One Health Consensus Report Annotation Checklist (OH-CRAC): A cross-sector checklist to support harmonized annotation of surveillance data in reports.

To facilitate cross-sector integration of surveillance data it is necessary to improve and harmonize the meta-information provided in surveillance data reports. Cross-sector integration of surveillance results in sector-specific reports is frequently difficult as reports with a focus on a single sector often lack aspects of the relevant meta-information necessary to clarify the surveillance context. Such reporting deficiencies reduce the value of surveillance reports to the One Health community. The One Health Consensus Report Annotation Checklist (OH-CRAC), described in this paper along with potential application scenarios, was developed to improve the current practice of annotating data presented in surveillance data reports. It aims to provide guidance to researchers and reporting officers on what meta-information should be collected and provided to improve the completeness and transparency of surveillance data reports. The OH-CRAC can be adopted by all One Health-related sectors and due to its cross-sector design, it supports the mutual mapping of surveillance meta-information from sector-specific surveillance reports on federal, national and international levels. To facilitate the checklist completion, OH-CRAC is also available as an online resource that allows the collection of surveillance meta-information in an easy and user-friendly manner. Completed OH-CRAC checklists can be attached as annexes to the corresponding surveillance data reports or even to individual data files regardless of the data source. In this way, reports and data become better interpretable, usable and comparable to information from other sectors, improving their value for all surveillance actors and providing a better foundation for advice to risk managers.

Reprint of: Survival of Trichinella spiralis in cured meat products.

Processing of meat is one possible approach to control meat-borne parasites. Processing methods such as freezing, cooking and irradiation are recommended for the control of Trichinella in pork, horse or game meat if specific technical conditions are fulfilled. Curing is a widely used preservation process influencing product characteristics such as shelf life, food safety, and taste. As curing methods are characterized by high parameter variability and predictions about inactivation of parasitic stages in raw meat products are difficult, curing and smoking are not recommended for Trichinella control. The objective of this study was to investigate the survival of T. spiralis in cured raw sausages taking into account water activity (aw-value), pH value, temperature, and time. For this purpose, four different types of sausage (Knackwurst, vacuum packed Knackwurst, short ripened salami, long ripened salami) were produced using T. spiralis infested batter. After production, the sausages were stored at product specific conditions for up to 35 days. During storage, pH value and aw-value of the sausages were monitored over time. Further, sausages of each type were digested using the magnetic stirrer method and the viability of the isolated larvae was assessed using a previously published larval motility test as a proxy for viability and infectivity of Trichinella larvae. In this context, we also introduce a three-level rated infectivity score (RIS) with a clear categorization scheme allowing the assessment of the infectivity of larvae. Based on the RIS, larvae isolated from the salamis were regarded as potentially infective until day 2 (short ripened salami) or day 3 (long ripened salami) post ripening, whereas in Knackwurst, potentially infective larvae were still found by day 8 post ripening. In contrast potentially infective larvae were detected in vacuum-packed Knackwurst until day 24 post ripening. Finally, using the RIS approach, data from previously published studies were collected and subjected to a correlation analysis to identify matrix factors linked to short Trichinella inactivation times.

A one health glossary to support communication and information exchange between the human health, animal health and food safety sectors.

Collaboration across sectors, disciplines and countries is a key concept to achieve the overarching One Health (OH) objective for better human, animal and environmental health. Differences in terminology and interpretation of terms are still a significant hurdle for cross-sectoral information exchange and collaboration within the area of OH including One Health Surveillance (OHS). The development of the here described glossary is a collaborative effort of three projects funded within the One Health European Joint Programme (OHEJP). We describe the infrastructure of the OHEJP Glossary, as well as the methodology to create such a cross-sectoral web resource in a collaborative manner. The new OHEJP Glossary allows OH actors to identify terms with different or shared interpretation across sectors. Being aware of such differences in terminology will help overcome communication hurdles in the future and consequently support collaboration and a more inclusive development of OHS. The OHEJP Glossary was implemented as a web-based, user-friendly and searchable infrastructure that complies with the Findable, Accessible, Interoperable, Reusable (FAIR) data principles. Maintenance, enrichment and quality control of the OHEJP Glossary is supported through a flexible and updatable curation infrastructure. This increases the uptake potential and exploitation of the OHEJP Glossary by other OH initiatives or tools and services.

One Health Surveillance Codex: promoting the adoption of One Health solutions within and across European countries.

