Microbial Risk Analysis最新文献_第2页

One-step kinetic analysis of Listeria innocua growth as a surrogate for Listeria monocytogenes on arugula leaves with background microbiota: model development and validation 具有背景菌群的芝麻菜叶片上无痕李斯特菌替代单核增生李斯特菌生长的一步动力学分析：模型建立与验证

IF 4 4区环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES

Microbial Risk Analysis

Pub Date : 2025-08-23 DOI: 10.1016/j.mran.2025.100353

Samet Ozturk

This study investigated the growth kinetics of L. monocytogenes and L. innocua on fresh arugula leaves under abusive conditions, considering the presence of background microbiota (BM) including (APC (Aerobic Plate Count), TPC (Total Psychrotrophic Count), and LAB (Lactic Acid Bacteria)). Additionally, the feasibility of using L. innocua as a surrogate for L. monocytogenes was evaluated for industrial practices. Predictive models were developed to assess the effect of temperature and time on the growth kinetics of both microorganisms. Experiments were conducted in triplicate to observe growth kinetics at temperatures from 5 to 35°C. The growth curves were analyzed using one-step analysis with the USDA IPMP-Global Fit software, employing the Huang full/no-lag phase growth as the primary models and the Huang sub-optimal (HSRM) and Ratkowsky sub-optimal square-root as the secondary models. An additional set of isothermal data, collected at 15°C and 20°C, was used to validate the models. Results showed that the minimum growth temperatures were 2.91±0.50°C for L. monocytogenes and 2.88±0.50°C for L. innocua, while 2.05±0.89, 1.93±0.98 and 3.55±1.97°C for APC, TPC and LAB, respectively. The specific growth rates of L. monocytogenes and L. innocua ranged from 0.01 to 0.93 h⁻¹. The root mean square error (RMSE) of model validation and development was less than 0.3 log CFU/g, indicating that the combination of the Huang growth model with HSRM could accurately predict the growth of L. monocytogenes under abusive conditions. Validated models can provide useful input to quantitative risk assessment tools to predict the growth of L. monocytogenes on arugula leaves in the presence of BM during distribution or storage. The findings of this study support the use of L. innocua with R²=0.961 as a suitable surrogate in industrial practices for fresh produce.

考虑到背景微生物群（BM）的存在，包括APC（好氧平板计数）、TPC（总嗜冷菌计数）和LAB（乳酸菌），本研究研究了在滥用条件下新鲜芝麻菜叶片上单核增生乳杆菌和innocua乳杆菌的生长动力学。此外，还在工业实践中评价了用无头乳杆菌代替单核增生乳杆菌的可行性。建立了预测模型来评估温度和时间对两种微生物生长动力学的影响。实验一式三次，观察5 ~ 35℃温度下的生长动力学。采用USDA IPMP-Global Fit软件对生长曲线进行一步分析，采用Huang全/无滞后期生长模型为一级模型，Huang次优（HSRM）和Ratkowsky次优平方根模型为二级模型。另外一组在15°C和20°C时收集的等温数据用于验证模型。结果表明，单核增生L.的最低生长温度为2.91±0.50°C，无尾L.的最低生长温度为2.88±0.50°C， APC、TPC和LAB的最低生长温度分别为2.05±0.89、1.93±0.98和3.55±1.97°C。单核增生李斯特菌和无毒李斯特菌的特定生长率为0.01 ~ 0.93 h⁻¹。模型验证和开发的均方根误差（RMSE）均小于0.3 log CFU/g，表明黄氏生长模型与HSRM相结合可以准确预测滥用条件下单核增生乳杆菌的生长。经过验证的模型可以为定量风险评估工具提供有用的输入，以预测在BM存在的情况下芝麻菜叶片上单核细胞增生乳杆菌的生长。本研究结果支持无头乳杆菌作为生鲜产品工业生产中适宜的替代菌，R2=0.961。

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引用次数: 0

Independent and combined effects of exposure to temperature and humidity on social contact in China 暴露于温度和湿度对中国社会接触的独立和联合影响

IF 4 4区环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES

Microbial Risk Analysis

Pub Date : 2025-08-23 DOI: 10.1016/j.mran.2025.100354

Guanlin Ou , Jianxiong Hu , Bing Zhang , Guanhao He , Mengen Guo , Keqing Liang , Sujuan Chen , Fengrui Jing , Tao Liu , Guanghu Zhu , Wenjun Ma

Background

Seasonal variations in social contact (SC) have been documented in prior epidemiological investigations, yet the exposure-response relationships between key meteorological factors and SC remain insufficiently characterized.

