Thesis subject

Strategies to increase antibiotics compliance behaviour

Level: BSc or MSc

Research area/discipline: (Agent) Simulation Modelling

Prerequisites: Agent-based modelling of complex adaptive systems (INF-50806)

Short description:

Inappropriate or too much antibiotic use in livestock farming can lead to resistant bacteria that contribute to an alarming health risk for society, causing societal commotion and extra costs. Regulated by law, the main responsibility for antibiotics use lies since 2013 with farmer and veterinarian. Current compliance strategies do not take into account that the farmers and veterinarians are a heterogeneous group with many individual differences, who make autonomous decisions, but who are also influenced by each other’s decisions.

Thesis project: compare populations with a fixed strategy Using agent-based modelling, a thesis project can be to vary (social) attributes of the population of farmers and/or veterinarians. These properties can be: farm characteristics; management style and attitudes; personal characteristics (e.g. inclination to be compliant; status; power).

Thesis project: compare farmers’ decision behaviour Using agent-based modelling, a thesis project can be to compare farmers’ decision-making behaviour with respect to compliance. Two extremes are (1) the rationalist, taking his own decision, not influenced by others, and (2) the imitator, who bases his decision exclusively on what others do. Most farmers will be somewhere in between.

Thesis project: compare veterinarians’ attitudes Using agent-based modelling, a thesis project can be to compare veterinarians’ attitudes with respect to compliance. Vets can only prescribe anti-biotics medication to farmers if they deem this appropriate, and they also need to register their antibiotics prescriptions. Some vets may be more inclined to prescribe than others.

Thesis project: sensitivity analysis In conjunction / following one of the other projects: once a model is ready, it takes a lot of time to run it and test how various parameters respond to each other; how sensible the model is to changes; how to present meaningful output from all these runs.

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