Pub Date : 2026-01-24DOI: 10.1016/j.mran.2026.100366
Marko E. Popović , Maja Stevanović , Stefan Panić
The yellow fever virus can infect several kinds of host cells in the human organism. However, liver damage dominates during yellow fever, due to lysis of hepatocytes and accumulation of virus particles inside them. Thermodynamic driving force for multiplication of viruses provides the answer to why the liver is among the most severely damaged organs during yellow fever, while less damage occurs in kidneys, spleen and bone marrow. The physicochemical perspective on pathogenesis indicates the most thermodynamically and kinetically favorable host cells for multiplication. The mechanistic model developed in this way relates the driving force as the fundamental physical force and pathogenesis as a biological phenomenon.
{"title":"Potential pandemic: Biothermodynamic analysis of the yellow fever virus-host interaction","authors":"Marko E. Popović , Maja Stevanović , Stefan Panić","doi":"10.1016/j.mran.2026.100366","DOIUrl":"10.1016/j.mran.2026.100366","url":null,"abstract":"<div><div>The yellow fever virus can infect several kinds of host cells in the human organism. However, liver damage dominates during yellow fever, due to lysis of hepatocytes and accumulation of virus particles inside them. Thermodynamic driving force for multiplication of viruses provides the answer to why the liver is among the most severely damaged organs during yellow fever, while less damage occurs in kidneys, spleen and bone marrow. The physicochemical perspective on pathogenesis indicates the most thermodynamically and kinetically favorable host cells for multiplication. The mechanistic model developed in this way relates the driving force as the fundamental physical force and pathogenesis as a biological phenomenon.</div></div>","PeriodicalId":48593,"journal":{"name":"Microbial Risk Analysis","volume":"31 ","pages":"Article 100366"},"PeriodicalIF":4.0,"publicationDate":"2026-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077971","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}
Pub Date : 2026-01-20DOI: 10.1016/j.mran.2026.100365
Eduardo de Freitas Costa , Andries A. Kampfraath , Dirkjan Schokker , Menno van der Voort , Roan Pijnacker , Clazien J. de Vos , Eric G. Evers , Alex Bossers , Jose L. Gonzales , Ewa Pacholewicz
Quantitative Microbiological Risk assessment (QMRA) models are essential tools for setting up mitigation strategies. Traditional QMRA modelling approaches do not account for the correlation between genetic traits and variability among pathogens, potentially leading to over- or underestimation of microbial exposure and associated risks. We aimed to integrate genomic data into QMRA to propagate bacterial strain variability and update the existing framework of QMRA, following a Next Generation Risk Assessment (NGRA) approach. We used a benchmark QMRA model describing the prevalence and concentration of Campylobacter jejuni on chicken in all stages from farm-to-fork, to model the risk of infection and illness related to consumption of chicken meat. We integrated extended the storage step, to account for genetic variability in cold inactivation by incorporating gene-level genomic data associated with cold tolerance, derived from literature and a large C. jejuni genomic dataset, into the traditional QMRA model by setting up cold inactivation curves from existing data to map the relationship between the number of cold tolerance genes and temperature-dependent inactivation. The predicted number of cases was 8822 human cases/year in the benchmark QMRA model. The contamination of meat with C. jejuni strains having lower cold tolerance genes can reduce the expected number of human campylobacteriosis cases up to 100%; on the other hand, higher number of cold tolerance genes resulted in an increase up to 335.8% on the expected number of cases. Although our results are based on simulations, we show a potential implementation of the genetic information into QMRA, linking risk estimates with whole-genome sequencing data. More research is needed to understand how genetic features shape phenotypical characteristics, which is one of the main uncertainties in the current NGRA model, and to further explore the implications for risk management.
