Determinants of COVID-19 pandemic in India: an exploratory study of Indian states and districts

Pandey, Arvind; Prakash, Aseem; Agur, Rajeev; Maruvada, Ganesh

doi:10.1007/s40847-021-00154-0

Cited by 15 publications

(9 citation statements)

References 21 publications

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“…Apart from this, Location Quotient (LQ) is a useful tool for comparing area characteristics (Moineddin et al, 2003 ;Ghosh et al, 2021 ; Pandey et al, 2021 ) and its use in healthcare has been well established (Hendayana, 2003 ; Saravanabavan et al, 2019 ; Sukmawati, 2021 ). Most of the studies in medical geography are concerned with the description of spatial and temporal variations of a particular disease and are attempted at the regional level (Bossak & Turk, 2021 ; Balaj & Saravanabavan 2019 , 2020 ; Vinothini et al, 2021 ; Bonner et al, 2019 ).…”

Section: Database and Methodologymentioning

confidence: 99%

Spatial variation of covid-19 morbidity status and identification of risk zone in Tamil Nadu (India) during first wave

Saravanabavan

Emayavaramban

et al. 2022

GeoJournal

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COVID-19 is a pandemic disease caused by severe acute respiratory syndrome corona virus 2 (SARS-CoV-2). The study area, Tamilnadu, is a southern state in India. The present study demonstrates the significance of mapping in identifying the risk zones of COVID-19 disease by taking a study of COVID-19 cases in Tamilnadu state. The main objective of the study was to identify the risk zone of COVID-19 disease in the first wave of Tamilnadu through geospatial mapping. Data on COVID-19 cases collected during March 2020 to March 2021 month wise in all 37 districts (unit area) of Tamilnadu in two-month interval wise was analysed. The study is based on secondary sources from respective office in Tamilnadu. Z-score techniques were used for standardizing data. Correlation analysis provided a measure of correlation between COVID-19 and working population. Location Quotient analysis aided the identification of COVID-19 risk zones in Tamilnadu. Present study indicates tertiary workers are more vulnerable to COVID-19 disease.

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Section: Database and Methodologymentioning

confidence: 99%

Spatial variation of covid-19 morbidity status and identification of risk zone in Tamil Nadu (India) during first wave

Saravanabavan

Emayavaramban

et al. 2022

GeoJournal

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“…Firstly, we examined the relationship between epidemiological data and different socioeconomic indicators using Pearson's correlation coefficient. Secondly, we examined the spatial concentrations of the epidemiological data using Location Quotient (LQ), which indicates the relative distributions or relative concentrations of a subarea to the area as a whole (Pandey et al 2021). Thirdly, we conducted a cluster analysis of the micro-regions (LAU 1) involving the rate of deaths within COVID-19 infected persons and three socio-economic indicators (annual income per taxpayer, unemployment rate and life expectancy at birth).…”

Section: Aims and Methodsmentioning

confidence: 99%

Spatial Analysis of the COVID-19 Pandemic in Hungary

Uzzoli¹,

Kovács²,

Fábián³

et al. 2021

REGION

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This paper examines the spatial dynamics and regional distribution of the novel coronavirus epidemic in Hungary in an effort to obtain a deeper understanding of the connection between space and health. The paper also presents comprehensive epidemiologic data on the spatiotemporal spread of the COVID-19 pandemic in terms of the epidemic waves. Following a comparison of the growth rates of infection numbers, the current study explores the geographical dimension of the three pandemic waves. The partial transformation of spatial characteristics during the three epidemic waves is among the most important results found. While geographical hotspots influenced the first wave, newly confirmed coronavirus cases in the second and third waves were due to community-based epidemic spreading. Furthermore, the western-eastern spatial relation and the core-periphery model also affected the regional distribution of new cases and deaths in the initial two waves. However, a new spatial pattern - realised by the northern-southern spatial orientation - appeared during the third wave. The outputs of this paper offer feasible suggestions for evidence-based policymaking in pandemic prevention, mitigation, and preparedness.

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“…2 These and other social determinants of health were major determining factors in the severity and numbers of COVID-19 cases. 18 Formal healthcare services are of poor quality as government clinics are understaffed or under-supplied and while private clinics are preferred; quality of care is patchy and services are costly. 19 Many residents work in informal, unregulated, and high-risk occupations such as waste recycling, and waste-related industries, which were duly recognised as 'essential work' during the pandemic.…”

Section: Covid-19 Vulnerabilitiesmentioning

confidence: 99%

Evidence Review: COVID-19 Recovery in South Asian Urban Informal Settlements

Aslam¹,

Schmidt‐Sane²

2022

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The global pandemic has brought renewed attention toward the everyday challenges in informal settlements. COVID-19 reminds us that southern urban life is rooted in ‘collective’ experiences where toilets and kitchens are shared by multiple families; where the categories of work and home, private and public space overlap; and where the majority live in vulnerable conditions. Despite these challenges, some of the most innovative and collective responses to COVID-19 have emerged from these areas. While informal settlements did face a host of risks and vulnerabilities during the pandemic, local responses have highlighted the resilience of informal settlement communities. However, few informal settlements are actually ‘resilient’ and any local responses must be robustly supported by system-wide change including support from local and national governments, improvements to built infrastructure, and improved access to health care services, among other priorities. The category of ‘informal settlements’ also captures a wide range of settlement types, from a legal slum to an informal settlement with no legal status, with many other types in between. This underscores the need to address fundamental issues that ‘perpetuate conditions of inequity, exclusion and vulnerability’ while also recognising the needs and contexts of different kinds of informal settlements. Whether COVID-19 helps governments recognise conditions of insecurity and vulnerability to address safe and secure housing and infrastructures remains to be seen. This is an update to the previous SSHAP brief on ‘COVID-19 in Informal Urban Settlements’ (March 2020). This evidence review highlights local responses, grassroots efforts, and challenges around COVID-19 recovery within urban informal settlements in South Asia. It focuses on specific examples from Karachi, Pakistan and Mumbai, India to inform policy responses for COVID-19 recovery and future epidemic preparedness and response. We show how local level responses are shaped in these cities where national and international responses have not reached communities at municipal and sub-municipal levels. This brief was written by Saba Aslam (IDS Alumni) and Megan Schmidt-Sane (IDS), with reviews from Professor Amita Bhide (Tata Institute of Social Sciences, India), Dr Asad Sayeed (Collective for Social Science Research, Pakistan), Annie Wilkinson (IDS), and contributions from Swati Mishra (LSHTM), Prerana Somani (LSHTM), Saleemullah Odho (Deputy Commissioner, Korangi district Karachi), Dr Noman Ahmed (NED University, Karachi), Tahera Hasan (Imkaan Foundation, Karachi), Atif Khan (District Health Officer, Korangi district Karachi), Dr Harris (District Focal person, Korangi), Aneeta Pasha (Interactive for Research and Development, Karachi), Yasmeen Shah (Pakistan Fisherfolk Forum), Ghulam Mustafa (HANDS Pakistan), and Dr Shehrin Shaila Mahmood (icddr,b). This brief is the responsibility of SSHAP.

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Determinants of COVID-19 pandemic in India: an exploratory study of Indian states and districts

Cited by 15 publications

References 21 publications

Spatial variation of covid-19 morbidity status and identification of risk zone in Tamil Nadu (India) during first wave

Spatial variation of covid-19 morbidity status and identification of risk zone in Tamil Nadu (India) during first wave

Spatial Analysis of the COVID-19 Pandemic in Hungary

Evidence Review: COVID-19 Recovery in South Asian Urban Informal Settlements

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