###### Abstract
For example, in the case of people waiting for a bus at an outdoor bus station without close contact, a large number of unnecessary contacts are detected when determining the contact status by GAEN. These two problems limit the global application of GAEN. The Ministry of Health and Welfare of South Korea also developed a GAEN-based app, but it was not used for practical applications because of concerns that the occurrence of unnecessary contacts may lead to an excessive load in the process of infectious disease management. Exposure status should be determined according to the transmission characteristics of each type of virus, instead of simply detecting the distance between people. The differences in the transmission patterns of the SARS-CoV-2 virus between indoor and outdoor environments were studied in [12]. This study reports that the transmission of COVID-19 is approximately 10 times higher in indoor environments than in outdoor environments. In addition, the US Centers for Disease Control and Prevention (CDC) announced that the spread of COVID-19 from a single patient to several customers in a restaurant in Guangzhou in January 2020 was through aerosol transmission via air conditioners [13]. These studies clearly demonstrate that the transmission patterns of the COVID-19 virus differ significantly between indoor and outdoor environments. Specifically, when individuals are in the same indoor space, there is a high probability of virus transmission, even if people maintain a considerable distance. On the other hand, when people are close to each other outdoors, the virus does not propagate easily. Hence, it is crucial to take into account the differences in transmission characteristics between indoor and outdoor environments. Health authorities have been hesitant to adopt approaches like GAEN, which primarily considers the physical distance between individuals without adequately addressing key transmission risk factors, such as co-location. Our study aims to advance the field of contact tracing by prioritizing the identification of whether individuals share the same environmental context, such as being in the same room or trajectory, which is a more relevant measure for assessing potential transmission events than mere physical distance. This approach is particularly pertinent given the heterogeneity of COVID-19 transmission rates reported in different settings. For indoor spaces, we propose space-based indoor Wi-Fi contact tracing, which uses the similarity of Wi-Fi fingerprints of access points in spacesand recognizes the co-location statuses of individuals in the same indoor space; it detects contacts based on this information. Unlike existing Wi-Fi-based contact tracing methods that rely on measuring physical distance or user location using received signal strength indicators (\(RSSI\)) [6], space-based indoor Wi-Fi contact tracing focuses on similarities in access point signals to identify co-located individuals. This is achieved through the implementation of a machine learning model. On the other hand, for outdoor spaces, we suggest trajectory-based outdoor GPS contact tracing. This method utilizes GPS data to track the movement patterns of individuals and detects potential exposure by identifying whether people were on the same trajectory and in close proximity. By analyzing the trajectories and distances, this approach enables the identification of potential contacts and exposure risks in outdoor environments. By implementing two distinct contact tracing methods--indoor space (space-based indoor Wi-Fi contact tracing) and outdoor space (trajectory-based outdoor GPS contact tracing)--we can effectively address the challenges of each environment and improve the overall contact tracing performance. We conducted experiments to validate the IntelliTrace. We obtained data from 28 experiment participants, consisting of groups in which regular contact may occur at the university.