Service Data & Statistics

The Division for Data, Statistics and Risk Assessment (DSR) is not only responsible for statistical analyses and data management within AGES, but has also been providing services to other client groups for many years.

Contact us!

The Division for Data, Statistics and Risk Assessment (DSR) is not only responsible for statistical analyses and data management within AGES, but has also been providing services to other client groups for many years.

Contact us!

Data Management

Mag. (FH) Monika Schagerl
Phone: +43 50 555-25715
Spargelfeldstraße 191
1220 Wien

Statistics

Mag. Dr. Hans Peter Stüger
Phone: +43 50 555-61401
Zinzendorfgasse 27/1
8010 Graz

Data Management

Mag. (FH) Monika Schagerl
Phone: +43 50 555-25715
Spargelfeldstraße 191
1220 Wien

Statistics

Mag. Dr. Hans Peter Stüger
Phone: +43 50 555-61401
Zinzendorfgasse 27/1
8010 Graz

The information provided here will give you an overview of our portfolio.

Questionnaire Survey

Questionnaire Survey

Questionnaire Survey

Data is also collected through the surveying of consumers, businesses, stakeholders etc., in addition to laboratory and field measurement. The DSR has broad experience in empirical social research and in the customer-oriented, technical conducting of surveys.

The selection of the appropriate survey tool and development of that tool require expert knowledge and experience. Our expertise in the development of survey tools (questionnaire surveys, in particular) in the fields of sociology, psychology and statistics include:

  • Developing and conducting empiric studies (quantitative, qualitative)
  • Clear and comprehensible wording of the research questions and the creation of suitable question formats
  • Representative sampling plans
  • Survey concepts that are oriented towards the survey objective and that consider subsequent data analysis
  • Comprehensive analysis concepts including automated reporting

Survey Tools

Paper-based surveys and personal interviews are being increasingly replaced by electronic formats. These electronic formats have the advantage of being easy to disseminate and quick in terms of data analysis. We offer the following professional services for the conducting of empirical surveys:

  • EXCEL questionnaires that are user-friendly and ensure input security
  • Web-based surveys using online survey tools, making anonymous surveys possible

References

Food Safety and Hygiene in the Domestic Environment 

  • Lebensmittelsicherheit und Hygiene im Privathaushalt - (full report)
  • Aldrian U., Hofstädter D., Hölzl C., Fuchs K.: Food Safety and hygiene in the domestic environment – A representative telephone survey of food safety and hygiene practices and microbiological knowledge in Austria. Die Ernährung – Österreichische Zeitschrift für Wissenschaft, Recht, Technik und Wirtschaft, Vol. 37, 11/2013, p. 422-431.
  • Hölzl C., Mayerhofer U., Steininger M., Brüller W., Hofstädter D., Aldrian U.: Observational Trial of Safe Food Handling Behavior during Food Preparation Using the Example of Campylobacter spp. Journal of Food Protection, Vol. 76, 3/2013, p. 482-489.

Guideline School Buffet – Evaluation 

Challenge Communal Catering  - Evaluation 

  • Pichler J., Ziegler J., Aldrian U., Allerberger F.: Evaluating levels of knowledge on food safety among food handlers from restaurants and various catering businesses in Vienna, Austria 2011/2012. Food Control Vol. 35, 1/2014, p. 33-40.
  • Pichler J., Ziegler J., Aldrian U.: Erhebung des Wissens über Lebensmittelsicherheit von Küchenpersonal in Wiener Restaurants und Betrieben der Gemeinschaftsverpflegung, 2011/2012. Ernährung aktuell 1/2013, p. 8-10.

Sampling Plans/Control Systems

Sampling Plans/Control Systems

Sampling Plans/Control Systems

Data collection based on sampling plans is of extreme importance in science and business, as well as in official control systems. A well-designed sampling plan is a decisive factor in ensuring the quality of survey results. On the other hand, non-representative surveys could lead to false findings and conclusions. Thus, differentiating between representative and risk-based samples is essential in practice. Sample plans are among the DSR’s core competences, in particular for the following:

  • Sample size: as few as possible, as many as necessary – depending on the survey‘s objective and the resources available
  • Sample structure: representative mixture, through stratification, for example
  • Choosing sampling units: selection of businesses, stock, spatial units, individuals etc. via random sampling methods

 Study Planning

The proper statistical planning of scientific studies is a major factor in the success of such projects. The DSR’s services include

  • Mathematical-statistical system analysis during the conception stage: consistency of objectives, data collection, measurement processes and data analysis
  • Study-specific sampling plan and representative and efficient selection of sampling units
  • Method planning: advice on the choice of suitable statistical analysis methods

Design of Control Systems

The control and monitoring of risks is a major objective in official control systems, but also in internal control systems within individual businesses. The DSR has a wide range of application methods in the fields of agriculture, veterinary medicine, food safety and human health at its disposal.

