Microbial Risk Analysis最新文献

{"title":"Microbial risk analysis from a food industry perspective – insights from an international survey","authors":"Alexandra Fetsch ,&nbsp;Nunzio Sarnino ,&nbsp;Konstantinos Koutsoumanis ,&nbsp;Maarten Nauta ,&nbsp;Martin Wiedmann ,&nbsp;Katharina D.C. Stärk ,&nbsp;Monika Ehling-Schulz ,&nbsp;Roger Stephan ,&nbsp;Sophia Johler","doi":"10.1016/j.mran.2024.100340","DOIUrl":"10.1016/j.mran.2024.100340","url":null,"abstract":"<div><div>Foodborne microbial hazards lead to substantial morbidity and mortality. To assure consumer protection, a need to move from hazard-based to risk-based food safety approaches is increasingly recognized. Food-business-operators play a crucial role by implementing risk management practices in their facilities. Still, there is very limited data on current approaches to ensure microbial food safety and the profiles and perceptions of professionals assessing, managing, and communicating risks in food industry. This study addresses food safety approaches and challenges in food industry aiming to provide data on microbial risk analysis according to Codex Alimentarius. A survey elicited responses from 108 food professionals involved in microbial risk assessment, risk management, or risk communication in the food industry. The findings highlight drivers and trends relevant to food safety and the food industries’ internal decision-making processes. Most participants had risk-based food-safety management systems established. A microbial risk assessment according to Codex Alimentarius principles was conducted by 85 %. Professionals pinpointed areas that led to significant microbial incidents such as contaminated raw materials, poor hygiene, or emerging pathogens. Interestingly, one third of the participants believed that zero risk is possible, which contrasts with the scientific consensus that microbial food safety is not absolute as zero risk is not feasible. The results of this work provide insights into the implementation and understanding of microbial risk analysis from a food industrial perspective and could be leveraged to develop innovative microbial risk analysis frameworks that meet the challenges of future food systems.</div></div>","PeriodicalId":48593,"journal":{"name":"Microbial Risk Analysis","volume":"29 ","pages":"Article 100340"},"PeriodicalIF":3.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143140945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantitative microbiological risk assessment using individual data on food storage and consumption (Part 2): A comparison with traditional QMRA approaches","authors":"Hernán G. Redondo ,&nbsp;Laurent Guillier ,&nbsp;Virginie Desvignes ,&nbsp;Matthias Filter ,&nbsp;Sara M. Pires ,&nbsp;Maarten Nauta","doi":"10.1016/j.mran.2024.100339","DOIUrl":"10.1016/j.mran.2024.100339","url":null,"abstract":"<div><div>In a previous study, we integrated data from individual consumers collected in a dietary survey in France in a multi-food quantitative microbiological risk assessment (QMRA) for listeriosis. Here, we compared the “individual-based” modelling approach applied in that study with several other approaches where the data are treated as in more “traditional” QMRA methods, for example by assuming independent randomly sampled variables from distributions fitted through the data, instead of the observed individual data themselves. We found that assigning randomly sampled storage times instead of the reported individual storage times resulted in a higher risk estimate than the baseline, expressed as expected annual number of cases in the population. Assigning randomly sampled storage temperature and point estimates for portion size and frequency of consumption, slightly increased the estimated risk. Statistical analysis did not show dependency between portion size, frequency of consumption, storage temperature and storage time in the data set, which can be explained by the fact that only a few individuals had a large impact on the final population risk. Analysis of expected numbers of cases per age class, sex and food group showed small differences between approaches. Our analysis was challenged by the difference between a model structure where the risk is calculated per individual (when based on a dietary survey with individual data) and one where it is calculated per serving, as in “traditional” QMRA. We showed that an “individual-based” QMRA is more resource-demanding but can give fundamentally different risk estimates, which are potentially more accurate. The application of tools for efficient knowledge exchange and integration is needed to facilitate the usage of this type of QMRA.