{"title":"Next generation risk assessment: A proof of concept for the integration of genomic data on cold tolerance into quantitative microbial risk assessment for Campylobacter jejuni in poultry meat","authors":"Eduardo de Freitas Costa , Andries A. Kampfraath , Dirkjan Schokker , Menno van der Voort , Roan Pijnacker , Clazien J. de Vos , Eric G. Evers , Alex Bossers , Jose L. Gonzales , Ewa Pacholewicz","doi":"10.1016/j.mran.2026.100365","DOIUrl":"10.1016/j.mran.2026.100365","url":null,"abstract":"<div><div>Quantitative Microbiological Risk assessment (QMRA) models are essential tools for setting up mitigation strategies. Traditional QMRA modelling approaches do not account for the correlation between genetic traits and variability among pathogens, potentially leading to over- or underestimation of microbial exposure and associated risks. We aimed to integrate genomic data into QMRA to propagate bacterial strain variability and update the existing framework of QMRA, following a Next Generation Risk Assessment (NGRA) approach. We used a benchmark QMRA model describing the prevalence and concentration of <em>Campylobacter jejuni</em> on chicken in all stages from farm-to-fork, to model the risk of infection and illness related to consumption of chicken meat. We integrated extended the storage step, to account for genetic variability in cold inactivation by incorporating gene-level genomic data associated with cold tolerance, derived from literature and a large <em>C. jejuni</em> genomic dataset, into the traditional QMRA model by setting up cold inactivation curves from existing data to map the relationship between the number of cold tolerance genes and temperature-dependent inactivation. The predicted number of cases was 8822 human cases/year in the benchmark QMRA model. The contamination of meat with <em>C. jejuni</em> strains having lower cold tolerance genes can reduce the expected number of human campylobacteriosis cases up to 100%; on the other hand, higher number of cold tolerance genes resulted in an increase up to 335.8% on the expected number of cases. Although our results are based on simulations, we show a potential implementation of the genetic information into QMRA, linking risk estimates with whole-genome sequencing data. More research is needed to understand how genetic features shape phenotypical characteristics, which is one of the main uncertainties in the current NGRA model, and to further explore the implications for risk management.</div></div>","PeriodicalId":48593,"journal":{"name":"Microbial Risk Analysis","volume":"31 ","pages":"Article 100365"},"PeriodicalIF":4.0,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037795","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}
Pub Date : 2026-01-14DOI: 10.1016/j.mran.2026.100364
Wilson José Fernandes Lemos Junior , Larissa P Margalho , Claudio Cipolat-Gotet , Anderson S. Sant'Ana
Recent advances in genomics, pangenomics, transcriptomics, and metatranscriptomics have expanded the resolution with which microbial traits relevant to food safety can be described. These approaches complement classical predictive models, which traditionally rely on population-averaged parameters and may overlook the heterogeneity that exists among strains and microbial communities. Omics data help identify genetic, functional, and regulatory features that underpin differences in stress tolerance, growth potential, and virulence, offering a more precise basis for hazard identification and exposure assessment. Genomic and pangenomic analyses clarify how core and accessory gene pools shape strain-level behavior, while transcriptomic studies reveal active pathways during acid, cold, or osmotic challenges. Metatranscriptomics extends this insight to complex communities, capturing how dominant and satellite members contribute to ecosystem function under food-relevant conditions. Incorporating these datasets into predictive microbiology and quantitative microbial risk assessment (QMRA) supports more realistic estimates of growth, survival, and persistence, reducing uncertainty in hazard characterization. Evidence shows that many food-associated strains are hypovirulent or slow-growing, indicating that risk may be overestimated when genetic heterogeneity is not considered. Although molecular data do not directly prescribe mitigation strategies, they support risk management by identifying which subpopulations merit targeted interventions, clarifying which process parameters influence persistence, and refining prioritization decisions. Our work discusses how omics tools align with primary, secondary, and tertiary predictive models and examines the complementarity between traditional decision-making frameworks and AI-based methods. Emphasis is also placed on sustainability, as omics-informed modeling enables more efficient in silico assessments and reduces dependence on resource-intensive challenge testing. Together, these developments strengthen the connection between risk assessment and risk management, supporting more proportionate and informed food safety decisions.
