Mapping the spatial patterns of Covid-19 case fatality ratio in India
Bibliographic Data
| ID | 22428712 |
|---|---|
| Authors | Ananya Kundu (0000-0002-9536-6069, University of Calcutta, corresponding author), Sumit Ram (0000-0001-9163-5287, Banaras Hindu University, corresponding author), Arabindo Tanti (0000-0002-7510-562X, Central University of Kerala), Sumana Bandyopadhyay (0000-0001-9896-549X, University of Calcutta) |
| Year | 2026 |
| Volume | 18 |
| Issue | 1 |
| Pages | 100267 |
| Publication date | 2026-01-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Regional Science Policy & Practice (JOURNAL) |
| Journal identifiers | ISSN: 1757-7802 • E-ISSN: 1757-7802 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.rspp.2025.100267 |
| OpenAlex | W4415929887 |
| Language | EN |
| References cited | 31 |
While most studies on COVID-19 in India have centred on infection rates, this study shifts the lens towards mortality by examining the spatial disparities and determinants of the COVID-19 case fatality ratio (CFR), a critical yet underexplored indicator of disease severity with economic implications. District-level data on confirmed COVID-19 cases and deaths (January 2020-October 2021) were sourced from an official health database and the Census of India (2011). Analytical techniques such as Moran’s I and Local Indicators of Spatial Association (LISA) map was conducted across 707 districts to identify geographic patterns and hotspots of CFR. Spatial autoregressive models were applied to explore its key determinants. Results indicated that districts in Maharashtra, Punjab, Delhi, and Nagaland reported the highest CFRs. Moran’s I value of 0.51 ( p < 0.05) confirmed significant spatial clustering, with LISA map identifying 60 high-high cluster districts predominantly in Maharashtra and Punjab. Factors such as the percentage of elderly population, urban residents, and large households were significantly and positively associated with higher CFR. In 2020-21, COVID-19 led to widespread economic decline across Indian states, with Maharashtra, Chandigarh, and Manipur worst affected. In contrast, the post-pandemic years showed a marked economic upsurge, with Kerala, Andhra Pradesh, and Delhi leading recovery, indicating a V-shaped rebound but with uneven recovery trajectories across regions. The study underscores the need for spatially targeted public health interventions. Districts with ageing populations and congested housing should be prioritized in future pandemic planning, while the uneven post-pandemic economic recovery calls for region-specific resilience strategies.
Case fatality rate · Census · Common spatial pattern · Psychological resilience · Public health · Spatial analysis · Spatial ecology · Urbanization · COVID-19 epidemiological studies · COVID-19 Pandemic Impacts · Regional resilience and development
The Covid-19 pandemic
Global, regional, and national estimates of the population at increased risk of severe Covid-19 due to underlying health conditions in 2020
The Spatial Association of Demographic and Population Health Characteristics with Covid‐19 Prevalence Across Districts in India
India’s policy response to the Covid-19 pandemic
Covid-19 Lockdown, Food Systems and Urban–Rural Partnership
Anemia among Men in Malaysia
Covid-19 outbreak and Urban dynamics
Local Indicators of Spatial Association—Lisa
| Citation velocity | historical |
|---|---|
| Highly cited | No |