</div></div>","PeriodicalId":48593,"journal":{"name":"Microbial Risk Analysis","volume":"29 ","pages":"Article 100339"},"PeriodicalIF":3.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143140946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Microbial assessments for import risk assessment: reflections and future perspectives","authors":"Amie Adkin,&nbsp;Steve Wearne","doi":"10.1016/j.mran.2025.100341","DOIUrl":"10.1016/j.mran.2025.100341","url":null,"abstract":"","PeriodicalId":48593,"journal":{"name":"Microbial Risk Analysis","volume":"29 ","pages":"Article 100341"},"PeriodicalIF":3.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143140947","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":"Pathogens-in-Foods (PIF): An open-access European database of occurrence data of biological hazards in foods","authors":"Ursula Gonzales-Barron ,&nbsp;Ana Sofia Faria ,&nbsp;Anne Thebault ,&nbsp;Laurent Guillier ,&nbsp;Lucas Ribeiro Mendes ,&nbsp;Lucas Ribeiro Silva ,&nbsp;Winy Messens ,&nbsp;Pauline Kooh ,&nbsp;Vasco Cadavez","doi":"10.1016/j.mran.2025.100342","DOIUrl":"10.1016/j.mran.2025.100342","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":48593,"journal":{"name":"Microbial Risk Analysis","volume":"29 ","pages":"Article 100342"},"PeriodicalIF":3.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143509337","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":"An international disease monitoring tool to estimate the likelihood of entry of animal health hazards from legal trade of live animals and products of animal origin imported from different countries (IDM+)","authors":"Alex Royden ,&nbsp;Robert Dewar ,&nbsp;Brendan Cowled ,&nbsp;Rohan Sadler ,&nbsp;Alison Hillman ,&nbsp;Laura C. Gonzalez-Villeta ,&nbsp;Helen Roberts ,&nbsp;Catherine McCarthy ,&nbsp;Robin R.L. Simons","doi":"10.1016/j.mran.2024.100338","DOIUrl":"10.1016/j.mran.2024.100338","url":null,"abstract":"<div><div>Trade of live animals and products of animal origin (POAO) carries an inherent risk of spreading pathogens. As such, it is essential for any country to have effective early detection and/or horizon scanning systems in place to be aware of the potential entry risks of pathogens. To this end, the United Kingdom has a number of horizon scanning and risk assessment tools to carry out international disease monitoring. Here we develop these ideas further in a generic, semi-quantitative risk assessment tool; the International Disease Monitoring Plus (IDM+) tool.</div><div>The IDM+ tool utilises publicly available data on the presence of terrestrial animal health pathogens and volumes of commodities imported into Great Britain (GB) from multiple trading partners. Identifying the likelihood of a pathogen arriving at the GB border involved &gt;550 individual commodity types, 125 pathogens and 55 countries. It also includes expert opinion on country and commodity specific mitigation measures, to derive a likelihood of entry score for each country, commodity, and pathogen combination.</div><div>This paper presents an example of the model used to assess commodities being imported into GB. However, the principle could be applied to any country accepting imports of live animals or POAO.</div><div>The IDM+ model considers changes in global pathogen distribution and trade volumes to provide ongoing and rapid appraisal of the likelihood of entry for different commodities, countries, and pathogens. It is designed to be quick to run with a largely automated process, further enabling rapid updates with new disease and trade source and volume data. The model can present results with and without trade volume weighting and with different likelihoods. When a specific import disease risk is identified to be of concern, carrying out a comprehensive import risk analysis is still recommended. However, this model is a valuable tool to provide a holistic overview and comparison of the likelihood of entry to GB of a large number of potential threats to animal health. It can be utilised in time-constrained environments and when limited data are available. The results can be of direct use for a variety of purposes, including, but not limited to, prioritisation of border inspections and in-country audits, rapid output generation for emergency outbreak assessments and/or assessing risk from specific imported consignments.</div></div>","PeriodicalId":48593,"journal":{"name":"Microbial Risk Analysis","volume":"29 ","pages":"Article 100338"},"PeriodicalIF":3.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143140944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","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":"2025-06-01","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}

