A Clustering Mechanism to Identify Close Contact for the Ship Passenger Health

Abstract

COVID-19 is spreading globally, and this spread is continuous. Ships have become the leading platform for virus transmission as a means of transportation. The small space of ships makes the possibility of virus outbreaks highly increased. The current way to effectively interrupt the spread of the virus is to track close contacts and physically isolate them. Therefore, the identification of close contacts becomes critical. This paper proposes a close contact identification algorithm applicable to the ship environment. The user ID is creatively proposed as the initialized location point cluster in this algorithm. And the KDE is introduced into the clustering process of the algorithm, and the center of the cluster is calculated by using the KDE of the location points as weights. The threshold value is used as the criterion for merging the clusters. Finally, the correct cluster result is obtained. This algorithm can provide technical support for ship companies to sustainably manage ships in the post-epidemic era, thus serving the purpose of maximizing the protection of ship passengers' health.