Microbial Risk Analysis最新文献

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.

Campylobacteriosis is an emerging foodborne illness which is frequently linked to the consumption of inadequately prepared poultry. The purpose of this study was to assess the risk associated with thermotolerant Campylobacter spp. in chicken döner kebab and to estimate the number of campylobacteriosis cases due to its consumption in Tlemcen city (Algeria). In order to estimate the human exposure to Campylobacter from a döner kebab meal and the number of human cases associated to this exposure, a stochastic risk model was developed, covering the whole food pathway. The model details the spread and transfer of Campylobacter in döner kebab from slaughter to consumption and the relationship between ingested dose and the probability of developing campylobacteriosis. Information and data for the development of the risk model were obtained in this study and when not available they were obtained from other research. Whenever possible, the data were represented by probability distributions rather than single point estimates, as they were to be integrated in a probabilistic estimation of the risk using Monte Carlo simulation. We found that 86.2% of chicken meat samples were contaminated, 17% higher than 4.0 Log cfu/g. 37.5% of döner kebab samples were contaminated. It is estimated that 5 infections will occur for every 100 döner kebab consumptions and that one person in sixty-six can get a campylobacteriosis by eating a döner kebab meal. The QMRA approach allows for an overall scenario analysis. It was found that intervention during slaughter and at the cooking process of döner kebab is probably most efficient to reduce Campylobacter health risks. Furthermore, important data gaps could be identified.

The probability of cysticercus bovis (CB) infection of cattle (cysticerci from Taenia saginata) in a country where T. saginata is not endemic (i.e. Australia) was assessed using a Quantitative Microbial Risk Assessment (QMRA) approach. Two important features of the QMRA were (i) a dose-response curve to describe ingestion of eggs of the helminth T. saginata (HE) by cattle and the development of cysticerci due to the infection, and (ii) characterisation of HE concentrations. Data limitations relating to HE quantification are described, and several other key variables provided the basis for a probabilistic QMRA model.

Data from over 554 sewage samples from 11 wastewater treatment plants (WWTPs) in Southern Australia indicated the background concentration of T. saginata eggs was low (<0.1 HE L^-1 measured, 0.003 HE L^-1 as an estimated baseline modelled on a ratio of Taenia:Ascaris determined from the literature). Such a low sewage concentration was estimated to require only a 2.2 log₁₀ reduction value (LRV) via sewage treatment to maintain the baseline risk of CB equivalent to background levels in Australia. However, to protect against potential future detectable outbreaks of Taeniasis in the human population and all potential exposure scenarios considered, a 3.5 LRV for WWTP was considered appropriate with confirmation by appropriate sewage monitoring. In addition, analysis of several specific exposure scenarios using the QMRA indicated that LRV credits (0.5 to 2.0 LRV) could decrease the required LRV for wastewater treatment based on the size of the WWTP and on-site management strategies (e.g. restriction of recycled water use for livestock drinking water, the years of exposure for cattle to sites irrigated with recycled water, and the use of fodder off-site). Without such measures, a HE LRV of 4.0 is recommend for WWTPs to ensure adequate protection of systems with no on-site controls.

As a response to the pandemic caused by SARS-Cov-2 virus, on 15 March 2020, the Republic of Serbia introduced comprehensive anti-epidemic measures to curb COVID-19. After a slowdown in the epidemic, on 6 May 2020, the regulatory authorities decided to relax the implemented measures. However, the epidemiological situation soon worsened again. As of 7 February 2021, a total of 406,352 cases of SARSCov-2 infection have been reported in Serbia, 4,112 deaths caused by COVID-19. In order to better understand the epidemic dynamics and predict possible outcomes, we have developed an adaptive mathematical model SEAIHRDS (S-susceptible, E-exposed, A-asymptomatic, I-infected, H-hospitalized, R-recovered, d-dead due to COVID-19 infection, S-susceptible). The model can be used to simulate various scenarios of the implemented intervention measures and calculate possible epidemic outcomes, including the necessary hospital capacities. Considering promising results regarding the development of a vaccine against COVID-19, the model is extended to simulate vaccination among different population strata. The findings from various simulation scenarios have shown that, with implementation of strict measures of contact reduction, it is possible to control COVID-19 and reduce number of deaths. The findings also show that limiting effective contacts within the most susceptible population strata merits a special attention. However, the findings also show that the disease has a potential to remain in the population for a long time, likely with a seasonal pattern. If a vaccine, with efficacy equal or higher than 65%, becomes available it could help to significantly slow down or completely stop circulation of the virus in human population.