{"title":"Genomic, pangenomic, metagenomic and trancriptomics perspectives to enhance microbial modeling and quantitative risk assessment in food environments","authors":"Wilson José Fernandes Lemos Junior , Larissa P Margalho , Claudio Cipolat-Gotet , Anderson S. Sant'Ana","doi":"10.1016/j.mran.2026.100364","DOIUrl":"10.1016/j.mran.2026.100364","url":null,"abstract":"<div><div>Recent advances in genomics, pangenomics, transcriptomics, and metatranscriptomics have expanded the resolution with which microbial traits relevant to food safety can be described. These approaches complement classical predictive models, which traditionally rely on population-averaged parameters and may overlook the heterogeneity that exists among strains and microbial communities. Omics data help identify genetic, functional, and regulatory features that underpin differences in stress tolerance, growth potential, and virulence, offering a more precise basis for hazard identification and exposure assessment. Genomic and pangenomic analyses clarify how core and accessory gene pools shape strain-level behavior, while transcriptomic studies reveal active pathways during acid, cold, or osmotic challenges. Metatranscriptomics extends this insight to complex communities, capturing how dominant and satellite members contribute to ecosystem function under food-relevant conditions. Incorporating these datasets into predictive microbiology and quantitative microbial risk assessment (QMRA) supports more realistic estimates of growth, survival, and persistence, reducing uncertainty in hazard characterization. Evidence shows that many food-associated strains are hypovirulent or slow-growing, indicating that risk may be overestimated when genetic heterogeneity is not considered. Although molecular data do not directly prescribe mitigation strategies, they support risk management by identifying which subpopulations merit targeted interventions, clarifying which process parameters influence persistence, and refining prioritization decisions. Our work discusses how omics tools align with primary, secondary, and tertiary predictive models and examines the complementarity between traditional decision-making frameworks and AI-based methods. Emphasis is also placed on sustainability, as omics-informed modeling enables more efficient in silico assessments and reduces dependence on resource-intensive challenge testing. Together, these developments strengthen the connection between risk assessment and risk management, supporting more proportionate and informed food safety decisions.</div></div>","PeriodicalId":48593,"journal":{"name":"Microbial Risk Analysis","volume":"31 ","pages":"Article 100364"},"PeriodicalIF":4.0,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037796","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}
Pub Date : 2025-12-24DOI: 10.1016/j.mran.2025.100363
Aakash Pandey , Ian Spicknall , Andrea M. McCollum , Christine M. Hughes , Beatrice Nguete , Toutou Likafi , Robert Shongo Lushima , Placide Mbala-Kingebeni , Joelle Kabamba , Didine Kaba , Yoshinori Nakazawa
The global mpox outbreak of 2022, caused by the Clade IIb strain of monkeypox virus, underscored the potential of this virus to pose a significant public health threat on a global scale. The Democratic Republic of Congo is currently facing multiple outbreaks associated with Clade I. Effectively controlling localized community transmission within endemic areas through vaccination can reduce the likelihood of broader regional or even global outbreaks. Large-scale community vaccination in DRC is challenged by limited resources, including vaccine availability during early outbreaks in remote areas, whereas limited surveillance, contact tracing, and accessibility to remote locations can reduce the effectiveness of targeted ring vaccination. Furthermore, recent outbreaks in DRC have been driven by both sexual and non-sexual close contact transmissions. Here, we used an agent-based model with stochastic transmission within and between households to assess the effectiveness of ring vaccination for controlling localized community transmission in the presence of incomplete case reporting and delay in vaccination. We consider both nonsexual close contact and sexual transmission. We found that ring vaccination, even with 25–50 % reporting, is effective in reducing outbreak cluster sizes and the likelihood of large cluster sizes (>5 cases), particularly when implemented shortly after detection of initial cases. The effectiveness of ring vaccination reduces with the inclusion of sexual transmission. We show that outbreak size and the likelihood of large clusters are reduced when responding to every reported infection, even with 2–3 weeks of delay. Settings with strong surveillance systems characterized by high levels of reporting will have earlier case detection, enabling earlier response and improving the effectiveness of ring vaccination.