Objective

The study aimed to analyze the independent and combined effects of temperature and relative humidity on SC.

Methods

Contact datasets (2015-2018) from six Chinese metropolitan regions (Shanghai, Zhuhai, Guangzhou, Shenzhen, Hong Kong, and Foshan) were analyzed alongside meteorological records. Non-linear associations of temperature and humidity with contact numbers were assessed using generalized additive models. Combined effects were subsequently evaluated through quantile g-computation models, followed by random forest analyses to determine importance.

Results

The independent associations of temperature or relative humidity with the numbers of SC were U-shaped, with 12.0°C and 66% as the thresholds, respectively. The number of total contacts decreased by 0.19 (95% CI: -0.25, -0.13) for each 1°C increase below the threshold (12.0°C), which was higher than that (0.13, 95% CI: 0.11, 0.17) above the threshold (12.0°C). It increased by 0.74 (95% CI: 0.60, 0.88) for each 10% increase of relative humidity during high humidity (≥66%), higher than that (-0.46, 95% CI: -0.62, -0.30) during low humidity (<66%). For combined exposure, there was a J-shaped association of mixture exposure to temperature and relative humidity with social contact, which had similar contribution.

Conclusions

Both temperature and relative humidity were independently and synergistically associated with SC, which indicates the seasonality of some infectious diseases may be partly explained by the seasonal change of SC mediated by temperature and relative humidity.

社会接触（SC）的季节变化已在先前的流行病学调查中得到记录，但关键气象因素与SC之间的暴露-反应关系仍未充分表征。目的分析温度和相对湿度对sc的独立和联合影响。方法利用2015-2018年中国6个大都市（上海、珠海、广州、深圳、香港和佛山）的接触数据集和气象记录进行分析。使用广义加性模型评估了温度和湿度与接触号码的非线性关联。随后通过分位数g计算模型评估联合效应，然后进行随机森林分析以确定重要性。结果温度和相对湿度与SC数量呈u型关系，分别以12.0℃和66%为阈值。在阈值（12.0°C）以下，每升高1°C，总接触数减少0.19 (95% CI: -0.25, -0.13)，高于阈值（12.0°C）（0.13, 95% CI: 0.11, 0.17）。在高湿（≥66%）条件下，相对湿度每增加10%，其增加幅度为0.74 (95% CI: 0.60, 0.88)，高于低湿（<66%）条件下（-0.46,95% CI: -0.62, -0.30）。对于混合暴露，温度和相对湿度混合暴露与社会接触呈j型相关，两者的贡献相似。结论温度和相对湿度与SC具有独立和协同的关系，表明温度和相对湿度介导的SC的季节性变化可能部分解释了某些传染病的季节性。

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引用次数: 0

Efficacy of machine learning models for the prediction of death occurrence and counts associated with foodborne illnesses and hospitalizations in the United States 机器学习模型在美国预测与食源性疾病和住院相关的死亡发生率和计数的有效性

IF 4 4区环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES

Microbial Risk Analysis

Pub Date : 2025-08-05 DOI: 10.1016/j.mran.2025.100351

Mohammed Rashad Baker , Selim Buyrukoğlu , Gonca Buyrukoğlu , Juan Moreira , Zeynal Topalcengiz

Foodborne outbreak data released through national surveillance systems provides essential information about the results of investigations. This study evaluates the efficacy of machine learning (ML) models for the prediction of death occurrence and counts associated with foodborne illnesses and hospitalizations in the United States. Confirmed foodborne outbreaks were obtained from the Centers for Disease Control and Prevention's National Outbreak Reporting System (NORS). Foodborne pathogens causing at least 10 deaths in total were selected for analysis. The binary classification performance (accuracy, %) and prediction efficacy of ML models (mean absolute errors, MAE) were used for evaluation. A total of 10,069 foodborne outbreaks with confirmed single etiology resulted in 275,827 illnesses, 18,579 hospitalizations, and 458 deaths. Salmonella was the leading causative agent (54.23 %) of bacterial foodborne outbreaks, followed by pathogenic Escherichia coli (12.13 %). Norovirus (96.69 %) and Cyclospora cayetanensis (60.76 %) represented major causes of viral and protozoan/parasite foodborne outbreaks, respectively. The classification performance of ML models ranged from 88.9 to 94.5 % for the overall prediction of death occurrence associated with foodborne illnesses and hospitalizations. Prediction efficacy of ML models for death counts remained <0.9 with MAE, except for Listeria monocytogenes with an average MAE of 134.1 ± 11.1. This study indicates the potential use and performance of ML algorithms for the prediction of death occurrence or counts caused by foodborne etiological agents to improve public health safety based on the numbers of illnesses and hospitalizations.

