ArticleScientific reports2024
Spatio-temporal epidemiology and associated indicators of COVID-19 (wave-I and II) in India.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
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- Monocyte-to-high-density lipoprotein cholesterol ratio predicts hematoma expansion and poor outcomes in patients with spontaneous intracerebral hemorrhage.BMC neurology · 2026Article
- Cycle Threshold (Ct) Value Trends in COVID-19: Analyzing Gender, Age, and Severity Factors Across Major Waves in India.Immunity, inflammation and disease · 2026Article
- Association between antibiotic resistance and air pollution and climate factors in China: A multivariable spatial-temporal analysis.One health (Amsterdam, Netherlands) · 2025Article
- Spatiotemporal dynamics and associated drivers of COVID-19 incidence in Nepal.Tropical medicine and health · 2025Article
- Investigation of influential environmental and climatic determinants on COVID-19 spread in India to formulate a sustainable pandemic response.One health (Amsterdam, Netherlands) · 2025Article
- Blockchain enabled collective and combined deep learning framework for COVID19 diagnosis.Scientific reports · 2025Article
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Abstract
The spatio-temporal distribution of COVID-19 across India's states and union territories is not uniform, and the reasons for the heterogeneous spread are unclear. Identifying the space-time trends and underlying indicators influencing COVID-19 epidemiology at micro-administrative units (districts) will help guide public health strategies. The district-wise daily COVID-19 data of cases and deaths from February 2020 to August 2021 (COVID-19 waves-I and II) for the entire country were downloaded and curated from public databases. The COVID-19 data normalized with the projected population (2020) and used for space-time trend analysis shows the states/districts in southern India are the worst hit. Coastal districts and districts adjoining large urban regions of Mumbai, Chennai, Bengaluru, Goa, and New Delhi experienced > 50,001 cases per million population. Negative binomial regression analysis with 21 independent variables (identified through multicollinearity analysis, with VIF < 10) covering demography, socio-economic status, environment, and health was carried out for wave-I, wave-II, and total (wave-I and wave-II) cases and deaths. It shows wealth index, derived from household amenities datasets, has a high positive risk ratio (RR) with COVID-19 cases (RR: 3.577; 95% CI: 2.062-6.205) and deaths (RR: 2.477; 95% CI: 1.361-4.506) across the districts. Furthermore, socio-economic factors such as literacy rate, health services, other workers' rate, alcohol use in men, tobacco use in women, overweight/obese women, and rainfall have a positive RR and are significantly associated with COVID-19 cases/deaths at the district level. These positively associated variables are highly interconnected in COVID-19 hotspot districts. Among these, the wealth index, literacy rate, and health services, the key indices of socio-economic development within a state, are some of the significant indicators associated with COVID-19 epidemiology in India. The identification of district-level space-time trends and indicators associated with COVID-19 would help policymakers devise strategies and guidelines during public health emergencies.
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