Crowds in buildings open to the public can alter the occupants’ safety in different emergency conditions, including those related to a pandemic. In this sense, university buildings are one of the most relevant scenarios in which the COVID-19 event clearly pointed out the stakeholders’ needs toward safety issues, especially because of the possibility of day-to-day presences of the same users (i.e. students, teachers) and overcrowding causing long-lasting contacts with possible “infectors” in such closed environments. While waiting for the vaccine, as for other public buildings, policy-makers’ measures to limit (second) virus outbreaks combine individual’s strategies (facial masks), occupants’ capacity and access control to avoid lockdowns and ensure adequate conditions for occupants. Simulators could support effectiveness evaluations of such measures. To fill this gap, this work proposes a quick and probabilistic simulation model based on consolidated proximity and exposure-time-based rules for virus transmission (confirmed by international health organizations). The building occupancy is defined according to university schedule, identifying the main “attraction areas” in the building (classrooms, break-areas). Scenarios are defined in terms of occupants’ densities, mitigation strategies, virus-related aspects. The model is calibrated on experimental data and applied to a relevant university building. Results demonstrate the model capabilities. In the case study, occupants’ capacity limitation could support the adoption of surgical masks by users instead of FFPk masks (thus improving users’ comfort issues). Preliminary correlations to combine acceptable mask filters-occupants’ density are proposed to support stakeholders in organizing users’ presences in the building during the pandemic.