maturation. Regarding the simulation in a Brazilian scenario of pork contamination, the model predicted, in the upper 95% confidence interval, zero log₁₀ cfu/g of Salmonella after 670 h of maturation. The results indicate that the Gamma concept model provide a robust alternative to predict the concentration of Salmonella in salami considering the characteristics of production adopted in small industries in Brazil. The model predicted that in a scenario of higher environmental temperatures (30°C fermentation/ 20°C drying) Salmonella absence can be achieved after 15 days.

A predictive model was made for the logarithm of the thermal decimal reduction time (logD) of Salmonella enterica (D = time to 90% reduction by inactivation). The model was fitted with multiple linear regression from 521 logD-values reported in literature for laboratory media and foods highly varying in water activity and pH. The single regression model with temperature as the only variable had a high residual standard error (RSE) of 0.883 logD and no predictive value (fraction of variance explained (R²) < 0.001). Adding water activity, sugar content and pH as predictors resulted in a model with a lower RSE of 0.458 logD and an adjusted R² of 0.73. The model was validated by comparing 985 predicted with observed logD for S. enterica from other publications. The model was subsequently validated with 1498 published logD-values for inactivation of vegetative cells of nine other pathogenic bacteria genera (mainly Listeria monocytogenes, Escherichia coli, Clostridium perfringens, Cronobacter spp., Staphylococcus aureus, Yersinia enterocolitica) in or on a variety of laboratory media, meat, fish, dairy, nuts, fruits and vegetables. Regression analyses for validation with the 985 logD of S. enterica and 2483 logD of all genera show deviations from the expected slope of 1 (both 0.81) and the expected intercept of 0 (0.04 and 0.19 logD respectively). However, only 0.7% and 2% respectively of the new logD (expected: 0.5%) were observed above the 99% prediction interval of the original S. enterica model based on 521 logD. The findings suggest that i) the variability of thermal resistance of strains within species is larger than between genera and species; ii) one generic predictive model, also accounting for variability, suffices for designing the thermal inactivation of a variety of vegetative pathogenic bacteria in many food types.

The potential risk-based improvement of the Salmonella Dublin surveillance programme in Danish dairy herds was investigated, considering herd status misclassifications due to testing errors. The programme started in October 2002. Currently (early 2021) all dairy herds are classified based on quarterly bulk tank milk (BTM) testing with an indirect antibody ELISA (iELISA). Over the last two decades, the prevalence of herds classified as “likely infected” (levels 2,3) reduced remarkably. However, since 2015, the apparent prevalence has increased again, calling for improved surveillance and control to protect animal and human health. A deterministic simulation model based on data (2018–2019) from 2283 dairy herds in level 1 (“most likely free from infection”), was developed to estimate status misclassifications as false negative (FN) and false positive (FP) herds, under two testing strategies. These were: (A) the current system based on quarterly BTM testing only, and (B) an alternative strategy based on additional blood testing of up to eight calves, within herds at high risk of infection (HR). Both strategies were evaluated using three risk classification methods (I to III) and four sensitivity analysis scenarios (SA1-4), where different temporal performances were simulated for the iELISA in BTM. To apply strategy B, the best high-risk classification method (II), which combined managerial applicability and minimized errors, would require testing approximately 1000 calves across 127 HR herds. In that case, strategy A would cause 3 FNs and 67 FPs, by assuming annual BTM sensitivity (BTMSe) 95% conditional on a 1-year disease history and specificity (BTMSp) 97%. Whereas strategy B could cause a similar number of FNs, but 7 FPs more, assuming a sensitivity (Se) of 77% and specificity (Sp) of 99% in individual blood-samples (SA1). Assuming also quarterly BTMSe 53% and BTMSp 99.9% (SA4), strategy A derived 28 FNs and 2 FPs, while strategy B resulted in 6 FNs less and 8 FPs more. Therefore, strategy B could improve early detection of infected HR herds, while strategy A would avoid more unnecessary restrictions in false-positive herds. This improves knowledge on the potential use of additional blood testing in HR herds and illustrates how deterministic modelling can be used to improve disease surveillance and control.

Indicator microorganisms are monitored in agricultural waters to foster produce safety. Various prediction models are used to estimate the population of indicator microorganisms and pathogens when no observation is available. The purpose of this study was to compare the performance of regression models with count data (zero-inflated Poisson and hurdle negative binomial) to artificial neural network and ensemble models (random forest and AdaBoost) for the prediction of generic Escherichia coli population in agricultural surface waters in relation with weather station measurements. Two-part count data models were built on E. coli population count frequencies (0, [1,10), [10,100), [100,1000), [1000, 10000), (>=10000)) based on the data structure. The use of artificial neural network, AdaBoost, and random forest were determined based on the mean absolute error (MAE) value over pre-tested six models. The MAE was also used to compare the performance of two-part count data models with artificial neural network and ensemble models. Over-dispersed E. coli population count frequencies was calculated between 2.2 and 52.2% for all ponds. Observed and predicted zero E. coli population counts for all ponds were matched from 82 to 100% for zero-inflated Poisson and 100% for hurdle negative binomial regression models. Overdispersion reduced the performance of tested models. AdaBoost-Twelve Estimators had the best performance with the lowest MAE values for all ponds (from 0.87 to 46.60). The ensemble models used in this study provided more promising performance when compared to tested regression models with count data.

The 2020 Olympic/Paralympic Games have been postponed to 2021, due to the COVID-19 pandemic. We developed a model that integrated source–environment–receptor pathways to evaluate how preventive efforts can reduce the infection risk among spectators at the opening ceremony of Tokyo Olympic Games. We simulated viral loads of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emitted from infectors through talking/coughing/sneezing and modeled temporal environmental behaviors, including virus inactivation and transfer. We performed Monte Carlo simulations to estimate the expected number of newly infected individuals with and without preventive measures, yielding the crude probability of a spectator being an infector among the 60,000 people expected to attend the opening ceremony. Two indicators, i.e., the expected number of newly infected individuals and the newly infected individuals per infector entry, were proposed to demonstrate the extent of achievable infection risk reduction levels by implementing possible preventive measures. A no-prevention scenario produced 1.5–1.7 newly infected individuals per infector entry, whereas a combination of cooperative preventive measures by organizers and the spectators achieved a 99% risk reduction, corresponding to 0.009–0.012 newly infected individuals per infector entry. The expected number of newly infected individuals was calculated as 0.005 for the combination of cooperative preventive scenarios with the crude probability of a spectator being an infector of 1 × 10⁻⁵. Based on our estimates, a combination of cooperative preventions between organizers and spectators is required to prevent a viral spread at the Tokyo Olympic/Paralympic Games. Further, under the assumption that society accepts < 10 newly infected persons traced to events held during the entire Olympic/Paralympic Games, we propose a crude probability of infectors of < 5 × 10⁻⁵ as a benchmark for the suppression of the infection. This is the first study to develop a model that can assess the infection risk among spectators due to exposure pathways at a mass gathering event.