绘制疫情地图:对基于地理信息系统的Covid-19空间建模进行了审查
Mustafa Shebani Aboalyem1,2, Mohd Tahir Ismail1
1School of Mathematical Sciences, Universiti Sains Malaysia, Gelugor, Pulau Pinang, Malaysia.
Journal of public health in Africa
|January 11, 2024
概括
空间分析,包括地理加权回归和Moran I,越来越多地用于研究COVID-19流行病. 本综述评估了在2020年至2022年期间应用于区域数据系统的空间模型.
科学领域:
- 流行病学 流行病学
- 地理信息系统 (GIS) 是指地理信息系统.
- 公共卫生 公共卫生
背景情况:
- 在全球范围内,COVID-19的流行病导致了数百万人的死亡.
- 空间分析正在成为了解疾病传播和影响的关键工具.
- 之前的研究已经探索了分析流行病数据的各种方法.
研究的目的:
- 审查和评估利用区域数据系统进行COVID-19流行病分析的研究.
- 确定应用到COVID-19数据的空间模型和方法的趋势.
- 为了使不同空间模型可以用于区域流行病研究的比较.
主要方法:
- 对2020年至2022年间发表的研究进行系统审查.
- 分析重点是空间模型,数据类别,作者,程序和发现.
- 在COVID-19研究中使用的空间分析技术的分类.
主要成果:
- 空间分析在COVID-19研究中的应用显著增加.
- 地理加权回归和Moran I被确定为经常使用的空间统计工具.
- 越来越多的人倾向于使用局部空间模型而不是其他统计方法.
结论:
- 空间分析为COVID-19流行病的区域动态提供了宝贵的见解.
- 地理加权回归和Moran I是空间时间流行病评估的有效工具.
- 走向本地模式的趋势表明需要本地化公共卫生干预措施.
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