{"title":"Optimizing vaccination strategies for mpox control in endemic areas: Modeling insights from the Democratic Republic of Congo","authors":"Aakash Pandey , Ian Spicknall , Andrea M. McCollum , Christine M. Hughes , Beatrice Nguete , Toutou Likafi , Robert Shongo Lushima , Placide Mbala-Kingebeni , Joelle Kabamba , Didine Kaba , Yoshinori Nakazawa","doi":"10.1016/j.mran.2025.100363","DOIUrl":"10.1016/j.mran.2025.100363","url":null,"abstract":"<div><div>The global mpox outbreak of 2022, caused by the Clade IIb strain of monkeypox virus, underscored the potential of this virus to pose a significant public health threat on a global scale. The Democratic Republic of Congo is currently facing multiple outbreaks associated with Clade I. Effectively controlling localized community transmission within endemic areas through vaccination can reduce the likelihood of broader regional or even global outbreaks. Large-scale community vaccination in DRC is challenged by limited resources, including vaccine availability during early outbreaks in remote areas, whereas limited surveillance, contact tracing, and accessibility to remote locations can reduce the effectiveness of targeted ring vaccination. Furthermore, recent outbreaks in DRC have been driven by both sexual and non-sexual close contact transmissions. Here, we used an agent-based model with stochastic transmission within and between households to assess the effectiveness of ring vaccination for controlling localized community transmission in the presence of incomplete case reporting and delay in vaccination. We consider both nonsexual close contact and sexual transmission. We found that ring vaccination, even with 25–50 % reporting, is effective in reducing outbreak cluster sizes and the likelihood of large cluster sizes (>5 cases), particularly when implemented shortly after detection of initial cases. The effectiveness of ring vaccination reduces with the inclusion of sexual transmission. We show that outbreak size and the likelihood of large clusters are reduced when responding to every reported infection, even with 2–3 weeks of delay. Settings with strong surveillance systems characterized by high levels of reporting will have earlier case detection, enabling earlier response and improving the effectiveness of ring vaccination.</div></div>","PeriodicalId":48593,"journal":{"name":"Microbial Risk Analysis","volume":"31 ","pages":"Article 100363"},"PeriodicalIF":4.0,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145883990","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}
Pub Date : 2025-12-03DOI: 10.1016/j.mran.2025.100362
Mickael Teixeira Alves , Mark Thrush , Edmund J. Peeler , Sophie Armitage , Debbie Murphy , Chantelle Hooper , John P. Bignell , Richard Hazelgrove , David Bass , Hannah J. Tidbury
Timely, comprehensive risk assessments for disease management are often challenging, particularly in the event of sudden epidemiological events, new threats or shifting drivers of disease expression. To better address emerging threats and justify control measures for existing diseases, a rapid risk assessment tool was developed to support strategic decision-making and effective aquatic animal health management. Based on international standards for risk analysis from the World Health Organisation and World Organisation for Animal Health, this tool aims to provide a systematic, transparent and repeatable risk assessment of aquatic animal diseases to advise on policy decision. Accessibility of the tool facilitates rapid gathering and evaluation of a wide range of information and data, in turn ensuring a flexible, dynamic and effective process to respond to rapid changes in knowledge or epidemiological situation. Informing on the amount of uncertainty at each step of the risk assessment, the tool directly supports risk communication by providing a clear, well-defined structure based on common risk methodology and terminology, and assists risk managers in understanding broader threats, including impacts on trade, wildlife and the environment. Applications of the tool to a wide range of epidemiological contexts, including novel domestic events, investigation of mortality events, data collation from expert elicitation, information dissemination and disease ranking for policy strategic prioritisation, provide valuable insights facilitating the provision and communication of risk-based advice to underpin aquatic disease management.