Cross-sector communication, collaboration and knowledge exchange are still significant challenges for practical adoption of the One Health paradigm. To address these needs the "One Health Surveillance Codex" (OHS Codex) was established to provide a framework for the One Health community to continuously share practical solutions (e.g. tools, technical resources, guidance documents and experiences) applicable for national and international stakeholders from different One Health Surveillance sectors. Currently, the OHS Codex provides a number of resources that support the adoption of the OH paradigm in areas linked to the harmonization and interpretation of surveillance data. The OHS Codex framework comprises four high-level "action" principles, which respectively support collaboration, knowledge exchange, data interoperability, and dissemination. These principles match well with priority areas identified in the "Tripartite Guide to Addressing Zoonotic Diseases in Countries" published by WHO, FAO and OIE. Within each of the four principles, the OHS Codex provides a collection of useful resources as well as pointers to success stories for the application of these resources. As the OHS Codex is designed as an open community framework, it will continuously evolve and adapt to the needs of the OH community in the future.

Minimum Information Required to Annotate Food Safety Risk Assessment Models (MIRARAM).

In the last decades, mathematical models and model-based simulations became important elements not only in the area of risk assessment concerning microbiological and chemical hazards but also in modelling biological phenomena in general. Unfortunately, many of the developed models are published in non-standardized ways, which hinders efficient exchange, re-use and continuous improvement of models within the risk assessment domain. The establishment of guidelines for model annotation is an important pre-condition to overcome these obstacles. Additionally, implementation of annotation guidelines can improve transparency, quality control and even aid the clarification of intellectual property rights. Here, we address the question of "What is the minimum set of metadata that should be provided for a model in the risk assessment domain?". The proposed guideline focuses on food safety risk assessment models and is called "Minimum Information Required to Annotate food safety Risk Assessment Models (MIRARAM)". MIRARAM supports the model creator during the model documentation step and could also be used as a checklist by scientific journal editors or database curators. Software developers could take up MIRARAM and develop easy-to-use software tools or new features in existing programs that can help model creators to provide proposed model annotations in harmonized file formats. Based on experiences from similar guidelines in related scientific disciplines (like systems biology), it is expected that MIRARAM could contribute to the promotion of application and re-use of models as well as to implementing more standardized quality control in the food safety modelling domain.

Survival of Trichinella spiralis in cured meat products.

Processing of meat is one possible approach to control meat-borne parasites. Processing methods such as freezing, cooking and irradiation are recommended for the control of Trichinella in pork, horse or game meat if specific technical conditions are fulfilled. Curing is a widely used preservation process influencing product characteristics such as shelf life, food safety, and taste. As curing methods are characterized by high parameter variability and predictions about inactivation of parasitic stages in raw meat products are difficult, curing and smoking are not recommended for Trichinella control. The objective of this study was to investigate the survival of T. spiralis in cured raw sausages taking into account water activity (aw-value), pH value, temperature, and time. For this purpose, four different types of sausage (Knackwurst, vacuum packed Knackwurst, short ripened salami, long ripened salami) were produced using T. spiralis infested batter. After production, the sausages were stored at product specific conditions for up to 35 days. During storage, pH value and aw-value of the sausages were monitored over time. Further, sausages of each type were digested using the magnetic stirrer method and the viability of the isolated larvae was assessed using a previously published larval motility test as a proxy for viability and infectivity of Trichinella larvae. In this context, we also introduce a three-level rated infectivity score (RIS) with a clear categorization scheme allowing the assessment of the infectivity of larvae. Based on the RIS, larvae isolated from the salamis were regarded as potentially infective until day 2 (short ripened salami) or day 3 (long ripened salami) post ripening, whereas in Knackwurst, potentially infective larvae were still found by day 8 post ripening. In contrast potentially infective larvae were detected in vacuum-packed Knackwurst until day 24 post ripening. Finally, using the RIS approach, data from previously published studies were collected and subjected to a correlation analysis to identify matrix factors linked to short Trichinella inactivation times.

Temperature-Dependent Growth Characteristics of Bacillus thuringiensis in a Ratatouille Food Model.

