Microbial Risk Analysis最新文献

Monkeypox (MPX) is a zoonotic infectious disease caused by Monkeypox virus (MPXV), an enveloped DNA virus belonging to the Poxviridae family and the Orthopoxvirus genus. Since early May 2022, a growing number of human cases of Monkeypox have been reported in non-endemic countries, with no history of contact with animals imported from endemic and enzootic areas, or travel to an area where the virus usually circulated before May 2022. This qualitative risk assessment aimed to investigate the probability that MPXV transmission occurs through food during its handling and consumption. The risk assessment used “top-down” (based on epidemiological data) and “bottom-up” (following the agent through the food chain to assess the risk of foodborne transmission to human) approaches, which were combined. The “top-down” approach first concluded that bushmeat was the only food suspected as a source of contamination in recorded cases of MPXV, by contact or ingestion. The “bottom-up” approach then evaluated the chain of events required for a human to become ill after handling or consuming food. This approach involves several conditions: (i) the food must be contaminated with MPXV (naturally, by an infected handler or after contact with a contaminated surface); (ii) the food must contain viable virus when it reaches the handler or consumer; (iii) the person must be exposed to the virus and; (iv) the person must be infected after exposure. Throughout the risk assessment, some data gaps were identified and highlighted. The conclusions of the top-down and bottom-up approaches are consistent and suggest that the risk of transmission of MPXV through food is hypothetical and that such an occurrence was never reported. In case of contamination, cooking (e.g., 12 min at 70°C) could be considered effective in inactivating Poxviridae in foods. Recommendations for risk management are proposed. To our knowledge, this is the first risk assessment performed on foodborne transmission of MPXV.

The use of reclaimed water for irrigation is one of the most common strategies to address water scarcity in many regions of the world, and many of the most intensive production areas of fruits and vegetables rely on these water sources to produce high quality fresh produce. However, there are still concerns regarding the microbiological quality and safety of products irrigated with reclaimed water. In this study, we propose an innovative approach to evaluate factors affecting this potential risk. Using the concentration of Escherichia coli as a proxy (an indicator) for bacterial pathogens, we define a probabilistic model divided in two parts. The variation in bacterial concentration during water reclamation and distribution is described by a Bayesian Network, where variability and uncertainty are included by data augmentation using non-parametric bootstrap. The second part, is a stochastic model that predicts the microbial concentration on the plant accounting for cross-contamination and bacterial survival.

The novel approach is used to evaluate the factors affecting the contamination and potential risk associated with the consumption of leafy greens irrigated with reclaimed water from two wastewater treatment plants (WWTP) in several growing fields located in the south-east of Spain. According to the model, the microbial concentration in the outlet of the WWTP has a relatively low impact on the probability of E. coli concentrations on the plant to exceed 2 log CFU/g (a common threshold), and the impact of the irrigation system (overhead, drip or irrigation) would be insignificant. Instead, the probability of exceedance would be dominated by soil-to-plant contamination due to splashing, when organic amendments are used as fertilizers. Therefore, provided every step in water reclamation from water generation to point of use is kept safe, current reclamation treatments from WWTPs would be effective in reducing microbial concentrations in reclaimed water.

Share tables (ST) allow students to share unwanted food items with other students in school cafeterias, making them a possible method to reduce food waste and insecurity. This study assesses potential food safety risks and food security benefits of a ST system, to assess if future work on STs is warranted. But food safety concerns from stakeholders hinder ST implementation. A quantitative microbial risk assessment was developed to (i) predict food safety risk (specifically norovirus transmission via apples) associated with the implementation of STs in school cafeterias, (ii) identify effective mitigation strategies to prevent illness, and (iii) screen for potential food security benefits. To estimate the impact and efficacy of mitigation strategies, illness prevalence was compared between 13 different what-if scenarios. Results show that STs modestly increase the mean illness prevalence from 1.5% (2.5–97.5th percentile: 0.52–2.7%) to 1.6% (2.5–97.5th percentile: 0.67–2.8%), a 6.8% increase in illness prevalence. Mitigation strategies that focus on managing incoming norovirus loads are predicted to be most effective. Specifically, efficient student handwashing and hand sanitizing reduced the illness prevalence in a ST system to 43.6 and 41.9%, respectively. Other mitigation strategies, such as washing and wrapping fruit, are predicted to be less effective. Other results show that STs have the potential to reduce food waste of fruit by 54% (2.5–97.5th percentile: 44–61%), increase consumption by 21% (2.5–97.5th percentile: 32–11%), and decrease item utilization by 6.9% (2.5–97.5th percentile: 1.5–13%), compared to the baseline traditional cafeteria scenario. This study suggests that share tables have potential to safely reduce food waste. While share tables are predicted to slightly increase the illness prevalence, that risk is manageable by applying mitigation strategies.

In this paper, for the first time, empirical formulas have been reported of the Delta and Omicron strains of SARS-CoV-2. The empirical formula of the Delta strain entire virion was found to be CH_1.6383O_0.2844N_0.2294P_0.0064S_0.0042, while its nucleocapsid has the formula CH_1.5692O_0.3431N_0.3106P_0.0060S_0.0043. The empirical formula of the Omicron strain entire virion was found to be CH_1.6404O_0.2842N_0.2299P_0.0064S_0.0038, while its nucleocapsid has the formula CH_1.5734O_0.3442N_0.3122P_0.0060S_0.0033. Based on the empirical formulas, standard thermodynamic properties of formation and growth have been calculated and reported for the Delta and Omicron strains. Moreover, standard thermodynamic properties of binding have been reported for Wild type (Hu-1), Alpha, Beta, Gamma, Delta and Omicron strains. For all the strains, binding phenomenological coefficients and antigen-receptor (SGP-ACE2) binding rates have been determined and compared, which are proportional to infectivity. The results show that the binding rate of the Omicron strain is between 1.5 and 2.5 times greater than that of the Delta strain. The Omicron strain is characterized by a greater infectivity, based on the epidemiological data available in the literature. The increased infectivity was explained in this paper using Gibbs energy of binding. However, no indications exist for decreased pathogenicity of the Omicron strain. Pathogenicity is proportional to the virus multiplication rate, while Gibbs energies of multiplication are very similar for the Delta and Omicron strains. Thus, multiplication rate and pathogenicity are similar for the Delta and Omicron strains. The lower number of severe cases caused by the Omicron strain can be explained by increased number of immunized people. Immunization does not influence the possibility of occurrence of infection, but influences the rate of immune response, which is much more efficient in immunized people. This leads to prevention of more severe Omicron infection cases.

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.