ArticlePloS one2021
District level correlates of COVID-19 pandemic in India during March-October 2020.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Effects of the built environment and human factors on the spread of COVID-19: A systematic literature review.The Science of the total environment · 2022Pooled it
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- Geographical access to COVID-19 testing centers in India.BMC health services research · 2025Article
- Socioeconomic status correlates with COVID-19 vaccination coverage among primary and secondary students in the most populated city of Chile.Scientific reports · 2025Article
- Construction and validation of a covariate-based model for district-level estimation of excess deaths due to COVID-19 in India.Journal of global health · 2024Article
- A Bayesian spatio-temporal model of COVID-19 spread in England.Scientific reports · 2024Article
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- Tokyo's COVID-19: An urban perspective on factors influencing infection rates in a global city.Sustainable cities and society · 2023Article
- COVID-19 Pandemic: Did Strict Mobility Restrictions Save Lives and Healthcare Costs in Maharashtra, India?Healthcare (Basel, Switzerland) · 2023Article
- Explore the reasons for SARS-CoV-2 vaccine hesitancy among healthcare workers: a cross-sectional study.Annals of medicine and surgery (2012) · 2023Article
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- A district-level vulnerability assessment of next COVID-19 variant (Omicron BA.2) in Uttarakhand using quantitative SWOT analysis.Environment, development and sustainability · 2022Article
- Hybrid immunity against COVID-19 in different countries with a special emphasis on the Indian scenario during the Omicron period.International immunopharmacology · 2022Article
- A cross-sectional study of COVID-19 outbreak in Indian population.Annals of medicine and surgery (2012) · 2022Article
- District-Level Risk Factors for COVID-19 Incidence and Mortality in Nepal.International journal of environmental research and public health · 2022Article
- Seroprevalence of SARS-CoV-2-specific anti-spike IgM, IgG, and anti-nucleocapsid IgG antibodies during the second wave of the pandemic: A population-based cross-sectional survey across Kashmir, India.Frontiers in public health · 2022Article
- The role of demography in COVID-19 in India.The Indian journal of medical researchArticle
- India's Multi-Sectoral Response to Oxygen Surge Demand during COVID-19 Pandemic: A Scoping Review.Indian journal of community medicine : official publication of Indian Association of Preventive & Social MedicineArticle
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9 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundCOVID-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.
objectiveThe 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
methodsWe 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.
resultsThe 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.
conclusionThe 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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