Microbial Risk Analysis最新文献

Salmonellosis involving chicken meat is one of the most frequent foodborne diseases registered worldwide. Many studies report the prevalence of Salmonella spp. on chicken meat; however, data are limited or variable. To perform stochastic Quantitative Microbial Risk Analysis, it is essential to input reliable data to estimate the risks, and the Bayesian meta-analysis model allows incorporating the uncertainty of the data into parameters which increases the robustness of the model. In this manuscript, we conduct a systematic review and a logit-normal hierarchical Bayesian meta-analysis model to assess the posterior distribution of Salmonella spp. prevalence of raw chicken meat. The posterior distribution of Salmonella spp. was reported according to carcass processing (whole carcass or cuts); cold status (fresh meat or frozen); place of sampling (retail or slaughterhouse), and geographical region (Brazil, Latin America, North America, Africa, Asia, and Europe). To implement the posterior distribution as uncertainty in stochastic a model, parameters were obtained by linear combination of the posterior distributions of the model. The percentual of variation regarding the heterogeneity between studies is 33.93%. Carcass processing and cold status do not influence Salmonella spp. prevalence. Raw chicken meat collected at slaughterhouses had a 4% higher chance of being positive for Salmonella spp. than those taken at retail. However, this small difference seems to be of minor relevance given the large 95% credible interval around the parameter. The posterior distribution shows lower Salmonella spp. prevalence for Latin America, Brazil, Africa, Europe when compared to North America and Asia. In the sensitivity analysis, the parameters ${\beta }_{cold}$, ${\beta }_{sample}$, and ${\beta }_{processing}$ were weakly influenced by the priors, however, the relevance of the priors was more evident for the geographic region related parameters. Salmonella Enteritidis was the most widespread serovar identified and only three studies verified the concentration of Salmonella spp. but we were not able to conduct a meta-analysis because the studies omitted the standard deviation.

There is a need to evaluate and minimize the risk of novel coronavirus infections at mass gathering events, such as sports. In particular, to consider how to hold mass gathering events, it is important to clarify how the local infection prevalence, the number of spectators, the capacity proportion, and the implementation of preventions affect the infection risk. In this study, we used an environmental exposure model to analyze the relationship between infection risk and infection prevalence, the number of spectators, and the capacity proportion at mass gathering events in football and baseball games. In addition to assessing risk reduction through the implementation of various preventive measures, we assessed how face-mask-wearing proportion affects infection risk. Furthermore, the model was applied to estimate the number of infectors who entered the stadium and the number of newly infected individuals, and to compare them with actual reported cases. The model analysis revealed an 86–95% reduction in the infection risk due to the implementation of face-mask wearing and hand washing. Under conditions in which vaccine effectiveness was 20% and 80%, the risk reduction rates of infection among vaccinated spectators were 36% and 96%, respectively. Among the individual measures, face-mask wearing was particularly effective, and the infection risk increased as the face-mask-wearing proportion decreased. A linear relationship was observed between infection risk at mass gathering events and the infection prevalence. Furthermore, the number of newly infected individuals was also dependent on the number of spectators and the capacity proportion independent of the infection prevalence, confirming the importance of considering spectator capacity in infection risk management. These results highlight that it is beneficial for organisers to ensure prevention compliance and to mitigate or limit the number of spectators according to the prevalence of local infection. Both the estimated and reported numbers of newly infected individuals after the events were small, below 10 per 3–4 million spectators, despite a small gap between these numbers.

This study aimed at, and developed, a climate-driven model for predicting the abundance of V. parahaemolyticus in oysters based on the local climatological and environmental conditions in Taiwan. The predictive model was constructed using the elastic net machine learning method, and the most influential predictors were evaluated using a permutation-based approach. The abundance of V. parahaemolyticus in oysters in different seasons, time horizons, and representative concentration pathways (RCPs) were predicted using the Elastic-net machine learning model. The results showed: (1) the variation in wind speed or gust wind speed, sea surface temperature, precipitation, and pH influenced the prediction of V. parahaemolyticus concentration in oysters, and (2) the level of V. parahaemolyticus in oysters in Taiwan was projected to be increased by 40–67% in the near future (2046–2065) and by 39–86% by the end of twentieth-century (2081–2100) if the global temperature continues to increase due to climate change. The findings in this study may be used as inputs for quantifying the V. parahaemolyticus infection risk from eating this seafood in Taiwan.