通过国家监测系统发布的食源性暴发数据提供了有关调查结果的基本信息。本研究评估了机器学习（ML）模型在预测美国与食源性疾病和住院相关的死亡发生率和计数方面的功效。确认的食源性暴发是从疾病控制和预防中心的国家暴发报告系统（NORS）获得的。选择了总共至少造成10人死亡的食源性病原体进行分析。采用二元分类性能（准确率，%）和ML模型的预测效果（平均绝对误差，MAE）进行评价。经确认的单一病因食源性暴发共发生10069起，导致275827人患病，18579人住院，458人死亡。沙门氏菌是食源性细菌暴发的主要病原菌（54.23%），其次是致病性大肠杆菌（12.13%）。诺如病毒（96.69%）和卡耶坦环孢子虫（60.76%）分别是病毒和原虫/寄生虫食源性暴发的主要原因。ML模型在预测与食源性疾病和住院相关的死亡发生率方面的分类性能从88.9%到94.5%不等。ML模型对死亡计数的MAE预测效率为<；0.9，但单核增生李斯特菌的平均MAE为134.1±11.1。本研究表明ML算法的潜在用途和性能，用于预测食源性病原体引起的死亡发生或计数，以改善基于疾病和住院人数的公共卫生安全。

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引用次数: 0

Improving a microbial risk assessment tool with direct feedback from school health staff 根据学校卫生人员的直接反馈，改进微生物风险评估工具

IF 4 4区环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES

Microbial Risk Analysis

Pub Date : 2025-08-05 DOI: 10.1016/j.mran.2025.100352

Mehedi Hasan , Megan Peterson , Emily K. Waldron , Nathan L. Mottern , Nicole T. Pargas , Lynn B. Gerald , Ashley A. Lowe , Amanda M. Wilson

Due to the impact of COVID-19, publicly available risk-based tools are becoming increasingly popular. However, subject experts develop most of these tools without consulting end users. Thus, this study aimed to explore users' perceptions, vision, and guidance for microbial risk assessment tool development through focus groups. This tool was intended to assist school health staff in decision-making regarding school respiratory viral outbreaks. Partnering with a school district in the Tucson metropolitan area, we conducted three focus groups with school health staff to gather feedback on a risk tool prototype. We discussed the staff’s vision for the tool, their feedback on tool capabilities and design, and how they could leverage tool output for informing decisions, advocating with administration, or educating parents, students, or staff. Focus groups were conducted at the district health office, and the transcripts were analyzed by two researchers using inductively informed themes. Thematic analysis revealed that a comprehensive microbial risk assessment tool must have the potential to manage large amounts of data, scope for incorporation of existing data management systems, have real-time data processing, and produce context-specific recommendations for advocacy. Risk tools can expand personalized risk assessment and management strategies. Directly engaging users will advance microbial risk assessment impact and implementation. In the context of schools, a collaborative, comprehensive, digital and real time microbial risk assessment tool is a timely demand by the school health staff to manage microbial risks.

由于COVID-19的影响，公开的基于风险的工具越来越受欢迎。然而，学科专家在开发这些工具时没有咨询最终用户。因此，本研究旨在通过焦点小组探讨用户对微生物风险评估工具开发的看法、愿景和指导。该工具旨在协助学校卫生工作人员就学校呼吸道病毒暴发作出决策。我们与图森市区的一个学区合作，与学校卫生工作人员进行了三个焦点小组讨论，以收集对风险工具原型的反馈。我们讨论了员工对工具的愿景，他们对工具功能和设计的反馈，以及他们如何利用工具输出来通知决策，与管理部门一起倡导，或者教育家长、学生或员工。在地区卫生办公室进行了焦点小组研究，两名研究人员使用归纳性信息主题对记录进行了分析。专题分析表明，综合微生物风险评估工具必须具有管理大量数据的潜力、纳入现有数据管理系统的范围、具有实时数据处理能力，并针对具体情况提出宣传建议。风险工具可以扩展个性化的风险评估和管理策略。直接参与用户将促进微生物风险评估的影响和实施。在学校环境下，协同、全面、数字化、实时的微生物风险评估工具是学校卫生工作人员对微生物风险管理的及时需求。

