Hospital-acquired infections (HAIs) are a major concern nowadays, since they entail a big threat to society and an increase in healthcare costs. AI techniques show great performance in the development of effective systems to help in their control and prevention. However, many recent studies highlight the lack of available datasets for reproducing their experiments, claiming for more trustworthy medical AI models. Realistic data simulation is a valid approach for testing these models when data is publicly unavailable or when clinical data gathering is cumbersome or impossible. Main simulators often focus on implementing compartmental epidemiological models and contact networks for validating epidemiological hypotheses. However, very little attention is paid to hospital infrastructure (e.g. hospital building, policy, shifts, etc.) which plays a key role in the infection and outbreak processes. This paper proposes a novel approach for a simulation model of HAI spread, combining agent-based patient description, spatial-temporal constraints of the hospital settings, and microorganism behavior driven by epidemiological models.