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

doi:10.1016/j.scs.2020.102577

Cited by 103 publications

(86 citation statements)

References 78 publications

(101 reference statements)

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“…For example, our results show that increased adequacy of medical practitioners can mitigate pandemic disaster vulnerability in high density cities (shown by the negative coefficients for the interaction term), but cannot achieve the similar effects for large-scale economic activity. Moreover, how urban characteristics interplay with other socioeconomic factors (including poverty and income) that have strong influence on severety of outbreak and how these factors should be considered in urban planning, design and governance, are subject to future investigation ( Das et al, 2021 ; Sannigrahi, Pilla, Basu, Basu, & Molter, 2020 ; Silva et al, 2021 ; Wang, 2021 ).…”

Section: Empirical Results and Discussionmentioning

confidence: 99%

What determines city’s resilience against epidemic outbreak: evidence from China’s COVID-19 experience

Chen¹,

Guo

Pan³

et al. 2021

Sustainable Cities and Society

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By employing the city-level data from China during the spring of 2020, this study investigates the relationship between city-level resilience against the outbreak of COVID-19 pandemics and its affecting factors, including the inflow risk pressure of COVID-19 virus (population inflow from the epicenter), city agglomeration characteristics (urban population density and city size), healthcare resource adequacy, among others. The results reveal that, while managing COVID-19 inflow risk pressure plays a critical role in the city’s pandemic disaster resilience. city agglomeration characteristics also matters. To be exact, we find that large and high-density cities with high inter and intra-city mobility flows have more difficulties in containing the epidemic spread, but improving healthcare infrastructure adequacy and urban governance capacity can increase time efficacy of pandemic control and then improve the city’s resilience against pandemic. Although our analysis is based on the performance of Chinese cities in the case of COVID-19, the research framework can be applied in understanding COVID-19 control performance of cities in other countries and the findings can be useful for improving health-related urban resilience and sustainability.

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Section: Empirical Results and Discussionmentioning

confidence: 99%

What determines city’s resilience against epidemic outbreak: evidence from China’s COVID-19 experience

Chen¹,

Guo

Pan³

et al. 2021

Sustainable Cities and Society

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“…This result was expected since (a) COVID-19 started and is still concentrated mostly in big cities in the study area, and (b) there is significant evidence from literature associating higher urbanization with worst respiratory and cardiovascular health, as presented in section 1 . Studies including Kodera et al (2020) in Japan, Das et al (2021) in Kolkata, India, Li et al (2021) in Huangzhou, China, and Mansour et al (2021) in Oman, already show an association between urban parameters, such as population density, and COVID-19.…”

Section: Discussionmentioning

confidence: 96%

The influence of urban, socio-economic, and eco-environmental aspects on COVID-19 cases, deaths and mortality: A multi-city case in the Atlantic Forest, Brazil

Viezzer

Biondi

2021

Sustainable Cities and Society

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Urban, socio-economic and eco-environmental influences on people’s health are widely studied and well-known. Their relation to COVID-19, however, is still a novel research topic. Thus, we investigated if COVID-19 parameters are higher in cities with higher urbanization, worst socio-economic conditions, and less vegetation cover, considering 3,052 municipalities in the Atlantic Forest, Brazil. Brazil is the second country most affected by COVID-19, and the Atlantic Forest is its most urbanized, populous, and deforested region. Indexes were created through multivariate principal components analysis using secondary official data: population, demographic density, absolute built area, and relative built area as urbanization parameters; average per capita income, relative people vulnerable to poverty, illiteracy rate of the population aged 18 or over, and human development index (HDI) as socio-economic parameters; and absolute and relative vegetation cover, absolute and relative forest cover as eco-environmental parameters. These indexes were correlated with absolute and relative confirmed COVID-19 cases, absolute and relative confirmed deaths, and mortality rate via Spearman’s and Kendall’s coefficients. Strong correlations (>0.50) were found between COVID-19 and urbanization. Socio-economic and eco-environmental aspects, although weaker predictors of COVID-19, presented meaningful relations with the health parameters. This study contributes to the evidence regarding COVID-19 incidence in the Brazilian population.

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“…Aside from the physical restriction measures imposed by local and federal governments, other potential municipal-level factors associated to dynamic of the disease could be existing socio-economic inequalities. The empirical evidence of Das et al (2020) showed that COVID-19 spread grow exponentially in those Indian regions with large socio-economic deprivation. Similarly, the evidence comparing United Kingdom regions (Sun et al, 2020b) revealed important geographical disparities in risk and outcomes of the disease.…”

Section: Discussionmentioning

confidence: 99%

The main factors influencing COVID-19 spread and deaths in Mexico: A comparison between Phases I and II

Benita

Gasca-Sanchez

2020

Preprint

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This article investigates the geographical spread of COVID-19 confirmed cases and deaths across municipalities in Mexico. It focuses on the spread dynamics between Phase I (from March 23th to May 31st, 2020) and II (from June 1st to August 22th, 2020) of the social distancing measures. It also examines municipal-level factors associated with cumulative COVID-19 cases and deaths to understand the spatial determinants of the pandemic. The analysis of the geographic pattern of the pandemic via Space-Time Scan Statistics (SaTScan) revealed fast spread among municipalities. During Phase I, clusters of infections and deaths were mainly located at the center of the country, while in Phase II, these clusters dispersed to the rest of the country. The regression results from the Zero-Inflated Negative Binomial Regression analysis suggested that income inequality, prevalence of obesity and diabetes, and concentration of fine particulate matter (PM 2.5) are strongly positively associated to confirmed cases and deaths regardless of lockdown.

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Living environment matters: Unravelling the spatial clustering of COVID-19 hotspots in Kolkata megacity, India

Abstract: Graphical abstract

Cited by 103 publications

References 78 publications

What determines city’s resilience against epidemic outbreak: evidence from China’s COVID-19 experience

What determines city’s resilience against epidemic outbreak: evidence from China’s COVID-19 experience

The influence of urban, socio-economic, and eco-environmental aspects on COVID-19 cases, deaths and mortality: A multi-city case in the Atlantic Forest, Brazil

The main factors influencing COVID-19 spread and deaths in Mexico: A comparison between Phases I and II

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