A probabilistic model to evaluate the effectiveness of main solutions to COVID-19 spreading in university buildings according to proximity and time-based consolidated criteria

University buildings are one of the most relevant closed environments in which the COVID-19 event clearly pointed out 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". While waiting for the vaccine, as for other public buildings, policy-makers' measures to limit virus outbreaks combine individual's strategies (facial masks), occupants' capacity and access control. But, up to now, no easy-to-apply tools are available for assessing the punctual effectiveness 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 scheduling, identifying the main "attraction areas" in the building (classrooms, break-areas). Scenarios are defined in terms of occupants' densities and the above-mentioned mitigation strategies. The model is calibrated on experimental data and applied to a relevant university building. Results demonstrate the model capabilities. In particular, it underlines that if such strategies are not combined, the virus spreading can be limited by only using high protection respiratory devices (i.e. FFP3) by almost every occupant. On the contrary, the combination between access control and building capacity limitation can lead to the adoption of lighter protective devices (i.e. surgical masks), thus improving the feasibility, users' comfort and favorable reception. Simplified rules to combine acceptable mask filters-occupants' density are thus provided to help stakeholders in organizing users' presences in the building during the pandemic.