Services

  • Conception of control systems according to representative and/or risk-based criteria 
  • Conception of surveillance systems to verify a disease-free Status 
  • Control plans taking into consideration legal prerequisites, statistical safety criteria, capacity limits and control objectives 
  • Automated target-performance comparison for monitoring control processes 
  • Development of data-based indicator systems for control results 
  • Establishing regular reporting Systems 
  • Statistical analysis of data collected: (two-tier) prevalence estimate Estimating intra-herd prevalence etc.

Reference Projects

  • Sampling plan for official food control in Austria 
  • Multi-year, integrated control plan for agriculture (foodstuff, fertilizer, plant protection products, seeds)
  • Plans for animal disease surveillance (brucella melitensis, BLI, bluetongue, scrapie, BSE, TBC etc.)
  • Control plan for national zoonosis and antibiotics resistancy Monitoring
  • Sampling plan for national pesticide Monitoring 
  • Sampling plan for residue control for animal medications and hormones 
  • Sampling plan for TGD farm auditing 
  • Comparison of sampling methods to show PRRS on a simulated pig breeding farm 
  • TB in Alpine Wildlife: EMIDA ERA-NET -- Tuberculosis in Alpine wildlife - Monitoring, diagnostics and potential control strategies of tuberculosis in wild animals in the Alpine provinces of Austria, Germany, Italy and Switzerland, 2011—2013

Referenzen:

Data Management

Data Management

Data Management

The data management unit’s tasks are very diverse and include, among other fields, master data maintenance for the in-house laboratory information system and complex data preparation for various stakeholders. Therefore, high-performance databases must be used and maintained to achieve these goals. The Division for Data, Statistics and Risk Assessment has bundled together considerable know-how in terms of database architecture, design and operation.

Data Processing and Transmission

Services

The laboratory data collected at AGES is an essential tool for health-related questions. The data gathered is, thus, transferred to a number of national and international authorities.
Professional tools are used for data selection. More often than not, the data must also be translated to meet the requirements of the recipient. This “translation” does not only refer to the language desired, but also to matching the standards needed with the value lists and codes defined. A very high degree of automation has been reached for much of the data transmission in order to carry out these sophisticated processes efficiently. This is done via the maintenance of various mapping catalogues, which enable the quick translation of data from its original format into the values accepted by the recipient.
Data transmission is done mainly in XML format. The files created are checked against the XSD Schema before transmission.eferenzen

References

Extensive projects on the standardisation of data transfer have been conducted together with the EFSA (European Food Safety Authority).a

Conception/Set Up of Databases

Services

AGES maintains an in-house environment for data retrieval. This comprises AGES internal data from its own laboratory information system and also data from external sources. The data must be provided by the source systems as files, interface tables or via a view layer to ensure transfer, so they can be uploaded, adjusted and integrated in the core. All these data flows are summarised under the term ETL (Extraction, Transformation, Loading) and managed using the appropriate tools.

References

AGES employs a highly trained and experienced team of database experts, who have many years of experience in the building and development of complex evaluation structures.

Network Analysis

Network Analysis

Network Analysis

Animal trade is characterised by intensive national and international networks. Live animal transportation is an important route of disease transmission.
Diseases can spread at different rates, depending on the structure of the network in question. The analysis of animal movement data can help optimise monitoring programs, as well as facilitate the preparation of risk management activities in the event of a crisis.
DSR can carry out the following analyses based on VIS and TRACES data:

  • Single animal movement analysis: what is the contact chain for the individual farm or a specific animal?

  • General animal movement analyses examine the entire livestock network:

    • Which businesses play an important part in the network?
    • How strong are the links in the network? Are there regional differences?
    • Are there movement patterns that have special importance in the case of a disease outbreak?

