封锁、移民和印度Covid-19病例的空间分布

Z. Husain, R. Kothari
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引用次数: 1

摘要

本研究分析了印度国家一级的封锁对农民工的影响,以及它如何改变Covid-19病例的空间分布。使用空间统计方法(地图集、地方空间分析指标、Getis-Ord gi*统计和多尺度地理加权回归模型)分析公开的区级数据。2020年3月24日,印度总理宣布全国封锁,以遏制Covid-19在印度的传播。这导致成千上万的移民工人被困在他们的工作场所-暴露于Covid-19,没有任何工作或收入。他们回家的长途跋涉最初被忽视了;直到5月,印度铁路才开始将这些工人运送回他们的原籍国。这项研究认为,由于在源头和目的地都没有进行充分的健康检查,火车拥挤不堪,卫生条件不卫生,火车无法按时运行,Shramik列车导致Covid-19从马哈拉施特拉邦和古吉拉特邦等热点地区蔓延到西孟加拉邦、比哈尔邦、奥里萨邦和阿萨姆邦等东部州,这些地区是移民工人的发源地。©2022选择和编辑事项,Rajib Bhattacharyya, Ananya Ghosh Dastidar和Soumyen Sikdar;个人章节,贡献者。
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Lockdowns, migrants, and the spatial distribution of Covid-19 cases in India
This study analyses the impact of the national level lockdown in India on the migrant workers, and how it changed the spatial distribution of Covid-19 cases. Publicly available district-level data is analysed using spatial statistical methods (choropleth maps, Local Indicators of Spatial Analysis, the Getis-Ord gi* statistic, and multi-scale Geographically Weighted Regression models). On 24th March, 2020, the Prime Minister announced a national lockdown to combat the spread of Covid-19 in India. This resulted in lakhs of migrant workers being stranded in their places of work - exposed to Covid-19, without any work or income. Their long march back to their homes was initially ignored;it was only from May that the Indian Railways started to transport these workers back to their states of origin. This study argues that in the absence of adequate health screening at both source and destination, over-crowded trains, insanitary conditions, and failure to run trains on schedule - the Shramik trains resulted in Covid-19 spreading from hotspots like Maharashtra and Gujarat to create new epicentres in eastern states like West Bengal, Bihar, Orissa, and Assam from where the migrant workers had originated. © 2022 selection and editorial matter, Rajib Bhattacharyya, Ananya Ghosh Dastidar and Soumyen Sikdar;individual chapters, the contributors.
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