ABSTRACT: In contrast to Bacillus cereus, the role of Bacillus thuringiensis in foodborne illness has been controversially discussed. As B. thuringiensis-based biopesticides containing a mixture of crystal toxins and viable spores are widely used, a current European Food Safety Authority opinion underlines the need for additional data to enable risk assessment. However, it is currently poorly understood if B. thuringiensis is able to multiply in food, which is crucial to sound risk assessment. Therefore, the aim of this study was to investigate growth of selected B. thuringiensis strains from food and insecticides in a ratatouille food model. To this end, the growth parameters of three B. thuringiensis strains were determined: insecticide strain ABTS-351 (CH_119, B. thuringiensis serovar kurstaki), insecticide strain ABTS-1857 (CH_121, B. thuringiensis serovar aizawai), and CH_48 (wild-type B. thuringiensis isolated from rosemary), producing extremely high levels of enterotoxins. After an initial drop in colony counts, we observed a statistically significant growth for the tested B. thuringiensis strains between 6 and 24 h at 22, 30, and 37°C, conditions mimicking prolonged holding times. We were also able to show that the enterotoxin overproducer CH_48 can grow up to 108 CFU/g in the ratatouille matrix within 24 h at 37°C. The two midlevel enterotoxin formers ABTS-351 (CH_119) and ABTS-1857 (CH_121) isolated from biopesticides exhibited growth between 6 and 24 h, with one of the strains growing to 107 CFU/g. To our knowledge, this is the first study providing evidence of B. thuringiensis growth in a food model with intact competitive flora. Our findings suggest strain-specific variation and stress the complexity of assessing the risk related to B. thuringiensis in food, indicating that some strains can represent a risk to consumer health when vegetable-based foods are stored under conditions of prolonged temperature abuse.

STEM: An Open Source Tool for Disease Modeling.

The Spatiotemporal Epidemiologic Modeler (STEM) is an open source software project supported by the Eclipse Foundation and used by a global community of researchers and public health officials working to track and, when possible, control outbreaks of infectious disease in human and animal populations. STEM is not a model or a tool designed for a specific disease; it is a flexible, modular framework supporting exchange and integration of community models, reusable plug-in components, and denominator data, available to researchers worldwide at www.eclipse.org/stem. A review of multiple projects illustrates its capabilities. STEM has been used to study variations in transmission of seasonal influenza in Israel by strains; evaluate social distancing measures taken to curb the H1N1 epidemic in Mexico City; study measles outbreaks in part of London and inform local policy on immunization; and gain insights into H7N9 avian influenza transmission in China. A multistrain dengue fever model explored the roles of the mosquito vector, cross-strain immunity, and antibody response in the frequency of dengue outbreaks. STEM has also been used to study the impact of variations in climate on malaria incidence. During the Ebola epidemic, a weekly conference call supported the global modeling community; subsequent work modeled the impact of behavioral change and tested disease reintroduction via animal reservoirs. Work in Germany tracked salmonella in pork from farm to fork; and a recent doctoral dissertation used the air travel feature to compare the potential threats posed by weaponizing infectious diseases. Current projects include work in Great Britain to evaluate control strategies for parasitic disease in sheep, and in Germany and Hungary, to validate the model and inform policy decisions for African swine fever. STEM Version 4.0.0, released in early 2019, includes tools used in these projects and updates technical aspects of the framework to ease its use and re-use.

Application of data science in risk assessment and early warning.

The food supply chain has been recognised by the EU as a critical infrastructure, and its complexity is the main cause of vulnerability. Depending on the food matrix, natural and/or deliberate contamination, food-borne diseases or even food fraud incidents may occur worldwide. Consequently, robust predictive models and/or software tools are needed to support decision-making and mitigating risks in an efficient and timely manner. In this frame, the fellow participated in data collection and analysis tasks, so as to provide additional predictive models. The working programme, covered a wide range of aspects related to risk assessment including identification of emerging risks (quantitative), microbiological risk assessment, authenticity assessment, spatio-temporal epidemiological modelling and database formation for hosting predictive microbial models. The training and close integration, in the open-source, in-house (German Federal Institute for Risk Assessment (BfR)) developed software tools under the framework of FoodRisk-Labs (https://foodrisklabs.bfr.bund.de.) for data analysis, predictive microbiology, quantitative microbiological risk assessment and automatic data retrieval purposes allowed for the independent use. Moreover, the fellow actively contributed to the update of the upcoming Yersinia enterocolitica risk assessment, and also in authenticity assessment of edible oils. Over the course of the year, the fellow was closely involved in international and national research projects with experts in the above-mentioned disciplines. Lastly, he consolidated his acquired knowledge by presenting his scientific work to conferences, and BfR-internal meetings.