Conventional wastewater treatment plants do not effectively remove parasitic protozoa resulting in their presence in wastewater effluent. Therefore, agricultural reuse of wastewater effluent could be a route of Cryptosporidium transmission into the food chain. Samples of secondary wastewater, wastewater-irrigated soil and vegetable samples were collected from an experimental field and analyzed using real-time PCR for quantification of Cryptosporidium oocysts. Quantitative microbial risk analysis (QMRA) was performed to determine the annual disease burden from Cryptosporidium associated with consumption of wastewater-irrigated vegetables. Detection of Clostridium perfringens spores as a potential indicator for the presence of Cryptosporidium was also performed. Cryptosporidium was observed in 64% of the effluent samples ranging from 16 to 162 oocysts L⁻¹, whereas no oocysts were detected in wastewater-irrigated soil and the vegetable samples. The mean annual disease burden for consumption of lettuce (6.6 × 10⁻⁵ DALY per person per year (pppy)) was higher than spring onion (3.4 × 10⁻⁶ DALY pppy) which both exceeding the WHO guideline of 10⁻⁶ DALYs pppy. However, the disease burden was within the acceptable range considering the less stringent level of 10⁻⁴ DALY pppy. Clostridium spores were detected in 92% of the effluent samples with no relationship with the presence of Cryptosporidium. The results of this study suggest that agricultural reuse of treated wastewater may not be a major health concern of cryptosporidiosis in a semi-arid region. However, because of the higher risk from lettuce, the choice of an appropriate crop would be needed to completely meet the WHO recommendations for safe reuse of wastewater. The accuracy of the QMRA model could be improved by further investigating the decay rate of oocysts in the same region.

Zoonotic tuberculosis, caused by Mycobacterium bovis, is mainly linked to the consumption of raw milk from infected cows. In many countries, cases are rare, due to pasteurisation of milk and national programmes to control M. bovis infection in cattle. Speciality cheeses, which are often produced using raw milk, present challenges to risk managers in countries where M. bovis is endemic or (re-) emerging. A key concern is the potential risk of zoonotic transmission of M. bovis via the consumption of dairy products produced using raw milk originating from herds infected with M. bovis (bovine tuberculosis, bTB). The aim of this study was to determine parameter estimates to support the future risk assessment of M. bovis in raw milk cheese. In this study, the hazard was identified as viable M. bovis organisms in raw milk cheese. Parameters of interest in this study related to exposure assessment (the estimated extent of human exposure to viable M. bovis organisms) and hazard characterisation (the risk posed to human health following exposure to viable M. bovis organisms). The pathway for exposure assessment was visualised using a conceptual framework, which describes the steps through which M. bovis may be transferred from an infected animal(s) through manufacturing to the final cheese product. Estimation of most parameters for exposure assessment and hazard characterisation was undertaken using systematic literature reviews. Estimates could be derived for many parameters, but not all. In particular, the number of M. bovis organisms excreted in the milk and present in the faeces of infected cattle are unknown. There is zero-tolerance for M. bovis in foods of animal origin destined for human consumption in European legislation. This work has highlighted important gaps in knowledge, and areas for further research. For each of the parameters for which estimates are available, we outline the types/sources of uncertainty as reflected in relevant published papers. In any future application of these parameter estimates, care will be needed to reflect the uncertainties associated with these elements of exposure assessment.

Microbial risk assessment is a valuable tool to provide scientific evidence to control food safety. Therefore, this study evaluated the risk of foodborne illness caused by Staphylococcus aureus in ready-to-eat (RTE) salad products and economic impact. The prevalence of S. aureus in salads, the RTE salad consumption, and distribution conditions (time and temperature) in market parameters were determined, and the data were analysed using the @RISK program to determine the appropriate probabilistic distribution. Predictive models were developed to describe the fate of S. aureus under distribution conditions. A simulation model was prepared with the collected data to calculate the risk of illness per person per day, and this risk was used to calculate the economic impact. S. aureus was detected in 2% of RTE salads, and the initial contamination level was calculated using the Beta distribution. Baranyi model was used to calculate the maximum specific growth rate (μ_max), lag phase duration (LPD), and the secondary models well described the temperature effect on LPD and μ_max with R² values of 0.973–0.979. Also, the root mean square error values of 0.362 suggested that the model performance was appropriate. Lognormal distribution estimated that the average daily consumption amount and ratio was 137.7 g and 9.8%, respectively. The simulation model showed that the average probability of S. aureus foodborne illness following RTE salad consumption was 2.1 × 10⁻⁹ per person per day. The probability was used to estimate the socio-economic burden, and the annual socio-economic cost was calculated as $48,343.

Human and animal feces are one of the main pollutants in drinking water systems (DWS). Both sources of fecal pollution are related to environmental conditions, such as poor land use management and little micro-basin protection. Cryptosporidium sp. and Giardia sp. are zoonotic protozoan water and foodborne transmitted parasitic pathogens and a frequent cause of diarrhea in children in low- and middle-income countries. In Latin America, DWS microbial risk assessment of these parasites is scarce. The aim of this study was to apply land use analysis, quantitative microbial risk assessment (QMRA), and disease burden (DALY; disability-adjusted life years) estimation of Cryptosporidium sp. and Giardia sp. for DWS that supplies 1.4% of the Costa Rican population. Land use analysis showed pollution sources and urban activities at the catchment area of all micro-basins, demonstrating the pollution's vulnerability, especially near to catchment sites of the DWS. The risk of infection by Cryptosporidium sp. and Giardia sp. was higher than US EPA standards, and the health burdens were above WHO recommendations. The higher risk of infection was observed in the micro-basins with the higher area of urban use, mainly in the buffer zones. QMRA and land use analysis are useful tools for the characterization of possible pollution foci in hazard identification.

