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The reported research focuses on occupational density as an increasingly important archi tectural measure and uses occupancy simulation to optimize distancing criteria imposed by the COVID-19 pandemic. The paper addresses the following questions: How to engage computational techniques (CTs) to improve the accuracy of two existing types of indoor positioning systems? How to employ simulation methods in establishing critical occupation density to balance social distancing needs and the efficient use of resources? The larger objective and the aim of further research is to develop an autonomous system capable of establishing an accurate number of people present in a room and informing occupants if space is available according to prescribed sanitary standards. The paper presents occupancy simulation approximating input that would be provided by the outlined multisensor data fusion technique aiming to improve the accuracy of the existing indoor localization solutions. The projected capacity to capture information related to social distancing and occupants' positioning is used to ground a method for determining a room-specific occupational density threshold. Our early results indicate that the type of activities, equipment, and furniture in a room, addressed through occupants' positioning, may impact the frequency of distancing incidents. Our initial findings centered on simulation modeling indicate that data, composed of the two sets (occupant count and the number of recorded distancing incidents) can be overlapped to help establish room-specific standards rather than apply generic measures. In conclusion, we discuss the opportunities and challenges of the nrnnnsed system and its role after the nandemin.