Das Arijit, Ghosh Sasanka, Das Kalikinkar, Basu Tirthankar, Dutta Ipsita, Das Manob
Department of Geography, University of Gour Banga, Malda, India.
Department of Geography, Kazi Nazrul University, Asansol, India.
Sustain Cities Soc. 2021 Feb;65:102577. doi: 10.1016/j.scs.2020.102577. Epub 2020 Oct 31.
The emergence of COVID-19 has brought a serious global public health threats especially for most of the cities across the world even in India more than 50 % of the total cases were reported from large ten cities. Kolkata Megacity became one of the major COVID-19 hotspot cities in India. Living environment deprivation is one of the significant risk factor of infectious diseases transmissions like COVID-19. The paper aims to examine the impact of living environment deprivation on COVID-19 hotspot in Kolkata megacity. COVID-19 hotspot maps were prepared using Getis-Ord-Gi* statistic and index of multiple deprivations (IMD) across the wards were assessed using Geographically Weighted Principal Component Analysis (GWPCA).Five count data regression models such as Poisson regression (PR), negative binomial regression (NBR), hurdle regression (HR), zero-inflated Poisson regression (ZIPR), and zero-inflated negative binomial regression (ZINBR) were used to understand the impact of living environment deprivation on COVID-19 hotspot in Kolkata megacity. The findings of the study revealed that living environment deprivation was an important determinant of spatial clustering of COVID-19 hotspots in Kolkata megacity and zero-inflated negative binomial regression (ZINBR) better explains this relationship with highest variations (adj. R2: 71.3 %) and lowest BIC and AIC as compared to the others.
新冠疫情的出现给全球带来了严重的公共卫生威胁，尤其对世界上大多数城市而言，即使在印度，超过50%的病例来自十大城市。加尔各答特大城市成为印度主要的新冠疫情热点城市之一。生活环境匮乏是新冠疫情等传染病传播的重要风险因素之一。本文旨在研究生活环境匮乏对加尔各答特大城市新冠疫情热点的影响。利用Getis-Ord-Gi*统计量绘制新冠疫情热点地图，并使用地理加权主成分分析（GWPCA）评估各病房的多重剥夺指数（IMD）。使用泊松回归（PR）、负二项回归（NBR）、障碍回归（HR）、零膨胀泊松回归（ZIPR）和零膨胀负二项回归（ZINBR）等五种计数数据回归模型，来了解生活环境匮乏对加尔各答特大城市新冠疫情热点的影响。研究结果表明，生活环境匮乏是加尔各答特大城市新冠疫情热点空间聚集的重要决定因素，与其他模型相比，零膨胀负二项回归（ZINBR）能更好地解释这种关系，具有最高的变异度（调整R2：71.3%）以及最低的贝叶斯信息准则（BIC）和赤池信息准则（AIC）。
