A close contact identification algorithm using kernel density estimation for the ship passenger health

COVID-19 has been spread globally, with ships posing a significant challenge for virus containment due to their close-quartered environments. The most effective method for preventing the spread of the virus currently involves tracking and physically isolating close contacts. In this paper, we propose the Close Contact Identification Algorithm (CCIA). The probability density of user location points may be higher in a certain spatial range such as a cabin where there are more location points. The characteristics of CCIA include using Kernel Density Estimation (KDE) to calculate the probability density of each user location point and seeking the maximum Euclidean distance between location points in each cluster for merging clusters. CCIA is capable of calculating the probability density of each location point, a feature that other clustering algorithms, such as Kmeans, Hierarchical, and DBSCAN, cannot achieve. The contribution of CCIA is using the probability density of each location point to identify close contacts in ship environments. The performance of CCIA shows more accurate clustering compared to Kmeans, Hierarchical, and DBSCAN. CCIA can effectively identify close contacts and enhance the capabilities of user devices in mitigating the spread of COVID-19 within ship environments.