通过基于GIS的相互连接集群的空间时间分析,追踪印度的COVID-19传播模式
Mousumi Gupta1, Arpan Sharma2, Dhruva Kumar Sharma3
1Department of Computer Applications, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Majitar, 737136, India. mousmigt@gmail.com.
Scientific reports
|January 9, 2024
概括
时空空间分析揭示了印度的COVID-19传播模式. 沿海附近的高风险区域和与铁路相关的更温暖的气候,需要针对未来的流行病政策.
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:
- 了解COVID-19传播动态对于有效的公共卫生干预至关重要.
- 时空分析为疾病传播模式提供了关键的见解.
研究的目的:
- 分析印度不同波段的COVID-19病例的空间分布和时间模式.
- 识别高风险区域并了解影响COVID-19传播的因素,以便为未来的流行病做好准备.
主要方法:
- 利用了2020年3月至2022年12月的印度医学研究委员会 (ICMR) 的数据.
- 采用空间分析技术:用于空间自相关的莫兰I指数和用于集群识别的Getis Ord Gi*统计.
- 使用ArcGIS软件绘制每月的阳性病例集群.
主要成果:
- 从2020年5月到2022年12月的COVID-19阳性病例表现出积极的空间自相关性 (莫兰的I:0.11-0.39).
- 确定了高风险区域,包括喀拉拉邦,马哈拉施特拉邦,新德里,泰米尔纳德邦和古吉拉特邦,通常靠近沿海和温暖的气候地区.
- 在COVID-19传播,印度铁路网络,气候区和地理位置之间发现了显著的相关性.
结论:
- 时空分析对于预测和管理COVID-19浪潮和未来的流行病至关重要.
- 识别高风险区域和了解影响因素可以为有针对性的公共卫生政策提供信息.
- 交通网络,气候和地理的相互联系对疾病传播产生了重大影响.
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