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Living environment matters: Unravelling the spatial clustering of COVID-19 hotspots in Kolkata megacity, India.
Das, Arijit; Ghosh, Sasanka; Das, Kalikinkar; Basu, Tirthankar; Dutta, Ipsita; Das, Manob.
  • Das A; Department of Geography, University of Gour Banga, Malda, India.
  • Ghosh S; Department of Geography, Kazi Nazrul University, Asansol, India.
  • Das K; Department of Geography, University of Gour Banga, Malda, India.
  • Basu T; Department of Geography, University of Gour Banga, Malda, India.
  • Dutta I; Department of Geography, University of Gour Banga, Malda, India.
  • Das M; Department of Geography, University of Gour Banga, Malda, India.
Sustain Cities Soc ; 65: 102577, 2021 Feb.
Article in English | MEDLINE | ID: covidwho-894214
ABSTRACT
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.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Language: English Journal: Sustain Cities Soc Year: 2021 Document Type: Article Affiliation country: J.scs.2020.102577

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Language: English Journal: Sustain Cities Soc Year: 2021 Document Type: Article Affiliation country: J.scs.2020.102577