了解葡萄牙COVID-19发病率的时空模式:从2020年8月到2022年3月的功能数据分析
Manuel Ribeiro1, Leonardo Azevedo1, André Peralta Santos2,3
1CERENA, DER, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal.
PloS one
|February 1, 2024
概括
这项研究引入了一种新的统计方法来分析葡萄牙的COVID-19发病率模式. 它揭示了不同的区域和季节性疾病动态,有助于公共卫生战略.
科学领域:
- 流行病学 流行病学
- 统计建模 统计建模
- 公共卫生 公共卫生
背景情况:
- 随着COVID-19的流行,就病例发生率产生了大量的数据集.
- 现有的方法缺乏全面的方法来描述时间空间疾病动态.
- 了解这些模式对于有效的公共卫生干预和风险沟通至关重要.
研究的目的:
- 开发和应用一个探索性统计工具来分析葡萄牙大陆COVID-19发病率的时空模式.
- 识别和分类随着时间的推移在各市区疾病动态的不同模式.
- 增强对改善公共卫生战略和政策评估的理解.
主要方法:
- 利用功能数据分析与无监督学习算法相结合.
- 从2020年8月到2022年3月,分析了市级每日确诊的COVID-19病例.
- 采用功能主要组件分析和对时空数据的等级聚类.
主要成果:
- 在北部/沿海地区和南部/内陆地区之间发现了COVID-19动态的显著差异.
- 2020-2021年和2021-2022年秋冬季季节之间的不同疾病模式.
- 证明了该方法能够检测关键的时空疾病发病率模式.
结论:
- 拟议的统计方法有效地提取了疾病发病率的有意义的时空模式.
- 调查结果为葡萄牙的公共卫生当局提供了宝贵的见解.
- 这种新的方法增强了公共卫生中空间时间分析的现有工具.
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