巴基斯坦新冠肺炎发病热点的时空分析:空间统计方法

IF 1.7 Q3 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH Spatial and Spatio-Temporal Epidemiology Pub Date : 2023-11-01 Epub Date: 2023-07-19 DOI:10.1016/j.sste.2023.100603
Nayab Arif, Shakeel Mahmood
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引用次数: 0

摘要

本文采用地理统计学方法对新冠肺炎在巴基斯坦的传播进行分析,对活跃病例的时空格局热点进行地理可视化。这项研究基于从有关政府部门收集的二手数据。采用Getis-Ord-Gi*统计模型估计代表各部位活动病例强度的Z评分和P评分值。结果表明:疫区高发病例在空间上主要分布在旁遮普省和信德省,并向西扩展;首都地区的活跃病例率也略有上升。然而,在开伯尔-普赫图赫瓦省(KP)、俾路支省、吉尔吉特-巴尔蒂斯坦(GB)和阿扎德-查谟和克什米尔,活跃病例率有所下降,但有一些波动。总的来说,这项研究突出了地理统计模型在确定任何流行病或大流行热点方面的有用性。通过了解一种疾病的热点,政策制定者可以很容易地确定其传播的原因、趋势和分布模式,从而更容易制定管理政策,以应对未来的任何大流行情况。
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Temporal and spatial analysis of COVID-19 incidence hotspots in Pakistan: A spatio-statistical approach

This research paper analyzes the spread of COVID-19 in Pakistan using geo-statistical approach to geo-visualize the spatio-temporal pattern hotspots of active cases. The study is based on secondary data, collected from concerned Government Department. Getis-Ord-Gi* statistical model was used to estimate Z score and P score values representing the intensity of active cases in each location. The results indicate that the high intensity of active cases in the selected period is spatially distributed in Punjab and Sindh provinces and extending towards the west. The capital territory also experiences a slight increase in active cases rate. However, the rate of active cases decreases in Khyber Pakhtunkhwa (KP), Balochistan, Gilgit Baltistan (GB) and Azad Jammu and Kashmir with some fluctuations. Overall, this research highlights the usefulness of geo-statistical modeling for identifying hotspots of any epidemic or pandemic. By knowing the hotspots of a disease, policy makers can easily identify the reasons for its spread, trends, and distribution patterns, making it easier to develop management policies to tackle any pandemic situation in the future.

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来源期刊
Spatial and Spatio-Temporal Epidemiology
Spatial and Spatio-Temporal Epidemiology PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH-
CiteScore
5.10
自引率
8.80%
发文量
63
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