The spread of a disease is depicted in the following animation via simulation. On the left you can see the example network. The upper right part shows the progression of the proportions of susceptible, infected and immune units (e.g. farms). The lower right part visualizes the number of newly infected units per time step.

Reference projects:

  • Simulation of the spread of foot and mouth disease for the nationwide real-time simulation PICORNA 14
  • Analysis of Alpine pasture transportation for the conception of the sample plan “Indicator Alpine pastures/TB Monitoring”
  • Transportation Animal movement analysis commissioned by TGD Tyrol
  • Analysis of animal transportation movements as part of the animal disease outbreak IBR 2015 in Austria, including the evaluation of contact animals at calf auctions.
  • Risk-based sampling plans (BLI in cattle, brucella melitensis in sheep and goats)

TRACES: Berichte Österreich

Statistical Data Evaluation

Statistical Data Evaluation

Statistical Data Evaluation

The evaluation of data using a wide range of statistical methods is a core competence of the DSR Division. Tailor-made solutions for calculating statistical benchmarks and their visualisation via clear, meaningful graphics are developed according to evaluation objectives. As part of the analysis, we also consider any uncertainties arising from sampling data and measurement inaccuracies. We use R and SAS software.

The DSR’s services encompassConsultation and concept creation for data evaluation systems

  • Conducting of descriptive statistical analysis
  • Creating graphics (also for the internet)
  • Automated reporting
  • Statistical significance tests
  • Statistical modelling

The DSR uses a broad range of statistical methods.

Statistical Methods

  • Descriptive processes
  • Sampling processes
  • Statistical experiment planning
  • Methods for statistical modelling, including:
    • Generalized Linear Models (GLM, GLMM)
    • Mixed Models
    • Generalized Additive Models (GAM and GAMM)
    • Generalized Estimating Equations (GEE)
    • Latent Class Models
    • Regression Trees
    • (Multi-Factorial) ANOVA
    • Principal Component Analysis
    • Factor Analysis
    • Cluster Analysis
  • Survival analysis
  • Time series analysis
  • Non-parametric methods
  • Methods of spatial statistics (Point Pattern Analysis, Kriging,…)
  • Network analysis
  • Stochastic simulation/ Markov Chains

References

  • Elmadfa I., A. Meyer, V. Hasenegger, K. Wagner, P. Putz, N. Weidl, D. Wottawa, T. Kuen, G. Seiringer, H.P. Stüger (2013): "Using biomarkers for the assessment of nutritional status in Austria – identified critical nutrients" in Elmadfa, Ibrahim L. (Hrsg.) "Referenzwerte für die tägliche Nährstoffzufuhr" 2013. Wissenschaftliche Verlagsgesellschaft Stuttgart.
  • Follak S., Aldrian U., Schwarz M.: Spread Dynamics of Abutilon theophrasti in Central Europe. Plant Protection Science 50, p. 157-163.
  • Follak S., Aldrian U., Moser D., Essl F.: Reconstructing the invasion of Cyperus esculentus in Central Europe. Weed Research. An international Journal of Weed Biology, Ecology and Vegetation Management. DOI: 10.1111/wre.12145, S.1-9.
  • Glawischnig W., Schleicher C., Griesbacher A., Stadlmüller L., Dablander K. (2014), Ergebnisse der nationalen Ringversuche zum Nachweis von Trichinella-Larven in Schweinefleischproben in Österreich 2008-2013, Wiener Tierärztliche Monatsschrift - Veterinary Medicine Austria, 101 (2014), 68-74.
  • Köglberger H., Moosbeckhofer R., Derakhshifar I., Etter K., Mayr J., Schwarz M., Stadlmüller L. (2014). Verluste von Bienenvölkern während der Überwinterung oder bei Verdacht auf Bienenfrevel - was waren die Ursachen? (Teil 1); Bienenaktuell; 4 : 28-31; 04.
  • Köglberger H., Moosbeckhofer R., Derakhshifar I., Etter K., Mayr J., Schwarz M., Stadlmüller L. (2014). Verluste von Bienenvölkern während der Überwinterung oder bei Verdacht auf Bienenfrevel - was waren die Ursachen? (Teil 2); Bienenaktuell; 5 : 28-31; 05.
  • Schöpf K., Hebel C., Weikel J., Glawischnig W., Hofer E., Revilla-Fernandez S., Stadlmüller L., Schmoll F. (2013). Evaluation of diagnostic testing tools for bovine tuberculosis; 21/NOV/2013; Wien; Poster bei 47th Annual Meeting of the ÖGTPM.

