基于k均值聚类的菲律宾拉古纳市Los Baños早期COVID-19空间流行病学分析

Jonardo R. Asor
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引用次数: 0

摘要

-本文旨在通过使用聚类算法分析COVID-19在菲律宾拉古纳市Los Baños的传播情况。使用2020年3月至2021年3月Los Baños的COVID-19病例记录作为数据集,其中包括易感病例、可能病例、确诊病例、康复病例和死亡病例。根据数据挖掘中的聚类技术,建立模型进一步分析COVID-19的模式。本研究中使用了三种著名的聚类算法,分别是;K-means, k - medium和mean shift。此外,在本研究中,使用GeoPandas对聚类数据进行空间分析,并使用Dunn指数和欧几里得距离树形图等聚类评价指标来检验聚类能力。通过使用Dunn指数,研究确定了K-Means是一种有效的COVID-19病例聚类方法。因此,本文表明,barangay Tuntungin Putho, Mayondon, San Antonio和Batong Malake形成了一种关系。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Spatial Epidemiological Analysis of Early COVID-19 in the Municipality of Los Baños, Laguna, Philippines using K-means Clustering
—This paper aims to analyze the spread of COVID-19 in the municipality of Los Baños, Laguna in the Philippines through the use of clustering algorithms. The record of the COVID-19 cases in Los Baños from March 2020 up-to March 2021 was used as dataset which includes susceptible, probable, confirmed, recovered and death cases. Following the clustering technique in data mining, a model was created to further analyzed the patterns of COVID-19. Three famous clustering algorithms were used in this study namely; K-means, K-medoids and mean shift. Furthermore, GeoPandas was used in this study for spatial analysis using cluster data while evaluation metrics for clustering such as Dunn index and Euclidean distance dendrogram were used to inspect clustering capability. Through the use of Dunn index, the study had identified K-Means as an efficient clustering method for COVID-19 cases. Hence, shown in this paper that barangay Tuntungin Putho, Mayondon, San Antonio, and Batong Malake formed a relationship.
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