The effects of vaccination depend primarily on: 1. Efficacy of available vaccine(s), 2. Prioritization of the population categories for vaccination, and 3. Overall vaccination coverage of the population, assuming that the vaccine(s) develop solid immunity in vaccinated individuals. With expected basic reproduction number of R_o=2.46 and vaccine efficacy of 68%, an 87% coverage would be sufficient to stop the virus circulation.

Biopreservatives such as plant-based antimicrobials and bacteriocinogenic starter cultures have been proposed as hurdles to increase microbiological safety of a variety of products, including cheese, and numerous studies have reported their pathogen inhibitory properties. For that reason, the objective of this meta-analysis was to summarise the inactivation of Listeria monocytogenes (LM), Staphylococcus aureus (SA) and Salmonella spp. (SS) in cheese attained by added lactic acid bacteria (LAB) and essential oils (EOs); and to compare the inhibitory effectiveness by application mode and specific antimicrobial. After systematic review, 1810 observations on log reduction data and study characteristics were extracted from 53 studies. Comparing among the factual methods of application of antimicrobials (in milk, cheese surface and incorporated in films), meta-regression models pointed out that addition of EOs to milk renders, as a whole, the lowest inhibitory effect against LM, SA and SS in the finished product; whereas for added LAB, incorporation in milk prompts a faster inactivation of LM than onto cheese surface. Lemon balm, sage and basil EOs showed the best inhibitory outcomes against LM and SA; whereas clove, oregano and bay EOs presented the highest bactericidal effect against SS. For a given increase in EO concentration, the application on cheese surface provides the greatest inhibitory effect against LM and SS, while EO-embedded films lead to a more rapid inactivation during maturation/storage. The experimental practice of inoculating the antimicrobial in cheese mixture should no longer be employed in challenge studies, since the meta-regression models have demonstrated that this application method biases the results, overestimating or underestimating the inhibitory effects of EOs or added LAB, respectively. This meta-analysis has also emphasised the need to further investigate the relationship between pathogen's inoculum size and their concentrations in time.

Salmonella has long presented a major problem for the food safety of broiler meat. As the popularity of alternatively produced (e.g. organic) broiler meat increases, an understanding of the food safety risks associated with these types of products is needed. The purpose of this study was to develop a retail-to-consumption quantitative microbial risk assessment model that could be used to estimate the differences in risk of salmonellosis acquired from the consumption of conventionally and alternatively produced broiler meat in the United States annually. Data were extracted and used to define distributions that could be used to estimate Salmonella growth during retail storage, transportation, and home storage, as well as concentration changes during preparation and due to cross-contamination. A Monte Carlo simulation with 100,000 iterations was performed to estimate the risk of infection per serving and total number of infections in the United States annually from both meat types. Sensitivity analyses were performed to determine the factors that were highly correlated with increased risk of salmonellosis. Conventionally produced chicken meat was estimated to have a median risk of infection per serving of 6.4 $\times$ 10⁻⁸ and cause an average of approximately 3,880,000 infections annually compared with a median risk of infection per serving of 7.7 $\times$ 10⁻⁸ and average of approximately 641,000 estimated infections for alternatively produced chicken. The sensitivity analysis identified cross-contamination of hands during meal preparation as the most important factor linked to risk. The ‘what-if’ scenario analysis estimated that using antimicrobial soap during hand washing after handling raw chicken can reduce the risk considerably. The developed risk assessment model provides information on the public health risk of conventionally and alternatively produced broiler meat. These results will be useful in determining the key intervention strategies to mitigate the food safety risks associated with the consumption of contaminated chicken products.