The COVID-19 pandemic has drastically affected human lives and the global economy. In particular, cruise ship companies around the world are among the worst hit industries, with ships becoming a hotbed of viral infection owing to their confined environment. With the economy slowly recovering in the post COVID-19 period, ship companies hope to return to normal operations by adopting a sustainable management model that prioritizes the health of ship passengers.
However, the close-quartered environment in ships pose a significant challenge for virus containment. Tracking and physical isolation of infected passengers remains the standard protocol for preventing the spread of virus. Unfortunately, an effective identification of individuals in close contacts, who have potentially been exposed to the virus and can spread it, remains challenging.
CCIA utilizes a statistical method, called"Kernel Density Estimation," to calculate the probability density of each user location point. This density is then used as the weight of each user location point. The center of these location points, which form a cluster, are then calculated based on each location point and its corresponding weight.
The researchers next conducted close contact tracing experiments on the HANNARA ship, a training vessel for KMOU. To their delight, they found that CCIA outperformed conventional clustering algorithms, such as Kmeans, Hierarchical, and DBSCAN, which cannot calculate the probability density of each location point.maritime industry
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