Building an Indoor Digital Twin—A Use-Case for a Hospital Digital Twin to Analyze COVID-19 Transmission - Youngin Lee; Min Hyeok Choi; Yong-Soo Song; Jun-Gi Lee; Jin Young Park; Ki-Joune Li (ISPRS International Journal of Geo-Information)
As indoor space becomes more important in our daily life, the demand to build digital twins for indoor spaces is increasing accordingly. The properties of indoor spaces, however, differ from those of outdoor spaces, and we need to apply different approaches to build indoor digital twins. In our work, we propose a framework for building an indoor digital twin with a use case for hospitals in general and large hospitals in particular, which may be considered as one of the most complicated types of digital twin. One of our goals is to establish a framework for building indoor digital twins based on standards and our framework starts from OGC IndoorGML, which is a standard for indoor data models and encoding schemes for indoor spatial data. In this paper, each step of the framework is presented for the construction of an indoor hospital digital twin focusing on a use case of epidemic analysis of COVID-19 transmission in a hospital. The use case study covers the entire life cycle of the indoor spatial application from requirement analysis, data modeling, and building indoor spatial data to the development of a COVID-19 transmission analysis. Our work represents a use case for indoor digital twins based on the OGC IndoorGML standard and eventually may serve as a framework and reference for building indoor digital twins. As our work is mainly focused on the construction of hospital digital twins, the study on COVID-19 infection model itself is limited in this paper. Improvement of the infection models and validations will be the next step of our work. As HVAC (heat, ventilation, and air conditioning) was not fully considered in our use case, we also expect that it is possible to strengthen our use case by including HVAC for the analysis of airflow dynamics.