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引用次数: 0

The prevalence and concentrations of four waste-pathogen combinations from land-spreading across high-income, temperate regions – A scoping review and pooled analysis 在高收入、温带地区土地传播的四种废物-病原体组合的流行率和浓度——范围审查和汇总分析

IF 4 4区环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES

Microbial Risk Analysis

Pub Date : 2025-07-26 DOI: 10.1016/j.mran.2025.100350

Jennifer E.M. McCarthy , Paul D Hynds , Declan J Bolton , Jesús M Frías Celayeta

Animal slurries and wastewater treatment sludges (WWTS) represent valuable biofertilisers in high-income, temperate regions and support transformative agri-food systems as sustainable, agricultural waste management practice. However, the presence of enteric pathogens in land-spread biowastes pose a public health risk, with food and water being critical transmission pathways. A dearth of spatiotemporally representative pathogen prevalence and concentration data from high-income, temperate regions exists to estimate the risk, achievable through quantitative microbial risk assessment (QMRA). A spatiotemporally explicit scoping review was undertaken of four waste-pathogen combinations (W-PCs) (i.e., bovine slurry-STEC serogroups O157/O26, bovine slurry-Cryptosporidium parvum, broiler litter-Campylobacter jejuni, and WWTS-norovirus genogroups GI/GII) from land-spreading in high-income, temperate regions. W-PC prevalence and concentration data from 46 farm-level studies were extracted, harmonised, and pooled, to obtain representative data for meta-analyses, distribution fitting, and QMRA from land-spreading across these regions in addition to providing individual study prevalence and concentrations. Pooled mean prevalence and the total number of biowaste samples across extracted studies for each W-PC ranged from 17 % for STEC O157/O26 (N = 14,204) to 48 % for norovirus GI/GII (N = 1027). These general estimates included specific and non-specific data (i.e., serogroups, species and subspecies, or genogroups), and thus, should be interpreted with a level of caution. Pooled mean and SD concentrations ranged from norovirus GI/GII 1.3, 0.5 log₁₀ gc ml^-1 to C. jejuni 5.1, 0.7 log₁₀ CFU g^-1. Spatiotemporal heterogeneity, unstandardised reporting, and study design biases were found across studies. Therefore, increased standardised data and reporting in primary studies are required for more accurate QMRA estimates. Furthermore, pooling heterogeneous secondary data as though they were homogeneous introduces general error, and hence, highlights the requirement for future meta-analyses and distribution fitting of these data to characterise the inter- and intra- study variability in addition to uncertainty and variability from environmental sources.

在高收入、温带地区，动物浆和废水处理污泥是有价值的生物肥料，并作为可持续的农业废物管理实践支持转型农业粮食系统。然而，在陆地传播的生物废物中存在的肠道病原体构成了公共卫生风险，食物和水是关键的传播途径。通过定量微生物风险评估（QMRA），缺乏高收入、温带地区具有时空代表性的病原体流行率和浓度数据来估计风险。对高收入温带地区陆地传播的四种废物-病原体组合（即牛浆液-产大肠杆菌血清群O157/O26、牛浆液-小隐孢子虫、肉鸡粪便-空肠弯曲杆菌和wwts -诺如病毒基因群GI/GII）进行了时空明确的范围审查。从46个农场水平的研究中提取、协调和汇总W-PC患病率和浓度数据，除了提供单个研究患病率和浓度外，还获得了这些地区土地分布的meta分析、分布拟合和QMRA的代表性数据。在提取的研究中，每种W-PC的汇总平均患病率和生物废物样本总数从STEC O157/O26的17% （N = 14,204）到诺如病毒GI/GII的48% （N = 1027）不等。这些一般估计包括特异性和非特异性数据（即血清群、种和亚种或基因群），因此应谨慎解释。混合平均浓度和SD浓度从诺如病毒GI/GII 1.3、0.5 log10 gc ml-1到空肠C. 5.1、0.7 log10 CFU g-1。在研究中发现了时空异质性、非标准化报告和研究设计偏差。因此，需要在初级研究中增加标准化数据和报告，以获得更准确的QMRA估计。此外，汇集异质的二手数据，就好像它们是同质的一样，会引入一般误差，因此，强调了对这些数据的未来荟萃分析和分布拟合的需求，以表征研究间和研究内的可变性，以及来自环境来源的不确定性和可变性。