Cross-contamination is an important event for bacterial transfer throughout the pork production chain. In Brazil, Salmonella sp. is the most relevant hazard in the pork industry, and further knowledge concerning its contamination is essential for in-depth risk assessments. Thus, we aimed to assess the transfer probability of Salmonella sp. between a knife and pork in a domestic kitchen scenario to provide parametrization for incorporating transfer of Salmonella sp. in risk assessment models. To estimate Salmonella Typhimuium transfer rates between contaminated pork and a knife blade during cutting, 23 independent experiments were performed. A Bayesian inference was utilized to determine the transfer probability, capturing the uncertainty generated in the transfer probability experiments. The mean transfer probability was 0.03 for knife to pork [0.029; 0.032] 95% credible interval (CrI) and 0.0042 for pork to knife [0.0041; 0.0043] 95% CrI. The probabilistic estimate of the transfer probability of Salmonella sp. during pork cutting gives insights on a relevant parameter for the consumer phase of the pork production industry in Brazil, allowing for enhanced risk assessment models.

This study was carried out to assess the bacteriological profile and to model the combined effects of physicochemical parameters on Escherichia coli ATCC 25922 levels in Amlou, a typical Moroccan traditional spread food made with argan oil, sweet almonds, and honey or sugar. Microbiological analyses of the samples (n = 44), collected from different traditional producers, included total viable count (TVC), total coliforms (TC), fecal coliforms (FC), E. coli, lactic acid bacteria (LAB), yeasts and molds (YM), Staphylococcus aureus, Salmonella, and Listeria spp. The results showed that 61% of samples met the Moroccan hygiene standards for vegetable paste spreads, with contamination levels of 4.21, 2.94, 2.38, 1.87, and 1.50 log cfu/g for TVC, YM, TC, FC, and E. coli, respectively. S. aureus was detected in 6.82% of Amlou samples, while Salmonella and Listeria spp. were not detected in any of the analyzed samples. On the other hand, the effects of water activity (a_w) (0.44, 0.50, 0.53), temperature (23, 30 °C), and pH (5.5, 6.5) on the kinetics parameters, inactivation rate (IR, log cfu/g/d), and shoulder period (SP, d) of E. coli ATCC 25922, were investigated. The survival curves generated under different conditions were fitted using the Baranyi model. Secondary modeling for the combined effects of a_w, temperature, and pH on the survival parameters was carried out using a polynomial equation. Finally, the goodness of fit was assessed for the survival kinetics of E. coli. Root mean square error (RMSE) and standard error of prediction (%SEP) obtained were 0.20 and 10.22% for IR and 0.24 and 40.01% for SP. These models can provide an estimate of E. coli inactivation in Amlou. Further studies should consider other factors such as argan oil concentration and amount of protein before being applied to ensure food safety for E. coli control in Amlou.

Campylobacter cross-contamination of Danish broiler flocks at slaughterhouses was investigated using data from two national surveillance components and from ad-hoc sampling. The animal level (AL) and food safety (FS) components from 2018 were compared. The AL component contained results of PCR on pools of cloacal swabs from 3,012 flocks processed at two Danish slaughterhouses (S1-S2), while the FS component regarded culture testing of leg skins from 999/3,012 flocks. The monthly “apparent” (AP) and “true” flock prevalence (TP) were estimated. Agreement between components was measured in percentage and in weighted-Kappa values. The relationship between the occurrence of cross-contamination (flock positive only in the FS component = cross-contaminated or CC, vs. flock negative in both components or NegBoth), slaughterhouse and surveillance period (quarter: Q1 to Q4) was evaluated by a generalized linear mixed effects (GLM) model. Thereafter, a linear mixed effects (LME) model was used to investigate the relationship between the level of meat contamination of carcass positive flocks (y = log10 colony forming units per gram, cfu/g), slaughterhouse, surveillance period, and flock type (CC vs. positive in both components or PosBoth). For both models, the farm was the random effect. Finally, in autumn 2019, ad-hoc field investigations were carried out testing caecal and neck skin samples, from two consecutive flocks at S1 and S2. Whole genome sequencing (WGS) was performed on isolates, for multilocus sequence typing (MLST) and single nucleotide polymorphisms (SNP) analysis. The monthly TP was always higher for the FS than for the AL component. Agreement between the components was substantial, but 8.1–8.6% of the flocks were CC. Those had median cfu/g 21–28 times lower than that of PosBoth flocks. In the GLM model, the explanatory variables were both significant (P-value <0.05). For example, the odds ratios (ORs) were 8.4 (95% CI: 4.0; 17.6) for Q3 vs. Q1, and 3.1 (1.8; 5.2) for S2 vs. S1. In the LME model, the flock type and the interaction between the other two variables, were significant. In the field study, a caecal positive flock was succeeded by an initially negative flock, in one out of five sampling sessions at S2. The cecal negative flock was positive in 58.3% of the neck skins with the isolate genetically similar to that from the caecal positive flock. Those results show that cross-contamination can be affected by surveillance periods and slaughterhouses, and it can contribute significantly to the TP of carcass positive flocks.