Application of data science in risk assessment and early warning.

The currently applied approaches, procedures and tools used for the identification of emerging risks vary greatly among Member States of the EU. EFSA established a structured approach for emerging risk identification that mainly consists of systematically searching, collecting, collating and analysing information and data. In addition, EFSA concluded that new methodologies and tools are needed to facilitate efficient and transparent sharing of data, knowledge and methods in the field of emerging risk identification between Member States. As the result of an open call issued by EFSA, the 'Determination and metrics of emerging risks' (DEMETER) project was established in spring 2017 to support current and future procedures for identification of emerging risks. As the Bundesinstitut für Risikobewertung (BfR) hosting site is involved in the DEMETER project, as well as in several other software development activities in the area of quantitative microbiological risk assessment, the fellow had the opportunity to play an active role in the project work and development of the running DEMETER project. The training and close integration in the project team enabled the fellow to make significant contributions, e.g. with the creation of new open source data processing workflows and by contributing to the Emerging Risk Knowledge Exchange Platform (ERKEP) Framework Concept Note. Besides DEMETER, the fellow participated in other activities of the Unit for Food Technologies, Supply Chains and Food Defence, including testing and applying several BfR open source software tools which had been developed in previous projects and that are used in microbiological risk assessment (e.g. Predictive Microbial Modelling Lab (PMM-Lab)) or as automatic data retrieval systems (e.g. SiLeBAT NewsRadar) - see https://foodrisklabs.bfr.bund.de.

Thermal Stability of Hepatitis E Virus as Estimated by a Cell Culture Method.

UNLABELLED: Hepatitis E virus (HEV) is an increasingly recognized zoonotic pathogen. Transmission is suspected to occur from infected pigs or wild boars to humans through direct contact, environmental pathways, or contaminated food. However, the physical and chemical stability of HEV is largely unknown, because suitable cell culture methods for infectivity measurement are missing. Here, we developed a titration method using infection of the cell line A549/D3 with HEV genotype 3 strain 47832c and subsequent counting of focus-forming units by immunofluorescence, which allowed HEV infectivity measurements within a 4-log-dilution range. Long-term storage of HEV in cell culture medium at different temperatures indicated a phase of rapid virus inactivation, followed by a slower progression of virus inactivation. Infective HEV was detected up to 21 days at 37°C, up to 28 days at room temperature, and until the end of the experiment (56 days) with a 2.7-log decrease of infectious virus at 4°C. Heat treatment for 1 min resulted in moderate decreases of infectivity up to 60°C, 2- to 3.5-log decreases between 65°C and 75°C, and no remaining virus was detected at temperatures of ≥80°C. Heating for 70°C resulted in a 3.6-log decrease after 1.5 min and the absence of detectable virus (>3.9-log decrease) after 2 min. The data were used to calculate predictive heat inactivation models for HEV. The results may help estimate HEV stability in the environment or food. The established method may be used to study other aspects of HEV stability in the future. IMPORTANCE: In this study, a cell culture method was developed which allows the measurement of hepatitis E virus (HEV) infectivity. Using this system, the stability of HEV at different time-temperature combinations was assessed, and a predictive model was established. The obtained data may help estimate HEV stability in the environment or food, thus enabling an assessment of the relative risks of HEV infection through distinct routes and by distinct types of food in the future.

FoodChain-Lab: A Trace-Back and Trace-Forward Tool Developed and Applied during Food-Borne Disease Outbreak Investigations in Germany and Europe.

FoodChain-Lab is modular open-source software for trace-back and trace-forward analysis in food-borne disease outbreak investigations. Development of FoodChain-Lab has been driven by a need for appropriate software in several food-related outbreaks in Germany since 2011. The software allows integrated data management, data linkage, enrichment and visualization as well as interactive supply chain analyses. Identification of possible outbreak sources or vehicles is facilitated by calculation of tracing scores for food-handling stations (companies or persons) and food products under investigation. The software also supports consideration of station-specific cross-contamination, analysis of geographical relationships, and topological clustering of the tracing network structure. FoodChain-Lab has been applied successfully in previous outbreak investigations, for example during the 2011 EHEC outbreak and the 2013/14 European hepatitis A outbreak. The software is most useful in complex, multi-area outbreak investigations where epidemiological evidence may be insufficient to discriminate between multiple implicated food products. The automated analysis and visualization components would be of greater value if trading information on food ingredients and compound products was more easily available.