Visualisation

Visualisation

Visualisation

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Image catalogue

Data visualisation plays an important role in the exploration of relationships and interactions, as well as for the presentation and communication of research results. During the explorative stage of the analysis, the use of appropriate visualisation techniques enables us to obtain a quick overview of large data sets, while facilitating the detection of trends, correlations, patterns and anomalies in the data. When communicating results, the choice of visualisation form has an impact on the ability to effectively convey relevant ideas and messages. A clear and undistorted visual representation is of critical importance in order to rule out misleading graphs which might support erroneous conclusions and lead to misinterpretation.

DSR provides support in finding the appropriate visualisation type from a wide range of options for the data in question. We create high-quality graphics for reports, slide shows and publications, maintaining well-established visualisation standards.
 
Below, you can find a description of some of the most common visualisation types in our service range:

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Scatter plot: scatter plots depict two metric values in the form of point clouds using Cartesian coordinates. This representation facilitates the detection of correlations (trends) and clustering.
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Bubble chart: bubble charts can be seen as a generalization of scatter plots, where an additional attribute is depicted using the area of the points (bubbles).
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Bar chart: bar charts depict the frequency distribution of categorical data and are used to compare groups.
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Time series: a time series consists of a sequence of successive data points (e.g. measurements) made over a time interval. Time series are usually displayed as line charts (see upper plot) as this facilitates the detection of developments and trends. Bar charts can be used as an alternative, when focusing on comparing individual values (see lower plot).
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Box plot: box plots display the frequency distribution of numerical variables and indicate their location, variance and skewness. The box, whiskers and individual data points depict distribution characteristics such as the median, the quartiles and outliers. Box plots are well suited for comparing groups, due to their elegant simplicity.
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Histogram: histograms display the frequency distribution of numerical variables by dividing the data into a series of intervals and depicting the count of the intervals as bars. As opposed to box plots histograms also indicate the shape (e.g. modality) of the distribution. The graphic representation, however, depends on the chosen bar width (= bin size) and possesses only limited suitability for the comparison of groups.
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Bean plot: a bean plot is a graphical method used to depicts the density of distributions together with markings for individual observations. Its compact form makes the bean plot very useful for comparing groups, while it also provides information on the shape (variability, skewness, modality) of the distributions.
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Mosaic plot: mosaic plots are the graphical representation of frequency tables in which the individual values are visualized using the area of the tiles. Such plots help to intuitively grasp relationships and dependencies between two or more categorical variables.
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Heat map: a heat map depicts data in matrix form by using different colours to encode numerical values. This visualisation form enables us to intuitively capture large amounts of data and to quickly identify prominent values.
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Correlation plot: the correlation structure of several numeric variables can efficiently be depicted using correlation plots. Pair-wise calculated correlation coefficients are encoded using colours or symbols and are depicted in matrix form, similar to a heat map.
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Parallel coordinate plots: parallel coordinates are used to visualise high-dimensional data (three or more variables are displayed simultaneously). Each parameter corresponds to an axis in the coordinate system. However, the axes do not intersect at a right angle, but run parallel to each other. As a result, each data point is represented by a line. Parallel coordinate plots enable the recognition of structures and patterns in high-dimensional data sets. However, the depiction depends on the order that the axes are arranged in.
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Radar chart: a radar chart is a graphical method of displaying multivariate data (three or more variables are displayed simultaneously) on an equi-angular coordinate system where each axis (spoke) represents a variable. For each data point, the values for each spoke are connected, resulting in a cyclical graph reminiscent of a spider’s web.
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Choropleth map: a choropleth is a map where certain areas (e.g. political/geographical regions) are patterned or shaded to represent the measurement of a statistical variable. This enables a quick overview of the geographical distribution of a variable.
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Network diagram: in some cases, the data of interest is not present in numerical form (e.g. measured values or frequencies), but in the form of structures that can be visualised. Network diagrams depict social networks using nodes (e.g. individuals, farms) and edges (e.g. interactions, links) to show how a data set is connected and to help identify important nodes and links.
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Surface plot: surface plots are often used when the value of a response variable should be visualised in relation to two explanatory variables. The value of the target parameter is depicted in the form of a three dimensional mesh, similar to a geographical relief. This helps to identify the optimal parameter combinations in an easy way.
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Contour plot: similar to a surface plot, a contour plot is used to display three-dimensional data sets. In a contour plot, the values of the target parameter are displayed in form of contour lines (isopleths) enabling us to find extreme values (minimum and maximum values) at a glance.

