Researchers from Korea Maritime and Ocean University (KMOU) have developed a novel algorithm called the close contact identification algorithm (CCIA) to effectively identify close contacts in confined environments like ships. The algorithm calculates the Euclidean distance between user location points to track and isolate potential virus spreaders. Cruise ship companies, heavily impacted by the COVID-19 pandemic, are looking for ways to prioritize the health of passengers and return to normal operations. However, the close quarters on ships make virus containment challenging. Identifying individuals in close contact who may have been exposed to the virus remains a challenge.
CCIA utilizes a statistical method called “Kernel Density Estimation” to calculate the probability density of each user location point. The algorithm then merges clusters of location points based on the maximum Euclidean distance between their centers. The researchers conducted experiments on a training vessel and found that CCIA outperformed other clustering algorithms. The algorithm could potentially be applied to other modes of transportation and public spaces, enhancing the capabilities of user devices like smartphones in mitigating the spread of COVID-19.
The researchers hope that CCIA will contribute to halting the spread of the virus, ensuring the health and safety of passengers and bolstering public confidence in the maritime sector.