{"title":"Development and implementation of a rapid risk assessment tool to enhance and standardise aquatic animal health risk management","authors":"Mickael Teixeira Alves , Mark Thrush , Edmund J. Peeler , Sophie Armitage , Debbie Murphy , Chantelle Hooper , John P. Bignell , Richard Hazelgrove , David Bass , Hannah J. Tidbury","doi":"10.1016/j.mran.2025.100362","DOIUrl":"10.1016/j.mran.2025.100362","url":null,"abstract":"<div><div>Timely, comprehensive risk assessments for disease management are often challenging, particularly in the event of sudden epidemiological events, new threats or shifting drivers of disease expression. To better address emerging threats and justify control measures for existing diseases, a rapid risk assessment tool was developed to support strategic decision-making and effective aquatic animal health management. Based on international standards for risk analysis from the World Health Organisation and World Organisation for Animal Health, this tool aims to provide a systematic, transparent and repeatable risk assessment of aquatic animal diseases to advise on policy decision. Accessibility of the tool facilitates rapid gathering and evaluation of a wide range of information and data, in turn ensuring a flexible, dynamic and effective process to respond to rapid changes in knowledge or epidemiological situation. Informing on the amount of uncertainty at each step of the risk assessment, the tool directly supports risk communication by providing a clear, well-defined structure based on common risk methodology and terminology, and assists risk managers in understanding broader threats, including impacts on trade, wildlife and the environment. Applications of the tool to a wide range of epidemiological contexts, including novel domestic events, investigation of mortality events, data collation from expert elicitation, information dissemination and disease ranking for policy strategic prioritisation, provide valuable insights facilitating the provision and communication of risk-based advice to underpin aquatic disease management.</div></div>","PeriodicalId":48593,"journal":{"name":"Microbial Risk Analysis","volume":"31 ","pages":"Article 100362"},"PeriodicalIF":4.0,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145749926","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}
Pub Date : 2025-12-01DOI: 10.1016/j.mran.2025.100358
Jane Pouzou , Solenne Costard , Régis Pouillot , Daniel Taylor , Francisco Zagmutt
We developed a QMRA model to assess changes in Non-Typhoidal Salmonella (NTS) illnesses under scenarios of microbial criteria (MC) based on proportion tested (TP) of production lots (combos), concentration rejection thresholds per gram (CT), differential targeting of the “higher” (HV) and “lower” (LV) virulence serovars, and concentration reduction (e.g. kill steps). MC scenarios were compared against a baseline without diversion of product positive for NTS. The QMRA incorporates a novel epidemiologically-validated genomic method to group serovars by virulence, and corresponding dose-response functions reflecting differences in HV and LV infectivity. HV serovars were S. Typhimurium, S. 1,4,(5),12:i:-, S. Enteritidis, S. Newport, S. Dublin, S. Paratyphi B, S. Muenchen, S. Agona, and S. Litchfield. Illness reductions >25 % can be achieved for TP >50 %, and CT of 1 or 10 CFU/g. Mean illness reduction of 71.0 % (95 %PI = 61.6-76.7 %) is predicted with TP = 100 % at CT = 10 CFU/g. At >50 % TP, illness reductions were similar for CT = 1 vs CT = 10 while more combos were diverted (e.g., 5.1×10-3 % vs 1.1×10-3 % of combos, respectively under 100 % TP). MC targeting only HV achieved significant illness reductions, but with fewer diverted combos. For example, applying the CT = 10 CFU/g only to HV with TP = 100 % led to a mean reduction of 46 % (34.9-56.4 %) and 1.1×10-5 of combos diverted.