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引用次数: 0

Prevalence and concentrations of four waste-pathogen combinations from land-spreading across high-income, temperate regions: Meta-modelling and distribution fitting for quantitative microbial risk assessment (QMRA) 四种来自高收入温带地区土地传播的废物病原体组合的流行率和浓度：定量微生物风险评估（QMRA）的元模型和分布拟合

IF 3 4区环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES

Microbial Risk Analysis

Pub Date : 2025-06-22 DOI: 10.1016/j.mran.2025.100348

Jennifer E M McCarthy , Paul D Hynds , Declan J Bolton , Jesús M Frías Celayeta

Land-spread organic wastes provide sustainable waste management across high-income, temperate regions. However, enteric pathogens in these animal manures and wastewater treatment sludges (WWTS) may pose food- and waterborne public health risks. Furthermore, these risks might increase due to climate change, with the likelihood of increasing temperature and precipitation across temperate latitudes. Quantitative microbial risk assessment (QMRA) is an established approach to estimate the potential risks, with a sparsity of spatiotemporally distributed waste-pathogen combination (W-PC) prevalence and concentrations from land-spreading existing in the literature for QMRA. Additionally, a knowledge gap exists regarding the availability of meta-models to predict pathogen prevalence based on spatially specific climatic or agricultural parameters. Accordingly, spatiotemporally representative data across high-income, temperate regions were extracted from 46 published studies based on a scoping review of four W-PC (i.e., bovine slurry-STEC serogroups O157/O26, bovine slurry-Cryptosporidium parvum, broiler litter-Campylobacter jejuni, and WWTS-norovirus genogroups GI/GII) prevalence and concentrations from land-spreading. Meta-analyses and distribution fitting of these data characterised variability and uncertainty, with generalised linear mixed effects models employed to develop prevalence meta-models in addition to generalised additive models for location, shape, and scale fitted to concentrations. Mean pathogen prevalence ranged from STEC O157/O26 7 % OR 1.07 p = 0.05 to C. jejuni 39 % OR 1.48 p < 0.0001, with bioclimatic indicators, namely temperature and precipitation seasonality, significant across all meta-models. The best fit was a 2-parameter reverse Gumbel for norovirus GI/GII log₁₀ gc ml^-1 concentration (µ = 0.33, p = 0.55; σ = 0.66, p = 0.004; GAIC = 69.21). While meta-analyses and distribution fitting accounted for uncertainty and variability associated with modelled data, more standardised secondary data are required from primary research to provide more accurate QMRA estimates for ensuring microbiological safety in primary agricultural production.

陆地上散布的有机废物为高收入温带地区提供了可持续的废物管理。然而，这些动物粪便和废水处理污泥（WWTS）中的肠道病原体可能构成食物和水传播的公共卫生风险。此外，由于气候变化，这些风险可能会增加，温带地区的温度和降水可能会增加。定量微生物风险评估（QMRA）是一种评估潜在风险的成熟方法，但文献中存在时空分布的废物-病原体组合（W-PC）流行率和浓度的稀疏性。此外，在基于空间特定气候或农业参数预测病原体流行的元模型的可用性方面存在知识缺口。因此，基于对四种W-PC（即牛浆液-产大肠杆菌血清群O157/O26、牛浆液-小隐孢子虫、肉鸡窝-空肠弯曲杆菌和wwts -诺如病毒基因群GI/GII）在陆地传播的流行和浓度的范围审查，从高收入、温带地区的46项已发表研究中提取了具有时空代表性的数据。这些数据的荟萃分析和分布拟合具有可变性和不确定性的特征，除了用于拟合浓度的位置、形状和规模的广义相加模型外，还使用广义线性混合效应模型来开发患病率元模型。平均致病菌患病率从STEC O157/O26 7% OR 1.07 p = 0.05到空肠C. jejuni 39% OR 1.48 p <；0.0001，生物气候指标，即温度和降水季节性，在所有元模型中都显著。诺如病毒GI/GII log10 gc ml-1浓度的最佳拟合为2参数反向Gumbel(µ= 0.33,p = 0.55；σ = 0.66, p = 0.004；gac = 69.21)。虽然荟萃分析和分布拟合解释了与建模数据相关的不确定性和可变性，但需要从初级研究中获得更多标准化的次级数据，以提供更准确的QMRA估计，以确保初级农业生产中的微生物安全。

