使用空间建模对COVID-19发病率及其决定因素进行空间分析:印度的一项研究
Ipsita Dutta1, Tirthankar Basu1, Arijit Das1
1Department of Geography, University of Gour Banga, Malda, West Bengal 732103, India.
概括
地理信息系统 (GIS) 的空间模型显示,人口密度,城市化和银行接入对印度的COVID-19传播有重大影响. 这些发现强调了有针对性的社会距离和流动性政策的必要性.
科学领域:
- 流行病学 流行病学
- 地理信息科学 地理信息科学
- 空间统计的空间统计.
背景情况:
- 印度于2020年1月30日报告了其第一个COVID-19病例,到6月初迅速升级至超过20万例活跃病例和5,800例死亡.
- 了解推动COVID-19传播的地区级因素对于有效的公共卫生干预至关重要.
研究的目的:
- 分析空间分布,并确定印度COVID-19病例的重要集群.
- 评估各种空间统计模型在解释COVID-19发病率变化的有效性.
- 确定影响COVID-19在当地传播的关键社会经济和人口因素.
主要方法:
- 使用了地理信息系统 (GIS),具有19个解释变量.
- 应用空间统计技术,包括空间滞后和错误模型 (SLM,SEM).
- 使用地理加权回归 (GWR) 和多尺度GWR (MGWR) 进行地方一级分析.
主要成果:
- 全球空间模型在解释COVID-19发病率方面表现不佳.
- 多尺度地理加权回归 (MGWR) 成为最适合的模型 (R2=0.75,最低AICc).
- 人口密度,城市化和获得银行设施被确定为增加COVID-19易感性的重要因素.
结论:
- MGWR提供了对影响COVID-19传播的当地层面因素的重要见解.
- 调查结果强调了针对社会距离和移动控制的有针对性的政策的重要性.
- 使用MGWR进行空间分析可以指导决策者实施有效的公共卫生战略.
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