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一种基于核密度估计的船舶乘客健康密切接触者识别算法。

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

作者信息

Lin Qianfeng, Son Jooyoung

机构信息

Department of Computer Engineering, Korea Maritime and Ocean University, 727 Taejong-ro, Yeongdo-Gu, Busan 49112, South Korea.

Division of Marine IT Engineering, Korea Maritime and Ocean University, 727 Taejong-ro, Yeongdo-Gu, Busan 49112, South Korea.

出版信息

J King Saud Univ Comput Inf Sci. 2023 Jun;35(6):101564. doi: 10.1016/j.jksuci.2023.101564. Epub 2023 Apr 25.

DOI:10.1016/j.jksuci.2023.101564
PMID:37152893
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10129340/
Abstract

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.

摘要

新冠病毒已在全球传播，由于船舶空间狭小，其环境给病毒防控带来了重大挑战。目前预防病毒传播的最有效方法是追踪密切接触者并对其进行实际隔离。在本文中，我们提出了密切接触者识别算法（CCIA）。在诸如客舱等有更多位置点的特定空间范围内，用户位置点的概率密度可能更高。CCIA的特点包括使用核密度估计（KDE）来计算每个用户位置点的概率密度，并在每个聚类中寻找位置点之间的最大欧几里得距离以合并聚类。CCIA能够计算每个位置点的概率密度，这是诸如Kmeans、层次聚类和DBSCAN等其他聚类算法所无法实现的功能。CCIA的贡献在于利用每个位置点的概率密度来识别船舶环境中的密切接触者。与Kmeans、层次聚类和DBSCAN相比，CCIA的性能显示出更准确的聚类效果。CCIA能够有效识别密切接触者，并增强用户设备在船舶环境中缓解新冠病毒传播的能力。

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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/05ae/10129340/ed235ea5b026/gr7_lrg.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/05ae/10129340/59329b67fc8e/gr9_lrg.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/05ae/10129340/010d19e6d315/gr12_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/05ae/10129340/2c5b0151a250/gr13_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/05ae/10129340/20700a0add28/gr14_lrg.jpg
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