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引用次数: 0

A comparison of machine learning models for predicting Vibrio parahaemolyticus in oysters 预测牡蛎中副溶血性弧菌的机器学习模型比较

IF 3 4区环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES

Microbial Risk Analysis

Pub Date : 2025-06-09 DOI: 10.1016/j.mran.2025.100345

Nodali Ndraha , Hsin-I Hsiao

Vibrio parahaemolyticus, a major seafood pathogen, threatens public health as oyster consumption rises. We evaluated 14 machine learning models to predict its concentrations in oysters, achieving high accuracy (Concordance Correlation Coefficient, CCC > 0.85 training, > 0.9 testing, except bag-MARS) across diverse algorithms. Processing times varied from 23 min (KNN) to 162 min (bag-RPart), highlighting computational trade-offs. Five top models—Elastic Net (EN), Random Forest (RF), XGBoost, Light Gradient-Boosting Machine (L-GBM), and Cubist (39–92 min)—were selected for their performance and efficiency, forming a robust toolkit for shellfish safety monitoring. Variable importance and partial dependence plots identified sea surface temperature (SST) and wind as primary drivers, with SST thresholds of 16–26 °C driving proliferation and wind showing mixed effects (negative >4 m/s, positive >6 m/s). Precipitation, salinity (>19 ppm), and pH (7.5–7.7) played supplementary roles. Lagged variables (e.g., SST_imX_25) underscored temporal dynamics, supporting real-time monitoring and risk assessment strategies.

副溶血性弧菌是一种主要的海产品病原体，随着牡蛎消费量的增加，它威胁着公众的健康。我们评估了14个机器学习模型来预测其在牡蛎中的浓度，取得了很高的准确性(一致性相关系数，CCC >；0.85培训，>；0.9测试，除了袋子-火星)跨不同的算法。处理时间从23分钟（KNN）到162分钟（bag-RPart）不等，突出了计算权衡。五个顶级模型-弹性网（EN），随机森林（RF）， XGBoost，光梯度增强机（L-GBM）和立体派（39-92分钟）-被选择为它们的性能和效率，形成了一个强大的贝类安全监测工具包。变量重要性图和部分依赖性图显示，海温和风是主要驱动因素，海温阈值为16-26℃驱动扩散，风的影响混合（负4 m/s，正6 m/s）。降水、盐度（19 ppm）和pH（7.5 ~ 7.7）起辅助作用。滞后变量（例如，SST_imX_25）强调时间动态，支持实时监测和风险评估战略。

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引用次数: 0

Human close contact behavior based respiratory diseases transmission in a university office building 基于呼吸系统疾病传播的大学办公大楼人类密切接触行为

IF 3 4区环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES

Microbial Risk Analysis

Pub Date : 2025-04-25 DOI: 10.1016/j.mran.2025.100344

Nan Zhang, Palmira Elisa Nhantumbo, Haochen Zhang

Understanding how respiratory infectious diseases spreads is critical for effective pandemic prevention and control. This study investigated the transmission of aerosol-transmissible respiratory pathogens within an office building for postgraduate students and teachers in Beijing, using SARS-CoV-2 as representative model, focusing on real-time occupancy and close-contact behaviors. Surveillance videos and RGB-D cameras were used to collect data, and a multi–route virus transmission model was established to assess the infection risk and evaluate the effectiveness of non–pharmaceutical interventions. Student offices experienced the longest room usage time (13.2 ± 0.4 h) but a lower room occupancy intensity rate (27.1 ± 7 %) during weekdays. Close contact rate in students and teacher offices ranged from 10 to 11 %, while the conference room displayed the highest rates of 93–96 %. Teacher offices had the lowest average interpersonal distance during close contact (0.73 m), followed by teachers' conference (0.85 m). If a single infected individual were set in the building, people in the student office would face the highest hourly infection risk at 0.12 %. The use of surgical masks and increasing indoor ventilation from 0.5 to 6 air changes per hour reduces the total infection risk by 66.4–76.0 % and 45.0–65.0 %, respectively. Maintaining a distance of 1.5 m when in contact can further lower the total infection risk to 52.8–51.9 %. The findings of this study provide valuable insights for understanding the transmission dynamics of a respiratory infectious disease within the building, essential knowledge for effective prevention and control strategies.

