The first section describes the “Extract, Transform, Load” (ETL) architecture for geospatial sensor data and resource datasets. Disparate geospatial and IoT data sources are available for monitoring and studying COVID-19 spread. The coordination of a diverse range of data requires a comprehensive communication, integration, and interoperability model. Existing IoT systems operate within silos of information, APIs, and proprietary data formats. Firstly, the proposed architecture aimed to aggregate heterogeneous and real-time COVID-19 data streams by extracting data from heterogeneous data sources. There were two types of location-based information used for the IoCT: Positioning and Proximity. GNSS-based positioning accurately (within two to five metres on average) estimates the outdoor location of a wearable device. Most proximity sensors only provide closeness information with a range of no more than five metres from a position that is usually represented by a Bluetooth beacon. Location information was integrated into a smartphone app in an edge gateway device for computation and the transference of data onto the cloud. The other data source for monitoring workplaces came from available data streams from smart camera and audio sensors. Smart cameras and audio sensors were attached to a Jetson Xavier NX development kit [49] which served as the edge computation device for deep learning (DL) computation and the IoT gateway. Various sensor data streams were transformed by data cleaning and preparation for contact tracing query and analytics. This vast amount of spatial-temporal data was then inserted into a data stream Management System (DSMS) in near real-time. After ETL, the sensor data loading modules streaming the disparate data sources into the cloud module can be developed using the OGC STA, an open geospatial IoT exchange standard. In the cloud data storage, these datasets need to be aggregated into a unified geospatial data model and encoding also known as the OGC IndoorGML hierarchy of indoor cell spaces.