使用无监督机器学习算法监测印度各州和 ಕೇಂದ್ರಾಡಳಿತ ಪ್ರದೇಶ的COVID-19病例和疫苗接种情况.
1Department of Mathematical and Computational Sciences, National Institute of Technology Karnataka, Mangalore, 575025 India.
Annals of data science
|April 16, 2024
概括
这项研究使用集群算法来分析COVID-19大流行.
科学领域:
- 公共卫生 公共卫生
- 流行病学 流行病学
- 数据科学数据科学数据科学
背景情况:
- 由中国武汉起源的新型冠状病毒引起的COVID-19流行病迅速在全球蔓延,对印度产生了重大影响.
- 印度各州和 ಕೇಂದ್ರಾಡಳಿತ ಪ್ರದೇಶ ( ಕೇಂದ್ರಾಡಳಿತ ಪ್ರದೇಶ) 发生了数以百万计的病例和无数的死亡,突出显示了迫切需要有效的制策略.
- 印度内部的快速传播主要是由具有旅行历史和密切联系的个人推动的.
研究的目的:
- 分析印度各州和 ಕೇಂದ್ರಾಡಳಿತ ಪ್ರದೇಶ的COVID-19流行病的影响.
- 监测印度不同地区疫苗接种计划的进展情况.
- 根据大流行病的严重程度和疫苗接种状况,为有针对性的干预行动分组各州/ UT.
主要方法:
- 应用两个无监督集群算法:k-means集群和等级集群.
- 利用了COVID-19数据集,涵盖了2020年3月至2021年6月初的时间段.
- 印度各州/ ಕೇಂದ್ರಾಡಳಿತ区进行了聚类,根据流行病影响和疫苗接种计划数据分组它们.
主要成果:
- 根据他们的COVID-19流行病负担和疫苗接种进展,确定了印度各州/ ಕೇಂದ್ರಾಡಳಿತ区的不同集群.
- 提供了对不同地区不同程度的影响和反应的见解.
- 突出了疫情控制和疫苗接种活动有效性的差异.
结论:
- 该研究为决策者和医疗保健工作者提供了有价值的数据,以了解区域性流行病挑战.
- 这些发现可以为减轻COVID-19传播和改善印度疫苗接种覆盖率的有针对性的战略提供信息.
- 这些结果作为未来研究印度COVID-19流行病的关键信息资源.
关键词:
聚合集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集群集在 COVID-19 疫情中,集群集成是指集群集成.冠状病毒 冠状病毒数据分析数据分析层次化的集群化 层次化的集群化一个流行病的流行病.k-表示集群的平均值.
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