了解呼吸道传染病如何传播对于有效预防和控制大流行至关重要。本研究以SARS-CoV-2为代表模型，对北京市某研究生和教师办公大楼内气溶胶传播呼吸道病原体的传播情况进行了调查，重点关注实时占用和近距离接触行为。采用监控视频和RGB-D摄像机采集数据，建立病毒多途径传播模型，评估感染风险，评价非药物干预措施的有效性。学生办公室工作日的用房时间最长（13.2±0.4 h），但用房强度率较低（27.1±7%）。学生办公室和教师办公室的密切接触率为10% - 11%，而会议室的密切接触率最高，为93% - 96%。教师办公室密切接触时的平均人际距离最低（0.73 m），其次是教师会议（0.85 m），如果在教学楼内设置单个感染者，学生办公室人员的小时感染风险最高，为0.12%。使用外科口罩和将室内换气次数从每小时0.5次增加到6次，可使总感染风险分别降低66.4 - 76.0%和45.0 - 65.0%。接触时保持1.5米的距离可进一步将总感染风险降低至52.8 - 51.9%。本研究结果为了解呼吸道传染病在建筑物内的传播动态提供了有价值的见解，为有效的预防和控制策略提供了必要的知识。

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引用次数: 0

Quantitative microbiological risk assessment using individual data on food storage and consumption (Part 1): A case study on listeriosis associated to ready-to-eat foods in France 利用食品储存和消费的个人数据进行定量微生物风险评估（第1部分）：法国与即食食品相关的李斯特菌病案例研究

IF 3 4区环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES

Microbial Risk Analysis

Pub Date : 2025-03-04 DOI: 10.1016/j.mran.2025.100343

Hernán G. Redondo , Laurent Guillier , Virginie Desvignes , Matthias Filter , Sara M. Pires , Maarten Nauta

The risk of acquiring foodborne infections such as listeriosis is influenced by individuals’ food consumption and food storage practices. So far, quantitative microbiological risk assessment (QMRA) studies have mostly treated the related variables as independent, creating potentially unrealistic high-risk conditions, such as combinations of big portion size, high frequency of consumption, high storage temperature and long storage time. Therefore, in this study, we integrated individual food consumption and food storage data collected by the French national dietary survey INCA3 with food contamination data to estimate the risk of listeriosis in France, without assuming independence of variables. The aim was to assess and compare risks for different population groups and for different food groups, to identify risk factors and characterize high-risk groups. We adapted a QMRA model previously developed for the assessment of the number of listeriosis cases associated to ready-to-eat (RTE) foods in the EU. We modified the exposure assessment to use an individual-based approach in which consumer-specific consumption data and food safety practices are used to calculate exposure. Results showed that high-risk individuals stored their food in their refrigerator for longer and at higher temperatures prior to consumption than low-risk individuals. Smoked fish and pâte were estimated to be responsible for 66 % of the likely 393 annual cases for France. Improved characterization of high-risk individuals and their determinants for risk may contribute to better targeted food safety guidance. We demonstrated that considering individual-based data in QMRA opens the way for the establishment of risk-based measures that are specific for distinct individuals within the population.The advantage of this “individual-based” QMRA approach is that the observed variation between individual consumers in the four variables “frequency of consumption”, “portion size”, “storage time” and “storage temperature” is taken into account, and that it includes their interdependency for each individual consumer.