MC targeting higher virulence and/or higher concentrations of NTS can achieve large public health gains. Optimal MC should also consider feasibility and cost of product diversions, speed and precision of assays to accurately identify high concentration combos, and individual plant characteristics that affect NTS concentrations and virulence profiles.
{"title":"Incorporating genomic virulence in a quantitative microbial risk assessment to assess the public health impact of alternative microbiological criteria for Salmonella","authors":"Jane Pouzou , Solenne Costard , Régis Pouillot , Daniel Taylor , Francisco Zagmutt","doi":"10.1016/j.mran.2025.100358","DOIUrl":"10.1016/j.mran.2025.100358","url":null,"abstract":"<div><div>We developed a QMRA model to assess changes in Non-Typhoidal <em>Salmonella</em> (NTS) illnesses under scenarios of microbial criteria (MC) based on proportion tested (<em>TP</em>) of production lots (combos), concentration rejection thresholds per gram (<em>CT</em>), differential targeting of the “higher” (HV) and “lower” (LV) virulence serovars, and concentration reduction (e.g. kill steps). MC scenarios were compared against a baseline without diversion of product positive for NTS. The QMRA incorporates a novel epidemiologically-validated genomic method to group serovars by virulence, and corresponding dose-response functions reflecting differences in HV and LV infectivity. HV serovars were <em>S.</em> Typhimurium, <em>S.</em> 1,4,(5),12:i:-, <em>S.</em> Enteritidis, <em>S</em>. Newport, <em>S.</em> Dublin, <em>S</em>. Paratyphi B, <em>S</em>. Muenchen, <em>S.</em> Agona, and <em>S</em>. Litchfield. Illness reductions >25 % can be achieved for <em>TP</em> >50 %, and <em>CT</em> of 1 or 10 CFU/g. Mean illness reduction of 71.0 % (95 %PI = 61.6-76.7 %) is predicted with <em>TP</em> = 100 % at <em>CT</em> = 10 CFU/g. At >50 % <em>TP</em>, illness reductions were similar for <em>CT</em> = 1 <em>vs CT</em> = 10 while more combos were diverted (e.g., 5.1×10<sup>-3</sup> % vs 1.1×10<sup>-3</sup> % of combos, respectively under 100 % <em>TP</em>). MC targeting only HV achieved significant illness reductions, but with fewer diverted combos. For example, applying the <em>CT</em> = 10 CFU/g only to HV with <em>TP</em> = 100 % led to a mean reduction of 46 % (34.9-56.4 %) and 1.1×10<sup>-5</sup> of combos diverted.</div><div>MC targeting higher virulence and/or higher concentrations of NTS can achieve large public health gains. Optimal MC should also consider feasibility and cost of product diversions, speed and precision of assays to accurately identify high concentration combos, and individual plant characteristics that affect NTS concentrations and virulence profiles.</div></div>","PeriodicalId":48593,"journal":{"name":"Microbial Risk Analysis","volume":"30 ","pages":"Article 100358"},"PeriodicalIF":4.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145683690","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}
Pub Date : 2025-12-01DOI: 10.1016/j.mran.2025.100357
Rodney J. Feliciano, Jeanne-Marie Membré, Louis Delaunay
Lentils are promoted as an alternative protein source due to their agricultural and nutritional benefits. However, information on the microbial risks associated with lentil consumption in domestic settings is limited. The countries of France and Hungary were selected to represent the lentil consumption in two different supply chains. Bacillus cereus was identified as a pathogen of concern in both hot and cold dishes, whereas Listeria monocytogenes was only identified in cold dishes. A probabilistic model accounting for uncertainty and variability was constructed, estimating the microbial concentration at subsequent stages of domestic handling: cooking, cooling, and 24–96 h of chilled storage. The number of foodborne illness cases and daily adjusted life years (DALY) were estimated at the point of consumption.
