The current SARS-COVID-19 pandemic has demonstrated that airborne diseases are a significant threat to human individuals and societies. Especially during the initial phase, when the virus was unknown, several precautionary measurements were considered and introduced by governments all over the world. At that point in time, the effectiveness of the different measurements was entirely unknown and based only on assumptions. While this generally changes over time when scientists gather knowledge about the disease, the es-sential question of how high the probability of getting infected is at different places remains unanswered. This issue can best be addressed by representing dynamic human behaviour in different spatial domains. Agent-based modelling allows to computationally simulate this behaviour. Therefore, this report focuses on the combination of simulations of human be-haviour in public spaces and scientific transmission parameters of airborne diseases to con-clude site-specific infection probabilities. The extracted knowledge can be used to develop suitable measurements for specific places. Suitable and target-oriented measurements are crucial to discover the balance between enforcing the necessary restrictions and maintain-ing an active society and economy. That supports the prevention of human and economic losses. At the end of the project, the proposed model can be helpful to estimate the infected occupants. It allows to formulate a general index of transmission for the chosen application cases. In the future, indexes based on the proposed modelling framework could support decision-makers.