{"title":"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","authors":"Hernán G. Redondo ,&nbsp;Laurent Guillier ,&nbsp;Virginie Desvignes ,&nbsp;Matthias Filter ,&nbsp;Sara M. Pires ,&nbsp;Maarten Nauta","doi":"10.1016/j.mran.2025.100343","DOIUrl":"10.1016/j.mran.2025.100343","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":48593,"journal":{"name":"Microbial Risk Analysis","volume":"29 ","pages":"Article 100343"},"PeriodicalIF":3.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143592291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessment of food safety risk using machine learning-assisted hyperspectral imaging: Classification of fungal contamination levels in rice grain","authors":"Ubonrat Siripatrawan ,&nbsp;Yoshio Makino","doi":"10.1016/j.mran.2024.100295","DOIUrl":"10.1016/j.mran.2024.100295","url":null,"abstract":"<div><p>A rapid and nondestructive assessment of food safety risk using machine learning-assisted hyperspectral imaging was developed for classification of fungal contamination in brown rice grain. Brown rice was inoculated with Penicillium. The fungal infected rice was then mixed with healthy rice to obtain 0 %, 5 %, 25 %, 50 % and 100 % (w/w) contamination of infected rice. Volatile compounds including pentamethyl-heptane, decane, dodecane, 3-octanone, and 1-octen-3-ol were found in fungal infected rice, as analyzed using gas chromatography-mass spectrometry. The HSI system was used to collect spectral reflectance and spatial data of the samples covering the wavelength range of 400–1000 nm. The hypercubed data were analyzed using machine learning algorithms, including principal component analysis (PCA), discriminant factor analysis (DFA) and support vector machine (SVM). Using PCA for data reduction, 3 principal components were extracted with a cumulative variance of 90.53 %. DFA (linear and quadratic algorithms) and SVM (linear, quadratic, cubic, and Gaussian algorithms) were then used to classify the samples. HSI integrated with Gaussian SVM gave 93.4% accuracy which was best for classifying rice with different percentages of contamination. The image analysis gave a pseudo-color distribution map which facilitated the visualization of the contaminated rice by presenting data in an uncomplicated image. The machine learning-assisted HSI can be used as a rapid, nondestructive and chemical-free tool for an assessment of food safety risk for rice grain.</p></div>","PeriodicalId":48593,"journal":{"name":"Microbial Risk Analysis","volume":"27 ","pages":"Article 100295"},"PeriodicalIF":2.8,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140788212","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 effect of sewage source on HF183 risk-based threshold estimation for recreational water quality management","authors":"Kyle Curtis ,&nbsp;Michael Jahne ,&nbsp;David Keeling ,&nbsp;Raul Gonzalez","doi":"10.1016/j.mran.2024.100315","DOIUrl":"https://doi.org/10.1016/j.mran.2024.100315","url":null,"abstract":"<div><p>Host-associated fecal indicator measurements can be coupled with quantitative microbial risk assessment to develop risk-based thresholds for recreational use of potential sewage-contaminated waters. These assessments require information on the relative concentrations of indicators and pathogens in discharged sewage, typically based on data collected from wastewater treatment plant influent samples. However, most untreated sewage releases occur from within the collection system itself (i.e. compromised sewer laterals, compromised gravity and force mains, sanitary sewer overflows), where these relationships may differ. This study therefore analyzed the concentrations of a selected reference pathogen (norovirus) and fecal indicator (HF183) in sewage samples from upper and lower segments of gravity sewage collection systems, wastewater pumpstations, and the influent and effluent of treatment plants, to characterize variability in their relative concentrations. Norovirus detection rates were lower and more variable in upper collection system samples due to the smaller population represented; whereas, HF183 was routinely detected at all sites with higher