The results were analysed at each stage; for example, at 96 h, B. cereus showed mean values of 2.52 [1.75; 4.12] log CFU/g in France and 2.03 [1.24; 3.56] log CFU/g in Hungary. For Listeria monocytogenes, the mean estimates were lower at 0.48 [−0.20; 1.16] log CFU/g in France and 0.69 [0.07; 1.36] log CFU/g in Hungary. The overall number of foodborne illness cases from both pathogens was computed based on the consumption frequency. They were estimated to be 0 [0–44] cases per 100,000 in France and 0 [0–5.8] in Hungary.
The uncertainty intervals are relatively large, reflecting uncertainty in the estimates, meaning that the risk is not an absolute zero. Moreover, it is likely that dietary shifts towards less meat consumption, as promoted by various institutions, will occur. Extended batch cooking practices can pose an additional risk of foodborne illnesses in the future.
{"title":"Microbial risk and health burden associated with the domestic preparation of lentils in France and Hungary","authors":"Rodney J. Feliciano, Jeanne-Marie Membré, Louis Delaunay","doi":"10.1016/j.mran.2025.100357","DOIUrl":"10.1016/j.mran.2025.100357","url":null,"abstract":"<div><div>Lentils are promoted as an alternative protein source due to their agricultural and nutritional benefits. However, information on the microbial risks associated with lentil consumption in domestic settings is limited. The countries of France and Hungary were selected to represent the lentil consumption in two different supply chains. <em>Bacillus cereus</em> was identified as a pathogen of concern in both hot and cold dishes, whereas <em>Listeria monocytogenes</em> was only identified in cold dishes. A probabilistic model accounting for uncertainty and variability was constructed, estimating the microbial concentration at subsequent stages of domestic handling: cooking, cooling, and 24–96 h of chilled storage. The number of foodborne illness cases and daily adjusted life years (DALY) were estimated at the point of consumption.</div><div>The results were analysed at each stage; for example, at 96 h, <em>B. cereus</em> showed mean values of 2.52 [1.75; 4.12] log CFU/g in France and 2.03 [1.24; 3.56] log CFU/g in Hungary. For <em>Listeria monocytogenes</em>, the mean estimates were lower at 0.48 [−0.20; 1.16] log CFU/g in France and 0.69 [0.07; 1.36] log CFU/g in Hungary. The overall number of foodborne illness cases from both pathogens was computed based on the consumption frequency. They were estimated to be 0 [0–44] cases per 100,000 in France and 0 [0–5.8] in Hungary.</div><div>The uncertainty intervals are relatively large, reflecting uncertainty in the estimates, meaning that the risk is not an absolute zero. Moreover, it is likely that dietary shifts towards less meat consumption, as promoted by various institutions, will occur. Extended batch cooking practices can pose an additional risk of foodborne illnesses in the future.</div></div>","PeriodicalId":48593,"journal":{"name":"Microbial Risk Analysis","volume":"30 ","pages":"Article 100357"},"PeriodicalIF":4.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145614524","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}
Pub Date : 2025-11-14DOI: 10.1016/j.mran.2025.100359
Yiyi Li, Cecil Barnett-Neefs, Matthew J. Stasiewicz
Prevalence-based performance standards have guided Salmonella control in poultry industry, but concentration- and virulence-based final product standards could target the most risky contamination more specifically. We adapted our previous risk assessment for chicken parts to comminuted turkey to assess the risk in products implicated by different final product standards, incorporating assumptions from FSIS 2024 risk assessments. We simulated the attributable fraction of illnesses from products contaminated over three level thresholds (0.0031 CFU/g, 1 CFU/g, and 10 CFU/g) and/or containing a serotype in three lists (“Top 3 most prevalent higher-virulence serotypes”, “All higher-virulence serotypes”, and “Higher-virulence proportion of each serotype”). Results showed that 87 % of illnesses were attributed to the 0.56 % of products with Salmonella exceeding 10 CFU/g. Under more specific criteria of level “AND” serotype, 60 % of illnesses were attributed to the 0.14 % of products contaminated with Salmonella exceeding 10 CFU/g and one of the three most prevalent higher-virulence serotypes. Further, applying genomic-based clustering information, 75 % of illnesses were attributed to slightly more products (0.19 %) containing Salmonella exceeding 10 CFU/g and higher-virulence proportion of each serotype. Under the less specific standard, however, 99 % of illnesses were attributed to the 5.7 % of products containing Salmonella exceeding 10 CFU/g “OR” one of the higher-virulence serotypes. Our study demonstrated that most salmonellosis risk is concentrated in comminuted turkey products with high levels of higher-virulence contaminations. Importantly, more specifically targeting those products could efficiently reduce public health risk while minimizing products implicated.