Risk Modelling

Risk Modelling

Risk Modelling

Risks for humans, animals and plants are not spread evenly, but are always dependent on specific influences. It is essential to identify these influences and evaluate their quantitative importance correctly when looking to pinpoint explanations and for risk management purposes. Getting a clear picture of the crucial risk factors involved is the basis for efficient, risk-based surveillance systems, the creation of an appropriate legal framework or for targeted information activities. The statistical evaluation of risk factors is based on data mainly taken from official surveillance activities, observation studies and experiments. The statistical methods that are considered the most appropriate are then used to determine the impact of potential factors on the issue in question. We use R and SAS software.

The key questions to be answered in risk modelling are

  • What are the statistically significant risk factors?
  • How do those factors affect the risk or the parameter in question at a quantitative level? 
  • What risk levels can be projected for specific population groups?
  • How much can the risk be reduced through intervention?

DSR can provide the following Services

  • Creation of a sampling or study design to collect a suitable data Basis 
  • Statistical modelling using a broad range of methods  
  • Development of forecast models allowing for measurement uncertainties and sample errors 

 Reference Projects

References

  • Flamm C., Kuchling S., Zechner E., Livaja M., Pauk J., 2013. Praktische Anwendbarkeit von Trockenstressparametern für die Weizenzüchtung; Proceedings 63. Jahrestagung der Vereinigung der Pflanzenzüchter und Saatgutkaufleute Österreichs, ISBN-13:978-3-902559-88-3, pp. 87–93
  • Fuchs K., Hofstädter D., Rauscher-Gabernig E., Lückl J., Scheriau S. (2014): „Risikolandkarte – Priorisierung von Risiken entlang der Lebensmittelkette“. Ernährung aktuell 03/2014
  • Kuchling S., Schleicher C., Raith J., Köfer J. (2015): Analyse des Einflusses von Tiergesundheitsparametern auf die durchschnittliche tägliche Zunahme von Mastschweinen. Berliner und Münchener Tierärztlichen Wochenschrift 128, Heft 5/6, 2015, 193—199.
  • Matt, M., H.P. Stüger, P. Pless. (2013) Risk Priority Number: A Measuring Instrument for Hygienic Management on Broiler Farms, Reflecting Their Campylobacter Status. Agriculture 3(4): 700–714.
  • Matt, M., Weyermair, K., 2015. “Expositionsmodell Campylobacter: Erkrankungswahrscheinlichkeit Bei Zubereitung Eines Hähnchens Mit 1.000 KBE/g in Österreichs Küchen.” Wiener Tierärztliche Monatsschrift Artikel 2, no. 1–2 (2015).
  • Much P, Sun H (2012) Streptomycin-Resistenz bei kommensalen E. coli aus Rinderdärmen im Hinblick auf die Auswirkungen der Anwendung von Streptomycin zur Bekämpfung von Feuerbrand in der Landwirtschaft. In: Resistenzbericht Österreich AURES 2011, Herausgeber: Bundesministerium für Gesundheit (BMG), ISBN Nr. 978-3-902611-61-1, 1. Auflage: November 2012. pp 340-341
  • Raith J., Scheriau S., Schleicher C., Köfer J. (2015):  Influence of porcine cicovirus type~2 vaccination on probability and severity of pneumonia detected post mortem. Veterinary Record (2015), Vol. 176, Issue 5, 124.
  • Rauscher-Gabernig E., Scheriau S., Öhlinger R., Fuchs K., 2014: Risk ranking of mycotoxins for the Austrian population based on data from official control. The World Mycotoxin Forum 2014 – the 8th Conference: Mycotoxin Control: The Systems Approach, Vienna, Austria, Poster - 10–12/11/2014.
  • Schleicher C., Scheriau S., Kopacka I., Wanda S., Hofrichter J., Köfer J. (2013): Analysis of the variation in meat inspection of pigs using variance partitioning. Preventive veterinary medicine, 111(3), 278-285.
  • Schmerold I., Sun H., Much P. (2015). AURES 2012: Antimikrobielle Resistenzen beim Indikatorkeim E. coli von Rind und Schwein. Klauentierpraxis 23, 5-10
  • Steinrigl A., Schiefer P., Schleicher C., Peinhopf W., Wodak E., Bagó Z., Schmoll F., 2014. Rapid spread and association of Schmallenberg virus with ruminant abortions and foetal death in Austria in 2012/2013. Preventive veterinary medicine, 116(4), pp. 350–359.
  • Stepanek, Walter; Marchart, Kristina; Brüller, Werner; Wögerbauer, Markus; Ribarits, Alexandra; Riediger, Klaus; Poglitsch, Martina; Kopacka, Ian; Kuffner, Melanie; Nossek, Georg; Steinwider, Johann; Bundesministerium für Gesundheit (2014); Risk Assessment of Second Generation Genetically Modified Organisms; Wien
  • Weyermair Karin, Pless Peter, Matt Monika (2014),  Statistische Modellierung von Hygienefaktoren und deren Einfluss auf den Campylobacter-Status von Broilermastbetrieben in Österreich; DACh-Epidemiologietagung—Internationale Fachtagung der DVG-Fachgruppe Epidemiologie und Dokumentation, Zürich, Poster 09/2014.
  • Wueppenhorst N., Draeger S., Stüger H.P., Hobmaier B., Vorreiter  J., Kist M., Glocker E. (2014): "Prospective multicentre study on antimicrobial resistance of Helicobacter pylori in Germany" Journal of Antimicrobial Chemotherapy, 69(11):3127-33.

