Course description

The aim of the course:

Evaluation of the safety of the food chain, risk assessment and in many cases risk management all need more complex and computation-intensive analyses and methods. The general objective of the course is to acquaint the students with the main computational methods applied in the field of food chain safety, their basics, application possibilities and limitations.

 

Prerequisite courses:

Veterinary training: Food hygiene I., Epidemiology I., Veterinary medicine I.
Biology MSc: Biomathematics, Zoology

 

Teacher responsible

Ákos Bernard Józwiak DVM PhD

 

Teachers participating

Miklós Süth DVM PhD, Ákos Bernard Józwiak DVM PhD, Szilveszter Csorba , Tekla Engelhardt PhD, Zsuzsa Farkas PhD, Erika Országh

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Lectures theme

The course will not be taught in the 1st semester of 2024/2025.

 

Week	Topic
1	Introduction. Data analysis and computational science in general. Defining computational methods, general applications, timeliness, benefits and limitations.
2	Modelling basics. Problems solvable by modelling and the limitations of modelling. Linear models.
3	Modelling. Markov models, game theory.
4	Modelling. Non-linear models, complexity science, their role and importance in food chain safety.
5	Network analysis. Basics of network analysis and application possibilities in the field of food chain safety.
6	Network analysis. Microbial metabolic pathways as networks. Epidemies and foodborne incidents as networks.
7	Epidemiological modelling. Diffusion, compartment, agent-based, spatio-temporal and network models.
8	Applications: KNIME, R, Python
9	Applications: Gephi, STEM, GleamViz
10	Data mining, text mining. Basics and application possibilities. Case study: identifying emerging risks with text mining.
11	Predictive microbiology. Basics and application possibilities from industrial, policy and research perspective.
12	Traceability. Role of data and IT in traceability systems. Investigation of foodborne outbreaks with FoodChainLab. Blockchain-based traceability systems.
13	Food chain data analysis, driver analysis. Process mining, Bayesian network analysis. Case study: milk production chain automated driver analysis and alert system.
14	Decision support. Data visualization, interpretation and communication of results. Communicating limitations and uncertainties. Ethical considerations. Decision making processes.
15	The future food data scientist: challenges and new areas. Big data and artificial intelligence (AI) application possibilities and limitations.Case study: literature review with the help of AI.

Evaluation description

Written submission.