COVID-19: District level vulnerability assessment in India

Sarkar, Apurba; Chouhan, Pradip

doi:10.1016/j.cegh.2020.08.017

Cited by 64 publications

(77 citation statements)

References 43 publications

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“…At the onset of the COVID-19 pandemic, India imposed world’s strictest nationwide lockdown beginning from March 25, 2020 [ 39 ], But, as of April 10, 2021, India was the third leading country after USA and Brazil’s identified cases [ 40 ]. Like several other parts of the world, India has been experiencing a massive surge of COVID-19 cases and deaths [ 41 – 43 ]. The second wave has started in the middle of March, 2021 and recorded highest number of cases (144,829) on April 09, 2021 [ 42 , 43 ].…”

Section: Resultsmentioning

confidence: 99%

District level correlates of COVID-19 pandemic in India during March-October 2020

et al. 2021

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Background COVID-19 is affecting the entire population of India. Understanding district level correlates of the COVID-19’s infection ratio (IR) is essential for formulating policies and interventions. Objective The present study aims to investigate the district level variation in COVID-19 during March-October 2020. The present study also examines the association between India’s socioeconomic and demographic characteristics and the COVID-19 infection ratio at the district level. Data and methods We used publicly available crowdsourced district-level data on COVID-19 from March 14, 2020, to October 31, 2020. We identified hotspot and cold spot districts for COVID-19 cases and infection ratio. We have also carried out two sets of regression analysis to highlight the district level demographic, socioeconomic, household infrastructure facilities, and health-related correlates of the COVID-19 infection ratio. Results The results showed on October 31, 2020, the IR in India was 42.85 per hundred thousand population, with the highest in Kerala (259.63) and the lowest in Bihar (6.58). About 80 percent infected cases and 61 percent deaths were observed in nine states (Delhi, Gujarat, West Bengal, Uttar Pradesh, Andhra Pradesh, Maharashtra, Karnataka, Tamil Nadu, and Telangana). Moran’s- I showed a positive yet poor spatial clustering in the COVID-19 IR over neighboring districts. Our regression analysis demonstrated that percent of 15–59 aged population, district population density, percent of the urban population, district-level testing ratio, and percent of stunted children were significantly and positively associated with the COVID-19 infection ratio. We also found that, with an increasing percentage of literacy, there is a lower infection ratio in Indian districts. Conclusion The COVID-19 infection ratio was found to be more rampant in districts with a higher working-age population, higher population density, a higher urban population, a higher testing ratio, and a higher level of stunted children. The study findings provide crucial information for policy discourse, emphasizing the vulnerability of the highly urbanized and densely populated areas.

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Section: Resultsmentioning

confidence: 99%

District level correlates of COVID-19 pandemic in India during March-October 2020

et al. 2021

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“…Therefore, the confirmed cases in Ramallah & Al-Bireh are very high whereas the CVI is relatively high. However, the number of confirmed cases is highly related to socio-economic conditions such as poor households [ 19 ] employment [ 43 ], car ownership [ 26 ], and household density [ 44 ]. Thus, the obtained CVI values do not reflect the actual number of the confirmed cases in all of the West Bank governorates.…”

Section: Resultsmentioning

confidence: 99%

GIS-based COVID-19 vulnerability mapping in the West Bank, Palestine

Shadeed

Alawna

2021

International Journal of Disaster Risk Reduction

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From the beginning of the COVID-19 pandemic, the world stands idly by in the face of the virus spreading. The prediction of highly vulnerable populations and the implementation of proper actions are very important steps to break the infection chain of any virus. This will, in turn, reduce the economic and social impact of this virus outbreak. In this study, the COVID-19 vulnerability map for the West Bank, Palestine was developed. Analytic Hierarchy Process (AHP) was used to develop the COVID-19 vulnerability map. The GIS in combination with multi-criteria decision analysis (MCDA) was adopted to estimate the COVID-19 vulnerability index (CVI) based on some selected potential criteria including population, population density, elderly population, accommodation and food service activities, school students, chronic diseases, hospital beds, health insurance, and pharmacy. The results of this study show that Nablus, Jerusalem, and Hebron governorates are under very high vulnerability. Tulkarm, Ramallah & Al-Bireh and Jenin governorates are high vulnerable to COVID-19. Additionally, 82 % of the West Bank populations are under high to very high COVID-19 vulnerability classes. Moreover, 14% and 4 % are medium and low to very low vulnerable, respectively. The obtained results are of high value to help decision-makers to take to proper actions as early as possible mainly in the highly COVID-19 vulnerable governorates to control the risk associated with the potential outbreak of the virus and accordingly to protect social life and to sustain economic conditions.

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“…In addition, the relaxation phase of domestic transportation (especially railways) from November 2020 up to almost mid of April made the situation favourable for rapid infection spread. A recent study revealed the composite analysis based on socio-economic-political parameters at the district level to construct a new Socio-Economic Vulnerability Index ( Sarkar and Chouhan, 2021 ). The index indicated the high vulnerability of North-West Delhi, Central Delhi, Kolkata, Mumbai and Ghaziabad.…”

Section: Resultsmentioning

confidence: 99%

“…The COVID-19 pandemic has resurfaced in India in the form of a hard-hitting second wave. The COVID-19 has brought a threatening challenge to Indian society and the economy ( Sarkar and Chouhan, 2021 ). India's devastating second wave of COVID-19 has overwhelmed its health system and the country ( Ranjan, 2020 , Ghosh et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

COVID-19 second wave: District level study of concentration of confirmed cases and fatality in India

Ghosh¹,

Sarkar²,

Chouhan³

2021

Environmental Challenges

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The paper aims to reveal the spatial pattern of the concentration of COVID-19 confirmed cases and the spread of the pandemic from the Case Fatality Ratio. The study has been accomplished with district-level data. The analysis of the spatial pattern decoding has been done considering the Global and Local Moran's I statistics comprising the linear trend of spatial autocorrelation for the whole India. The timeframe has been divided considering the surge of the second wave in March, 2021 and the peak of the wave in May 2021. The spatial clustering technique presents both the concentration of confirmeded cases using Location Quotient analysis and the pattern of spread of the infection-related fatality throughout the country. The high Location Quotient of the confirmeded cases strongly clustered around the Mumbai-Puna region, Kerala-Karnataka region, Garhwal Himachal, NCT of Delhi and Ladakh-Kashmir-Himachal Pradesh region during the period of the study. In May, the concentration has randomly clustered around the middle part of India. The Case Fatality Ratio was high in Maharashtra, Madhya Pradesh, Punjab and Haryana at the surge of the second wave. In peak (May), the two significant clusters of high case fatality ratio is pivoted around in Mumbai urban (Maharashtra) and NCT of Delhi (including Punjab-Haryana).

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COVID-19: District level vulnerability assessment in India

Cited by 64 publications

References 43 publications

District level correlates of COVID-19 pandemic in India during March-October 2020

District level correlates of COVID-19 pandemic in India during March-October 2020

GIS-based COVID-19 vulnerability mapping in the West Bank, Palestine

COVID-19 second wave: District level study of concentration of confirmed cases and fatality in India

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