获得李斯特菌病等食源性感染的风险受到个人食物消费和食物储存做法的影响。到目前为止，定量微生物风险评估（QMRA）研究大多将相关变量视为独立的，造成了潜在的不切实际的高风险条件，如大份量、高食用频率、高储存温度和长储存时间的组合。因此，在本研究中，我们将法国国家膳食调查INCA3收集的个人食品消费和食品储存数据与食品污染数据相结合，在不假设变量独立性的情况下估计法国李斯特菌病的风险。目的是评估和比较不同人群和不同食物组的风险，确定风险因素并确定高风险人群的特征。我们采用了先前开发的QMRA模型，用于评估欧盟与即食食品（RTE）相关的李斯特菌病病例数量。我们修改了暴露评估，使用基于个人的方法，其中使用消费者特定的消费数据和食品安全实践来计算暴露。结果表明，与低风险人群相比，高风险人群在食用前将食物放在冰箱里的时间更长，温度更高。据估计，在法国每年可能发生的393例病例中，熏鱼和鱼肉占66%。改进高风险个体及其风险决定因素的特征可能有助于更好地进行有针对性的食品安全指导。我们证明，在QMRA中考虑基于个人的数据，为建立针对人群中不同个体的基于风险的措施开辟了道路。这种“以个人为基础”的QMRA方法的优点是，在四个变量“消费频率”、“分量大小”、“储存时间”和“储存温度”中观察到的个体消费者之间的差异被考虑在内，并且它包括每个个体消费者的相互依赖性。

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引用次数: 0

Pathogens-in-Foods (PIF): An open-access European database of occurrence data of biological hazards in foods 食品中的病原体（PIF）：一个开放获取的欧洲食品中生物危害发生数据数据库

IF 3 4区环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES

Microbial Risk Analysis

Pub Date : 2025-02-27 DOI: 10.1016/j.mran.2025.100342

Ursula Gonzales-Barron , Ana Sofia Faria , Anne Thebault , Laurent Guillier , Lucas Ribeiro Mendes , Lucas Ribeiro Silva , Winy Messens , Pauline Kooh , Vasco Cadavez

The collection of occurrence data of foodborne pathogens in foods faces the hindrances of dispersion of information, lack of standardisation and harmonisation, and ultimately, high expenditure in time and resources. The Pathogens-in-Foods (PIF) database was conceived as a solution to centralise published data on prevalence and concentration of pathogenic bacteria, viruses and parasites occurring in foods, obtained through systematic review (SR), and categorised in harmonised data structures under controlled terminologies. The present article outlines how PIF was constructed to adhere to the FAIR (findability, accessibility, interoperability and reusability) principles for scientific data management; and proceeds with a description of the PIF concept, which entails two phases: the SR process and the population of PIF. The protocolled SR process is supported by a well-defined search strategy, inclusion criteria, and rules for internal validation assessment; whereas the population of PIF with new data relies in data extraction, validation and release. The article then introduces a novel data quality approach, named as the CCC approach (data consistency, conformity and completeness), which ensures proper interpretation of data, richness of data, and flawless transcription of data. After a brief explanation of the three PIF components – database, back-end and front-end – the article proceeds with the exposition of the data model, as well as the capabilities of the front-end, including data search, insertion and curation. The future of PIF lies in expanding its capabilities, addressing emerging challenges, and leveraging technological advancements to maintain its relevance and utility in the evolving landscape of food safety.

食品中食源性病原体发生数据的收集面临着信息分散、缺乏标准化和协调以及最终花费大量时间和资源的障碍。食品中病原体（PIF）数据库被设想为一种解决方案，用于集中通过系统审查（SR）获得的关于食品中致病菌、病毒和寄生虫的流行率和浓度的已发表数据，并在受控术语下按统一数据结构进行分类。本文概述了如何构建PIF以坚持科学数据管理的FAIR（可查找性，可访问性，互操作性和可重用性）原则；并开始描述PIF概念，其中包括两个阶段：SR过程和PIF的人口。协议化的SR过程由定义良好的搜索策略、包含标准和内部验证评估规则支持；而具有新数据的PIF的填充依赖于数据的提取、验证和发布。然后，本文介绍了一种新的数据质量方法，称为CCC方法（数据一致性、一致性和完整性），它确保了对数据的正确解释、数据的丰富性和数据的完美转录。在简要介绍了三个PIF组件（数据库、后端和前端）之后，本文继续阐述数据模型，以及前端的功能，包括数据搜索、插入和管理。PIF的未来在于扩大其能力，应对新出现的挑战，并利用技术进步来保持其在不断变化的食品安全领域的相关性和实用性。

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引用次数: 0