concentrations in the collection system compared to treatment plant influent, resulting in variable comparative relationships across sample locations (types). Mean HF183:NoV ratios ranged from 1.0 × 10⁵ for sewer lateral samples to 7 × 10° for force main samples. Results were used to develop risk-based thresholds for HF183 based on estimated recreational exposure to norovirus following a release from each potential sewage source, with higher thresholds for treatment facility influent compared to forced mains, or effluent. Consequently, this approach can allow for the rapid application of potential risk-based thresholds for recreational water quality applications based on different types of sewage discharge events.</p></div>","PeriodicalId":48593,"journal":{"name":"Microbial Risk Analysis","volume":"27 ","pages":"Article 100315"},"PeriodicalIF":3.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352352224000264/pdfft?md5=17093a2ed80eab4a8ccc55aff2792662&pid=1-s2.0-S2352352224000264-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141479189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Measuring transboundary disease spread - ASF in wild boars straddling Piedmont and Liguria","authors":"Nicoletta Vitale ,&nbsp;Paola Barzanti ,&nbsp;Ines Crescio Maria ,&nbsp;Rosanna Desiato ,&nbsp;Lisa Guardone ,&nbsp;Valeria Listorti ,&nbsp;Walter Martelli ,&nbsp;Cristiana Maurella ,&nbsp;Barbara Moroni ,&nbsp;Rosaria Possidente ,&nbsp;Francesca Rossi ,&nbsp;Giuseppe Ru","doi":"10.1016/j.mran.2024.100329","DOIUrl":"10.1016/j.mran.2024.100329","url":null,"abstract":"<div><div>The aim of this paper is to describe the impact, dynamics and risk factors of the incursion of the African Swine Fever (ASF) virus detected in early 2022 in the wild boar population straddling Piedmont and Liguria, 24 months after its emergence.</div><div>A study area of 4,162.2 km²s, comprising 234 municipalities in Liguria and Piedmont (north-western Italy), along with an external buffer zone, was considered. The epidemiological units were wild boars tested for ASF using real-time PCR between late December 2021 and December 31, 2023. The data were obtained from passive and active surveillance. Conventional methodologies were applied in the analysis of time series of tests, cases, or prevalence rates of positive animals. Maps were used to visualise and compare the monitoring activities, the location of cases and municipal standardised prevalence ratios. Clustering of high and low trends was studied by semester using a space-time permutation model. Risk factors analysis was based on multivariate Poisson regression modelling.</div><div>Over the 2-year study period, the epidemic's spread was closely monitored by testing 10,412 wild boars (25 % of them from passive surveillance). Probability of disease detection was 10.9 times higher among found dead animals compared with animals tested in active surveillance. Difficulties in carcass searching led to heterogeneities in surveillance sensitivity achieved locally and inherent uncertainties. A total of 1,165 wild boars that tested positive for ASF have been detected in 125 municipalities across three provinces. The outbreak has expanded over time and exhibited an increasing trend, with an epidemic doubling time of 10.7 months. There was seasonality, with an increasing trend from summer to spring. Prevalence rates followed the same temporal pattern. Significant low and high clusters respectively indicated endemic episodes in the interior areas of virus circulation and an ongoing invasion of surrounding regions.</div><div>The characteristics of this outbreak align with the cycle of wild boar habitat transmission observed in other outbreaks across Europe. The measures implemented were not sufficient to stop the epidemic: however, targeted interventions such as depopulation campaigns, reinforcement of physical barriers and increased biosecurity measures have prevented local spillover to domestic pigs and partially hindered the advance of the disease. In addition, the shown seasonality of the disease, likely can enhance control measures. The ongoing studies of the local wild boar populations and the evolution the epidemic along with the lessons learned so far will improve the efficiency and effectiveness of efforts to limit and ultimately eradicate the disease.</div></div>","PeriodicalId":48593,"journal":{"name":"Microbial Risk Analysis","volume":"27 ","pages":"Article 100329"},"PeriodicalIF":3.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}