COVID-19 and urban vulnerability in India

Abstract

The global pandemic has an inherently urban character. The UN-Habitat’s awareness of it has led to the publication of a Response Plan for mollification of the disease-induced externalities in the cities of the world. This article takes the UN-Habitat report as the premise to carry out an empirical investigation in the four metro cities of India. The report’s concern with the urban character of the pandemic has underlined the role of cities in disease transmission. In that wake, the study demarcates factors at the sub-city level that tend to jeopardize the two mandatory precautionary measures during COVID-19 – Social Distancing and Lockdown. It investigates those factors that bring deprived locales parallel to COVID-19 induced vulnerability. Secondly, UN-Habitat’s one of the major action areas is evidence-based knowledge creation through mapping and its analysis. In our study, we do it at a more granular scale than the city so a more nuanced understanding can be arrived at. Thus, in tune with the UN-habitat’s we have embarked on a detailed study of the four metro cities in India that are simultaneously the densest in the global south.

The COVID-19 pandemic: Impacts on cities and major lessons for urban planning, design, and management

“…ft. that is even below the recommended floor area for prisoners; the conditions are even worse for slum dwellers ( Biswas, 2020 ). This and other inequality issues make it difficult to socially distance, thereby undermining the effectiveness of ‘homestay’ orders for containing the spread of the virus ( Mishra et al, 2020 ). Similar issues have also been discussed in the context of some African and Brazilian cities ( Kihato and Landau, 2020 ; de Oliveira and de Aguiar Arantes, 2020 ).…”

Geospatial modelling on the spread and dynamics of 154 day outbreak of the novel coronavirus (COVID-19) pandemic in Bangladesh towards vulnerability zoning and management approaches

“…Literature supports that some certain studies were carried out over different parts of the world regarding the analysis of COVID-19 spatially (Zhou et al 2020 ; Fan et al 2020 , Gatto et al 2020 , Murugesan et al 2020 ; Kamel-Boulos and Geraghty 2020 ; Meyer et al 2017 ; Mo et al 2020 ; Roy et al 2020 ). Particularly, a comprehensive study on the nature of spatial spreading, geospatial pattern of transmission, vulnerability, and risk level along with hot spots analysis of COVID-19 were not widely analyzed (Ramírez-Aldana et al 2020 ; Huang et al 2020 ; Jia et al 2020 ; Kang et al 2020 ; Melin et al 2020 ; Ghosh et al 2020 ; Mishra et al 2020 ; Acharya and Porwal 2020 ). Conversely, in case of Bangladesh, in-depth study of COVID-19 using geospatial approach was very rare (Sakamoto et al 2020 ; Masrur et al 2020 ) and which were available mostly epidemiological, temporal, and modelling based and some of those mainly highlighted the impacts of COVID-19 on the economy, society, health, and environment (Anwar et al 2020 ; Bodrud-Doza 2020 ; Hossain et al 2020 ; Jahan et al 2020 ; Khan et al 2020a , b ; Khan et al 2020a ; Zabir et al 2020 ).…”

Evaluating the plausible application of advanced machine learnings in exploring determinant factors of present pandemic: A case for continent specific COVID-19 analysis

“…that were found to be closely associated with COVID-19 cases-fatality rate ( Ren et al, 2020 ; Sannigrahi et al, 2020a , Sannigrahi et al, 2020b ). According to UN-HABITAT (2020) , low income countries with poor healthcare infrastructure, high population and room density, and informal job markets, could be more affected by COVID-19 ( Mishra et al, 2020 ). Bolaño-Ortiz et al (2020) found that poverty and income inequality is playing a significant role in COVID-19 fatalities in South American cities.…”