Microbial Risk Analysis最新文献

Objectives

Variants of a coronavirus (SARS-CoV-2) have been spreading in a global pandemic. Improved understanding of the infectivity of future new variants is important so that effective countermeasures against them can be quickly undertaken. In our research reported here, we aimed to predict the infectivity of SARS-CoV-2 by using a mathematical model with molecular simulation analysis, and we used phylogenetic analysis to determine the evolutionary distance of the spike protein gene (S gene) of SARS-CoV-2.

Methods

We subjected the six variants and the wild type of spike protein and human angiotensin-converting enzyme 2 (ACE2) to molecular docking simulation analyses to understand the binding affinity of spike protein and ACE2. We then utilized regression analysis of the correlation coefficient of the mathematical model and the infectivity of SARS-CoV-2 to predict infectivity.

Results

The evolutionary distance of the S gene correlated with the infectivity of SARS-CoV-2 variants. The calculated biding affinity for the mathematical model obtained with results of molecular docking simulation also correlated with the infectivity of SARS-CoV-2 variants. These results suggest that the data from the docking simulation for the receptor binding domain of variant spike proteins and human ACE2 were valuable for prediction of SARS-CoV-2 infectivity.

Conclusion

We developed a mathematical model for prediction of SARS-CoV-2 variant infectivity by using binding affinity obtained via molecular docking and the evolutionary distance of the S gene.

Ebola virus is among the most dangerous, contagious and deadly etiological causes of viral diseases. However, Ebola virus has never extensively spread in human population and never have led to a pandemic. Why? The mechanistic biophysical model revealing the biothermodynamic background of virus-host interaction) could help us to understand pathogenesis of Ebola virus disease (earlier known as the Ebola hemorrhagic fever). In this paper for the first time the empirical formula, thermodynamic properties of biosynthesis (including the driving force of virus multiplication in the susceptible host), binding constant and thermodynamic properties of binding are reported. Thermodynamic data for Ebola virus were compared with data for SARS-CoV-2 to explain why SARS-CoV-2 has caused a pandemic, while Ebola remains on local epidemic level. The empirical formula of the Ebola virus was found to be CH_1.569O_0.3281N_0.2786P_0.00173S_0.00258. Standard Gibbs energy of biosynthesis of the Ebola virus nucleocapsid is -151.59 kJ/C-mol.

From march 2020 to march 2022 covid-19 has shown a consistent pattern of increasing infections during the Winter and low infection numbers during the Summer. Understanding the effects of seasonal variation on covid-19 spread is crucial for future epidemic modelling and management. In this study, seasonal variation in the transmission rate of covid-19, was estimated based on an epidemic population model of covid-19 in Denmark, which included changes in national restrictions and introduction of the $\alpha$-variant covid-19 strain, in the period March 2020 - March 2021. Seasonal variation was implemented as a logistic temperature dependent scaling of the transmission rate, and parameters for the logistic relationship was estimated through rejection-based approximate bayesian computation (ABC). The likelihoods used in the ABC were based on national hospital admission data and seroprevalence data stratified into nine and two age groups, respectively. The seasonally induced reduction in the transmission rate of covid-19 in Denmark was estimated to be $27\%$, (95% CI [$24\%$; $31\%$]), when moving from peak Winter to peak Summer. The reducing effect of seasonality on transmission rate per $+1{}^{\circ }$C in daily average temperature were shown to vary based on temperature, and were estimated to be $-2.2\%[-2.8\%;-1.7\%]$ pr. 1 ${}^{\circ }$C around $2^{\circ }$C; $2\%[-2.3\%;-1.7\%]$ pr. 1 ${}^{\circ }$C around $7{}^{\circ }$C; and $1.7\%[-2.0\%;-1.5\%]$ pr. 1 ${}^{\circ }$C around a daily average temperature of 11 ${}^{\circ }$C.

Extended-spectrum-β-lactamase-producing Escherichia coli (ESBL-EC) is a major public health concern. A better understanding of the dynamics of ESBL-EC transmission is required for effective prevention and control. We present here a multidirectional dynamic risk model for ESBL-EC transmission between broiler flocks, broiler farmers, and the open community, parameterized for the Netherlands. A discrete-time model was used to describe the transmission of ESBL-EC within and between populations including modeling the flock-to-human transmission via food consumption due to contamination at the slaughterhouse and/or during food preparation. The ESBL-EC prevalence reached an equilibrium prevalence of 0.65%, 24.7%, and 15.9% in the open community, farmers, and broiler flocks, respectively. The colonization of the open community could primarily be attributed to the open community itself (62%), followed by vegetable consumption (29.5%), and contact with farmers (8.5%). Model results were most sensitive to the estimated colonization and decolonization rate for humans. What-if analysis to explore the effect of interventions in the food production chain (i.e. from farm to fork) on the ESBL-EC prevalence in the open community indicated that interventions aimed at reducing the spread of ESBL-EC within broiler flocks were most effective. Interventions in the consumer phase (reduced cross-contamination in the kitchen, and reduced chicken meat consumption) resulted in a slightly lower ESBL-EC prevalence in the open community. Reducing cross-contamination at the slaughterhouse or reducing the proportion of broiler flocks with high antimicrobial use hardly had any effect on the prevalence in the open community. These results illustrate the relevance of the model for supporting the development of antimicrobial resistance risk mitigation strategies as part of public health policy making.

This paper reports, for the first time, standard Gibbs energies of binding of the BA.1, BA.2, BA.3, BA.2.13, BA.2.12.1 and BA.4 Omicron variants of SARS-CoV-2, to the Human ACE2 receptor. Variants BA.1 through BA.3 exhibit a trend of decreasing standard Gibbs energy of binding and hence increased infectivity. The BA.4 variant exhibits a less negative standard Gibbs energy of binding, but also more efficient evasion of the immune response. Therefore, it was concluded that all the analyzed strains evolve in accordance with expectations of the theory of evolution, albeit using different strategies.

During the COVID-19 pandemic, many statistical and epidemiological studies have been published, trying to predict the future development of the SARS-CoV-2 pandemic. However, it would be beneficial to have a specific, mechanistic biophysical model, based on the driving forces of processes performed during virus-host interactions and fundamental laws of nature, allowing prediction of future evolution of SARS-CoV-2 and other viruses. In this paper, an attempt was made to predict the development of the pandemic, based on biothermodynamic parameters: Gibbs energy of binding and Gibbs energy of growth. Based on analysis of biothermodynamic parameters of various variants of SARS-CoV-2, SARS-CoV and MERS-CoV that appeared during evolution, an attempt was made to predict the future directions of evolution of SARS-CoV-2 and potential occurrence of new strains that could lead to new pandemic waves. Possible new mutations that could appear in the future could lead to changes in chemical composition, biothermodynamic properties (driving forces of new virus strains) and biological properties of SARS CoV-2 that represent a risk for humanity.

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.