Spread Modelling

Spread Modelling

Spread Modelling

The spread of human, animal and plant diseases poses potential risks. When dealing with disease outbreaks, the main goals are early detection and the prevention of outbreaks. Thus, surveillance programs and preventive measures play a crucial role for the assurance and protection of public health. Furthermore, careful strategic planning is necessary so that appropriate and effective measures can be taken in the event of a disease outbreak. In both situations, computer-assisted simulation models offer valuable tools for the assessment of spread dynamics and the effectivity of control measures. The models are typically based on a statistical risk analysis which describes the relationship between possible risk factors and disease spread. In order to account for the random nature of disease transmission events, stochastic modelling allows for random variation of model parameters. The simulation results, therefore, provide information on the range of possible outcomes and can be used in many ways, especially for the evaluation of surveillance and intervention measures.

DSR offer to provide the following services related to the field of spread modelling

  • Simulation of disease outbreaks (human, animal) 
  • Modelling the spread of plant diseases, pests and invasive plant species 
  • Visualisation of spatial and temporal disease spread 
  • Evaluation of the efficiency of risk management measures 
  • Development of preventive surveillance programs 
  • Crisis scenario oriented evaluation and reporting systems

Reference Projects

  • Simulating the spread of the grapevine disease “Flavescence dorée” and its vector Scaphoideus titanus (VitisCLIM), 2011–2013.
  • Simulation of Foot and Mouth disease in Austria for the Austrian real-time exercise PICORNA 2014.

GIS Service

GIS-Services

GIS-Services

Geospatial components play a major role in many issues, such as in the dispersion of diseases or the spatial dispersion of pollutants. Cartographical visualisation opens up new perspectives for interpretation. It assists in the advanced recognition of spatial trends to help apply the appropriate measures on a timely basis.

The GIS Service Center at AGES supports all Divisions in the processing of geographical data to achieve the maximum protection for humans, animals and plants. To do this, spatial analyses are made and maps compiled as part of publications, research projects and reports, as well as interactive maps on the AGES homepage.

