Microbial Risk Analysis最新文献

{"title":"Quantitative data and models for bacterial cross-contamination in domestic kitchen during food handling and preparation","authors":"Dixuan Cai ,&nbsp;Jinhan He ,&nbsp;Runrun Zhang ,&nbsp;Xinyu Liao ,&nbsp;Juhee Ahn ,&nbsp;Jinsong Feng ,&nbsp;Tian Ding","doi":"10.1016/j.mran.2025.100356","DOIUrl":"10.1016/j.mran.2025.100356","url":null,"abstract":"<div><div>Cross-contamination is a significant factor contributing to outbreaks of foodborne diseases and food spoilage, and is an important component of quantitative microbial risk assessment (QMRA). The domestic environment represents the final stage of exposure assessment, and data underscore that the exposure risk of foodborne pathogens to consumers is closely linked to cross-contamination in household settings. However, transfer rate data and cross-contamination models from previous studies are fragmented and require integration and categorization for more effective utilization within the QMRA framework. This review summarizes the potential impacts of vehicles during bacterial transmission, transfer rate data for common routes, and current models in domestic kitchens, providing valuable support for cross-contamination modeling within the exposure assessment. In the future, the data gap in the household scenario should be further investigated, particularly in water- and glove-mediated processes. The models can be further improved and refined as deeper underlying mechanisms are uncovered, alongside consumer behavior investigations and the application of AI-powered methods.</div></div>","PeriodicalId":48593,"journal":{"name":"Microbial Risk Analysis","volume":"30 ","pages":"Article 100356"},"PeriodicalIF":4.0,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145264932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Literature-based explainable machine learning models for predicting pathogen and antibiotic resistance gene loads from animal manure","authors":"Ayşe Birsen Kadıoğlu Gökalp ,&nbsp;Handan Atalay Eroğlu ,&nbsp;Elif Nihan Kadıoğlu","doi":"10.1016/j.mran.2025.100355","DOIUrl":"10.1016/j.mran.2025.100355","url":null,"abstract":"<div><div>The use of animal manure (cattle, pigs, poultry, and sheep) in agriculture offers significant advantages such as increasing soil fertility and reducing the use of chemical fertilizers. However, this application also brings about serious environmental and public health problems due to the risk of microbial contaminants such as pathogenic microorganisms and antibiotic resistance genes (ARGs) spreading into the environment. In order to assess this dual risk, we developed a machine learning (ML) framework capable of simultaneously predicting pathogen load and ARG levels. The dataset contains 223 records systematically collected from 54 scientific studies published between 2015 and 2024. Six regression models were compared; Gradient Boosting algorithm (R² = 0.93) for pathogen load and Ridge Regression algorithm (R² = 0.84) for ARG level showed the highest accuracy performance. Model generalizability was tested with 5- and 10-fold cross-validation; low overfitting risk was confirmed by learning curves and residual analysis, specifically for the final selected models (Gradient Boosting for pathogen load and Ridge Regression for ARG level), while other models such as Decision Tree showed clear signs of overfitting and were therefore excluded from further analysis. The transparency of model decisions was examined with SHapley Additive exPlanations (SHAP) analyses; “application period”, “ARG type” and “fertilizer type” were highlighted as determining variables. In addition, Partial Dependence Plot (PDP) analyses revealed the marginal effects of environmental and operational factors on target variables in a biologically meaningful way. This integrated modelling approach contributes to the optimization of sustainable fertilization strategies and the development of environmental-health policies.</div></div>","PeriodicalId":48593,"journal":{"name":"Microbial Risk Analysis","volume":"30 ","pages":"Article 100355"},"PeriodicalIF":4.0,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"One-step kinetic analysis of Listeria innocua growth as a surrogate for Listeria monocytogenes on arugula leaves with background microbiota: model development and validation","authors":"Samet Ozturk","doi":"10.1016/j.mran.2025.100353","DOIUrl":"10.1016/j.mran.2025.100353","url":null,"abstract":"<div><div>This study investigated the growth kinetics of <em>L. monocytogenes</em> and <em>L. innocua</em> 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 <em>L. innocua</em> as a surrogate for <em>L. monocytogenes</em> 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 <em>L. monocytogenes</em> and 2.88±0.50°C for <em>L. innocua</em>, 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 <em>L. monocytogenes</em> and <em>L. innocua</em> 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 <em>L. monocytogenes</em> under abusive conditions. Validated models can provide useful input to quantitative risk assessment tools to predict the growth of <em>L. monocytogenes</em> on arugula leaves in the presence of BM during distribution or storage. The findings of this study support the use of <em>L. innocua</em> with R²=0.961 as a suitable surrogate in industrial practices for fresh produce.</div></div>","PeriodicalId":48593,"journal":{"name":"Microbial Risk Analysis","volume":"30 ","pages":"Article 100353"},"PeriodicalIF":4.0,"publicationDate":"2025-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144907254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Independent and combined effects of exposure to temperature and humidity on social contact in China","authors":"Guanlin Ou ,&nbsp;Jianxiong Hu ,&nbsp;Bing Zhang ,&nbsp;Guanhao He ,&nbsp;Mengen Guo ,&nbsp;Keqing Liang ,&nbsp;Sujuan Chen ,&nbsp;Fengrui Jing ,&nbsp;Tao Liu ,&nbsp;Guanghu Zhu ,&nbsp;Wenjun Ma","doi":"10.1016/j.mran.2025.100354","DOIUrl":"10.1016/j.mran.2025.100354","url":null,"abstract":"<div><h3>Background</h3><div>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.