Towards a Food Safety Knowledge Base Applicable in Crisis Situations and Beyond.

In case of contamination in the food chain, fast action is required in order to reduce the numbers of affected people. In such situations, being able to predict the fate of agents in foods would help risk assessors and decision makers in assessing the potential effects of a specific contamination event and thus enable them to deduce the appropriate mitigation measures. One efficient strategy supporting this is using model based simulations. However, application in crisis situations requires ready-to-use and easy-to-adapt models to be available from the so-called food safety knowledge bases. Here, we illustrate this concept and its benefits by applying the modular open source software tools PMM-Lab and FoodProcess-Lab. As a fictitious sample scenario, an intentional ricin contamination at a beef salami production facility was modelled. Predictive models describing the inactivation of ricin were reviewed, relevant models were implemented with PMM-Lab, and simulations on residual toxin amounts in the final product were performed with FoodProcess-Lab. Due to the generic and modular modelling concept implemented in these tools, they can be applied to simulate virtually any food safety contamination scenario. Apart from the application in crisis situations, the food safety knowledge base concept will also be useful in food quality and safety investigations.

A likelihood-based approach to identifying contaminated food products using sales data: performance and challenges.

Foodborne disease outbreaks of recent years demonstrate that due to increasingly interconnected supply chains these type of crisis situations have the potential to affect thousands of people, leading to significant healthcare costs, loss of revenue for food companies, and--in the worst cases--death. When a disease outbreak is detected, identifying the contaminated food quickly is vital to minimize suffering and limit economic losses. Here we present a likelihood-based approach that has the potential to accelerate the time needed to identify possibly contaminated food products, which is based on exploitation of food products sales data and the distribution of foodborne illness case reports. Using a real world food sales data set and artificially generated outbreak scenarios, we show that this method performs very well for contamination scenarios originating from a single "guilty" food product. As it is neither always possible nor necessary to identify the single offending product, the method has been extended such that it can be used as a binary classifier. With this extension it is possible to generate a set of potentially "guilty" products that contains the real outbreak source with very high accuracy. Furthermore we explore the patterns of food distributions that lead to "hard-to-identify" foods, the possibility of identifying these food groups a priori, and the extent to which the likelihood-based method can be used to quantify uncertainty. We find that high spatial correlation of sales data between products may be a useful indicator for "hard-to-identify" products.

A generic open-source software framework supporting scenario simulations in bioterrorist crises.

Since the 2001 anthrax attack in the United States, awareness of threats originating from bioterrorism has grown. This led internationally to increased research efforts to improve knowledge of and approaches to protecting human and animal populations against the threat from such attacks. A collaborative effort in this context is the extension of the open-source Spatiotemporal Epidemiological Modeler (STEM) simulation and modeling software for agro- or bioterrorist crisis scenarios. STEM, originally designed to enable community-driven public health disease models and simulations, was extended with new features that enable integration of proprietary data as well as visualization of agent spread along supply and production chains. STEM now provides a fully developed open-source software infrastructure supporting critical modeling tasks such as ad hoc model generation, parameter estimation, simulation of scenario evolution, estimation of effects of mitigation or management measures, and documentation. This open-source software resource can be used free of charge. Additionally, STEM provides critical features like built-in worldwide data on administrative boundaries, transportation networks, or environmental conditions (eg, rainfall, temperature, elevation, vegetation). Users can easily combine their own confidential data with built-in public data to create customized models of desired resolution. STEM also supports collaborative and joint efforts in crisis situations by extended import and export functionalities. In this article we demonstrate specifically those new software features implemented to accomplish STEM application in agro- or bioterrorist crisis scenarios.

Development of a comparative risk ranking system for agents posing a bioterrorism threat to human or animal populations.

