加强对COVID-19的地理空间时间序列数据的集群检测和降噪
Sabitri Gaire1, Abeer Alsadoon1,2,3, P W C Prasad1,2
1School of Computing Mathematics and Engineering, Charles Sturt University (CSU), Wagga Wagga, Australia.
Multimedia tools and applications
|June 26, 2023
概括
这项研究通过使用ECDeNR算法改进的Poisson前空间时间分析来增强COVID-19集群检测. 新方法提高了准确性,并确定了更多的小集群,改善了早期检测和公共卫生响应.
科学领域:
- 流行病学 流行病学
- 地理空间分析是什么
- 公共卫生 公共卫生
背景情况:
- 对COVID-19病例的时空分析对于了解传播和检测新兴集群至关重要.
- 传统的Poisson前空间时间分析在准确性,集群识别和处理时间方面面临挑战,特别是对于小集群而言.
研究的目的:
- 提高美国县级COVID-19集群检测的准确性.
- 为了增强小型COVID-19集群的检测,并减少噪音数据.
- 为了减少地理空间时间序列分析的处理时间.
主要方法:
- 实施Poisson前空间时间分析,与增强的集群检测和降噪算法 (ECDeNR) 集成.
- 使用了COVID-19数据集,使用SaTScan进行性能评估.
- 对比分析侧重于准确性,处理时间,集群数量和相对风险.
主要成果:
- 拟议的系统将集群的平均数量增加7个,平均相对风险增加9.19.
- 实现了91.35%的集群检测准确度,比之前的83.32%有所改善.
- 从7.36分钟的平均处理时间减少到5.69分钟.
结论:
- ECDeNR算法显著提高了COVID-19集群检测的准确性,并更早地识别出更多的小集群.
- 改进的方法有效地减少了噪音数据和处理时间,使其更有效地监测公共卫生.
- 这种方法为监测和管理传染病爆发提供了更强大的工具.
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