In the context of contact tracking for COVID-19, data is collected on the behavior of infected persons. The data that is collected is complex and most likely not perfectly structured in an optimal temporal space database. This makes it very difficult to analyze the data and to look for spatial-temporal information and knowledge about, among other things, super spreading events, super spreaders, ... It is interesting to apply the technique of association rules to data obtained through contact tracing. That is done in this project.