AGES also offers its GIS services to external clients:

Cartographic Visualisation

Service

The data gathered is allocated specific spatial locations (geo-related) and then entered into a geo-information system together with the geo-based data. Cartographical visualisation is used to make abstract, space-related data and spatial connections that are difficult to comprehend easier to grasp for the viewer. Interactive maps can be put on the internet using WebGIS technologies. Geo-information systems can – among other things – help:

  • Compile maps of risk regions
  • Visualise space-time analyses as animations 
  • Display the results of surveillance programs in map Format 
  • Plan and refine surveillance and sampling plans
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Picture 1: Spread of the corn rootworm between 2002 and 2008, including the percentage of corn fields are per arable area.
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Picture 2: First outbreak of blue tongue with the animal density of cattle and sheep (as of 24.09.2009).

Spatial Analyses

Service

Combining the various data and geo-statistical evaluations available can assist in the search for new information and connections that can be shown in digital map format. Analyses can discover circumstances and connections that would otherwise have remained invisible. The GIS palette of services includes functions using both geometric and geo-statistic analysis on a large scale.

The spatial analysis functions include

  • Classification 
  • Distance calculations 
  • Combination 
  • Buffer building
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Picture 3: Simulation of a classical swine fever outbreak in an Austrian district with a high number of pig farms.

Reference Projects 

References

Link with additional spatial data

Service

The GIS Service Center collects and manages geo-based data. This data includes political information, such as district borders and water networks, as well as all street names including house numbers.

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Picture 4: Combination of AGES expert data with geo-based data

Reporting

Reporting

Reporting

The data processed by the laboratory information system and from external sources are prepared and made available for specific target groups, according to recipient requirements. The scope of reports ranges from standardised evaluations that can be accessed directly by the user in the analysis tool and reports sent in the desired data format on a periodical basis up to extensive publications. The Division for Data, Statistics and Risk Assessment has an up-to-date tool mix that supports this broad spectrum of analysis and evaluation options in an optimal manner.

Automated Reporting

Services

The DSR’s reporting system serves a variety of stakeholders that includes the AGES management, as well as ministries, local authorities and European agencies. The objective and purpose of the evaluation is discussed with the client to ensure the delivery of the exact information required, as well as the adaptation of reports to specific readers. Various tables, graphs and images enable a rapid grasp of the data and the deduction of trends and developments. The DSR has in-depth knowledge of the principles of scientific working methods and delivers publishable reports on request.

References

The following reports were either written by or in cooperation with the DSR (Content only available in German):

Partner in Research Projects

Partner in Research Projects

Partner in Research Projects

Services

The DSR’s expertise in statistics and data management flows into numerous research projects. On the one hand, we are partners in and support research projects carried out by our colleagues in the Divisions Food Security, Food Safety, Public Health , Animal Health, Medicine and Medical Devices and Radiation Protection. On the other, we also take on the role as project leader and coordinator and run projects at an international level. Our staff members have many years of project experience, in addition to their high level of professional competence. Thus, the planning, acquisition, leadership and conducting of projects are important elements in our work.

As partners, we can provide the following expertise for research projects:

  • Experience in planning and managing Projects 
  • Advice in survey and study design 
  • Assistance with creating databases 
  • Creating statistically sound sampling plans 
  • Comprehensive data analysis: descriptive evaluation, visualisation, statistical modelling 
  • Automated evaluation and reporting systems

References

Training & Presentations

Training & Presentations

Training & Presentations

We are keen to transfer our DSR expertise in statistics and data management and organise and host lectures and training sessions. The content is focused specifically on the needs and requirements of our clients.

We offer the following services, in particular

  • Software training for the statistical software R, SAS and SPSS 
  • Statistical assistance for questions in data analysis and survey planning 
  • Presentations

Reference Projects

Development of/collaboration in multi-day courses for

  • Croatia
  • Lebanon
  • Macedonia
  • Saudi Arabia
  • Serbia
  • Slovakia
  • Czech Republic

Contact

Contact

Data Management

Mag. (FH) Monika Schagerl
Phone: +43 50 555-25715
Spargelfeldstraße 191
1220 Wien

Statistics

Mag. Dr. Hans Peter Stüger
Phone: +43 50 555-61401
Zinzendorfgasse 27/1
8010 Graz

Data Management

Mag. (FH) Monika Schagerl
Phone: +43 50 555-25715
Spargelfeldstraße 191
1220 Wien

Statistics

Mag. Dr. Hans Peter Stüger
Phone: +43 50 555-61401
Zinzendorfgasse 27/1
8010 Graz
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