The major goal of this research is to design, implement, and evaluate an interoperable, standard-based, scalable IoT architecture for integrating the disparate Internet of COVID-19 Things (IoCT). This paper proposed an effective post COVID-19 information system for evaluating transmission risk for both people and places using disparate IoT systems, e.g., proximity-based beacons or Global Navigation Satellite System (GNSS)-based tracking, camera-based COVID-19 risky behavior detection, and contextual indoor geospatial information. A low-cost, multi-sensor, real-time IoCT was deployed that can be rapidly applied to different COVID-19 workplace reopening scenarios such as schools, office management systems, and smart cities. The proposed IoCT was employed to identify and limit the risk pattern of COVID-19 transmission especially within enclosed buildings. The risk of COVID-19 spread inside buildings from person-to-person and person-to-place interactions when taking into consideration different distances, durations, and types of activities (e.g., disinfecting activities) was modelled using the IndoorGML graph data model [19,20]. This research presents the innovative use of the Open Geospatial Consortium (OGC) [21] SensorThings Application Programming Interface (API) [22,23], as well as, the IndoorGML that uses Poincare duality to geo-reference IoT sensor observations for both 3D spaces and Node-Relation graphs in topology space. Our paper also argues that the integration of the IndoorGML and SensorThings API is critical for effective COVID-19 risk analysis and visualization. To the best of our knowledge, this paper is the first real-world implementation of the SensorThings API (STA) and IndoorGML.