Various systems for prioritizing biological agents with respect to their applicability as biological weapons are available, ranging from qualitative to (semi)quantitative approaches. This research aimed at generating a generic risk ranking system applicable to human and animal pathogenic agents based on scientific information. Criteria were evaluated and clustered to create a criteria list. Considering availability of data, a number of 28 criteria separated by content were identified that can be classified in 11 thematic areas or categories. Relevant categories contributing to probability were historical aspects, accessibility, production efforts, and possible paths for dispersion. Categories associated with impact are dealing with containment measures, availability of diagnostics, preventive and treatment measures in human and animal populations, impact on society, human and veterinary public health, and economic and ecological consequences. To allow data-based scoring, each criterion was described by at least 1 measure that allows the assignment of values. These values constitute quantities, ranges, or facts that are as explicit and precise as possible. The consideration of minimum and maximum values that can occur due to natural variations and that are often described in the literature led to the development of minimum and maximum criteria and consequently category scores. Missing or incomplete data, and uncertainty resulting therefrom, were integrated into the scheme via a cautious (but not overcautious) approach. The visualization technique that was used allows the description and illustration of uncertainty on the level of probability and impact. The developed risk ranking system was evaluated by assessing the risk originating from the bioterrorism threat of the animal pathogen bluetongue virus, the human pathogen Enterohemorrhagic Escherichia coli O157:H7, the zoonotic Bacillus anthracis, and Botulinum neurotoxin.

Thermal stability of hepatitis E virus assessed by a molecular biological approach.

BACKGROUND: Hepatitis E virus (HEV) is a pathogen of emerging concern in industrialized countries. The consumption of wild boar meat has been identified as one risk factor for autochthonous HEV infections. Only limited information is available about thermal stability of HEV, mainly due to the lack of rapid and efficient cell culture systems for measurement of HEV infectivity. METHODS: A molecular biological method was implemented in order to distinguish disassembled from intact viral particles using RNase treatment followed by quantitative real-time RT-PCR. The method was applied to a wild boar liver suspension containing HEV genotype 3. RESULTS: Time-course analyses indicated that the decline of protected RNA could be described by a biphasic model with an initial decrease followed by a stationary phase. The stationary phase was reached after 1 hour at 4°C, 3 days at 22°C and 7 days at 37°C with log reductions of 0.34, 0.45 and 1.24, respectively. Protected RNA was detectable until the end of the experiments at day 50 or 70. Heat exposure for 1 minute resulted in a log reduction of 0.48 at 70°C and increased with higher temperatures to 3.67 at 95°C. Although HEV infectivity titration by inoculation of the liver suspension onto three cell lines did not succeed, the results of the RNase-based method are in accordance with published cell culture-based data. CONCLUSIONS: Measurement of intact viral particles using the RNase-based method may provide data on the stability of RNA viruses when cell culture-based infectivity titrations are not efficient or not available. The method enables processing of large sample numbers and may be suitable to estimate stability of HEV in different types of food.

Studies on the effect of an Enterococcus faecium probiotic on T cell populations in peripheral blood and intestinal epithelium and on the susceptibility to Salmonella during a challenge infection with Salmonella Typhimurium in piglets.

Although Enterococcus faecium is used as a probiotic feed supplement in animal production, feeding of the bacterium to piglets resulted in a more severe infection with Salmonella Typhimurium DT104 during a challenge experiment. To enlighten the mode of action by which E. faecium affected the piglets' health, we investigated the influence of the probiotic bacterium on the development of intestinal and circulating immune cells during a challenge experiment with S. Typhimurium DT104. To minimise varying impacts of the maternal immunity on the course of infection, only piglets were implemented that descended from Salmonella-free sows. In addition, the potency of purified blood and intraepithelial immune cells to control the growth of Salmonella was tested in vitro. In animals treated with E. faecium, a reduction of intraepithelial CD8alphabeta T cells, reduced circulating CD8alphabeta T cells and a less efficient control of intracellular Salmonella growth, mediated by peripheral blood mononuclear cells, were observed.

Survival of Brucella spp. in mineral water, milk and yogurt.

Knowledge of the number of organisms in a food product at the time of consumption is crucial to assess the risk from a deliberate contamination of food samples with Brucella. To date, very little data on the survival times of Brucella in different food matrices is available. This study was conducted to assess the survival times of Brucella spp. in water, milk and yogurt. These food products were inoculated with bacteria, serial dilutions of the food samples plated and the number of surviving bacteria counted. Under normal storage conditions Brucella survived in UHT milk for 87 days, for 60 days in water and less than a week in yogurt. Also, when milk was inoculated with low bacterial numbers, Brucella multiplied by five log units within three weeks. Further we could not confirm that a high fat content in food has a protective effect on Brucella survival. Brucella survived in 3.5% and 10.0% fat yogurt for four and two days, respectively. These results show that appropriate methods for the rapid detection of this pathogen from food matrices are required.