</div></div><div><h3>Objective</h3><div>The study aimed to analyze the independent and combined effects of temperature and relative humidity on SC.</div></div><div><h3>Methods</h3><div>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.</div></div><div><h3>Results</h3><div>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 (&lt;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.</div></div><div><h3>Conclusions</h3><div>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.</div></div>","PeriodicalId":48593,"journal":{"name":"Microbial Risk Analysis","volume":"30 ","pages":"Article 100354"},"PeriodicalIF":4.0,"publicationDate":"2025-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144903097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficacy of machine learning models for the prediction of death occurrence and counts associated with foodborne illnesses and hospitalizations in the United States","authors":"Mohammed Rashad Baker ,&nbsp;Selim Buyrukoğlu ,&nbsp;Gonca Buyrukoğlu ,&nbsp;Juan Moreira ,&nbsp;Zeynal Topalcengiz","doi":"10.1016/j.mran.2025.100351","DOIUrl":"10.1016/j.mran.2025.100351","url":null,"abstract":"<div><div>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. <em>Salmonella</em> was the leading causative agent (54.23 %) of bacterial foodborne outbreaks, followed by pathogenic <em>Escherichia coli</em> (12.13 %). Norovirus (96.69 %) and <em>Cyclospora cayetanensis</em> (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 &lt;0.9 with MAE, except for <em>Listeria monocytogenes</em> 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.</div></div>","PeriodicalId":48593,"journal":{"name":"Microbial Risk Analysis","volume":"30 ","pages":"Article 100351"},"PeriodicalIF":4.0,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144813915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving a microbial risk assessment tool with direct feedback from school health staff","authors":"Mehedi Hasan ,&nbsp;Megan Peterson ,&nbsp;Emily K. Waldron ,&nbsp;Nathan L. Mottern ,&nbsp;Nicole T. Pargas ,&nbsp;Lynn B. Gerald ,&nbsp;Ashley A. Lowe ,&nbsp;Amanda M. Wilson","doi":"10.1016/j.mran.2025.100352","DOIUrl":"10.1016/j.mran.2025.100352","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":48593,"journal":{"name":"Microbial Risk Analysis","volume":"30 ","pages":"Article 100352"},"PeriodicalIF":4.0,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144813914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The prevalence and concentrations of four waste-pathogen combinations from land-spreading across high-income, temperate regions – A scoping review and pooled analysis","authors":"Jennifer E.M. McCarthy ,&nbsp;Paul D Hynds ,&nbsp;Declan J Bolton ,&nbsp;Jesús M Frías Celayeta","doi":"10.1016/j.mran.2025.100350","DOIUrl":"10.1016/j.mran.2025.100350","url":null,"abstract":"<div><div>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-<em>Cryptosporidium parvum</em>, broiler litter-<em>Campylobacter jejuni</em>, 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 (<em>N</em> = 14,204) to 48 % for norovirus GI/GII (<em>N</em> = 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 <em>C. jejuni</em> 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.</div></div>","PeriodicalId":48593,"journal":{"name":"Microbial Risk Analysis","volume":"30 ","pages":"Article 100350"},"PeriodicalIF":4.0,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144738463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"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)","authors":"Jennifer E M McCarthy ,&nbsp;Paul D Hynds ,&nbsp;Declan J Bolton ,&nbsp;Jesús M Frías Celayeta","doi":"10.1016/j.mran.2025.100348","DOIUrl":"10.1016/j.mran.2025.100348","url":null,"abstract":"<div><div>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-<em>Cryptosporidium parvum</em>, broiler litter-<em>Campylobacter jejuni</em>, 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 <em>p</em> = 0.05 to <em>C. jejuni</em> 39 % OR 1.48 <em>p</em> &lt; 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, <em>p</em> = 0.55; σ = 0.66, <em>p</em> = 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.</div></div>","PeriodicalId":48593,"journal":{"name":"Microbial Risk Analysis","volume":"30 ","pages":"Article 100348"},"PeriodicalIF":3.0,"publicationDate":"2025-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144490852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comparison of machine learning models for predicting Vibrio parahaemolyticus in oysters","authors":"Nodali Ndraha ,&nbsp;Hsin-I Hsiao","doi":"10.1016/j.mran.2025.100345","DOIUrl":"10.1016/j.mran.2025.100345","url":null,"abstract":"<div><div><em>Vibrio parahaemolyticus</em>, 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 &gt; 0.85 training, &gt; 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 &gt;4 m/s, positive &gt;6 m/s). Precipitation, salinity (&gt;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.</div></div>","PeriodicalId":48593,"journal":{"name":"Microbial Risk Analysis","volume":"30 ","pages":"Article 100345"},"PeriodicalIF":3.0,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144272086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Human close contact behavior based respiratory diseases transmission in a university office building","authors":"Nan Zhang,&nbsp;Palmira Elisa Nhantumbo,&nbsp;Haochen Zhang","doi":"10.1016/j.mran.2025.100344","DOIUrl":"10.1016/j.mran.2025.100344","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":48593,"journal":{"name":"Microbial Risk Analysis","volume":"29 ","pages":"Article 100344"},"PeriodicalIF":3.0,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143882536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}