{"title":"Salmonellosis risk assessment for comminuted turkey under different specificities of concentration-based and virulence-based final product standards","authors":"Yiyi Li, Cecil Barnett-Neefs, Matthew J. Stasiewicz","doi":"10.1016/j.mran.2025.100359","DOIUrl":"10.1016/j.mran.2025.100359","url":null,"abstract":"<div><div>Prevalence-based performance standards have guided <em>Salmonella</em> control in poultry industry, but concentration- and virulence-based final product standards could target the most risky contamination more specifically. We adapted our previous risk assessment for chicken parts to comminuted turkey to assess the risk in products implicated by different final product standards, incorporating assumptions from FSIS 2024 risk assessments. We simulated the attributable fraction of illnesses from products contaminated over three level thresholds (0.0031 CFU/g, 1 CFU/g, and 10 CFU/g) and/or containing a serotype in three lists (“Top 3 most prevalent higher-virulence serotypes”, “All higher-virulence serotypes”, and “Higher-virulence proportion of each serotype”). Results showed that 87 % of illnesses were attributed to the 0.56 % of products with <em>Salmonella</em> exceeding 10 CFU/g. Under more specific criteria of level “AND” serotype, 60 % of illnesses were attributed to the 0.14 % of products contaminated with <em>Salmonella</em> exceeding 10 CFU/g and one of the three most prevalent higher-virulence serotypes. Further, applying genomic-based clustering information, 75 % of illnesses were attributed to slightly more products (0.19 %) containing <em>Salmonella</em> exceeding 10 CFU/g and higher-virulence proportion of each serotype. Under the less specific standard, however, 99 % of illnesses were attributed to the 5.7 % of products containing <em>Salmonella</em> exceeding 10 CFU/g “OR” one of the higher-virulence serotypes. Our study demonstrated that most salmonellosis risk is concentrated in comminuted turkey products with high levels of higher-virulence contaminations. Importantly, more specifically targeting those products could efficiently reduce public health risk while minimizing products implicated.</div></div>","PeriodicalId":48593,"journal":{"name":"Microbial Risk Analysis","volume":"31 ","pages":"Article 100359"},"PeriodicalIF":4.0,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145665510","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}
Pub Date : 2025-09-25DOI: 10.1016/j.mran.2025.100356
Dixuan Cai , Jinhan He , Runrun Zhang , Xinyu Liao , Juhee Ahn , Jinsong Feng , Tian Ding
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.
{"title":"Quantitative data and models for bacterial cross-contamination in domestic kitchen during food handling and preparation","authors":"Dixuan Cai , Jinhan He , Runrun Zhang , Xinyu Liao , Juhee Ahn , Jinsong Feng , 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}
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.
{"title":"Literature-based explainable machine learning models for predicting pathogen and antibiotic resistance gene loads from animal manure","authors":"Ayşe Birsen Kadıoğlu Gökalp , Handan Atalay Eroğlu , 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 (R<sup>2</sup> = 0.93) for pathogen load and Ridge Regression algorithm (R<sup>2</sup> = 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}
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