Covid-19 outbreak and Urban dynamics
Regional variations in India
Bibliographic Data
| ID | 11983337 |
|---|---|
| Authors | Devarupa Gupta (0000-0003-2398-1043, International Institute for Population Sciences, corresponding author), Dibyendu Biswas (0000-0001-6917-420X, Institute of Development Studies), Pintu Kabiraj (0000-0002-5353-5055, Institute of Development Studies) |
| Year | 2021 |
| Volume | 87 |
| Issue | 4 |
| Pages | 2719-2737 |
| Publication date | 2021-03-03 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | GeoJournal (JOURNAL) |
| Journal identifiers | ISSN: 0343-2521 • E-ISSN: 1572-9893 |
| Publisher | Springer Science+Business Media (PUBLISHER • DE) |
| DOI | 10.1007/s10708-021-10394-6 |
| PMID | 33678946 |
| OpenAlex | W3110423360 |
| Language | EN |
| Citations received | 11 |
| References cited | 37 |
India was the second highest COVID-19 affected country in the world with 2.1 million cases by 11th August. This study focused on the spatial transmission of the pandemic among the 640 districts in India over time, and aimed to understand the urban-centric nature of the infection. The connectivity context was emphasized that possibly had inflicted the outbreak. Using the modes of transmission data for the available cases, the diffusion of this disease was explained. Metropolitans contributed three-fourths of total cases from the beginning. The transport networks attributed significantly in transmitting the virus from the urban containment zones. Later, there was a gradual shift of infections from urban to rural areas; however, the numbers kept increasing in the former. The massive reverse migration after lockdown spiked the infected cases further. Districts with airports reported more with influx of international passengers. A profound east-west division in April with higher infections in the southern and western districts existed. By mid-May eastern India saw a steep rise in active cases. Moran's I analysis showed a low autocorrelation initially which increased over time. Hotspot clustering was observed in western Maharashtra, eastern Tamil Nadu, Gujarat and around Kolkata by the second week of August. The diffusion was due to travel, exposure to infected individuals and among the frontline workers. Spatial regression models confirmed that urbanization was positively correlated with higher incidences of infections. Transit mediums, especially rail and aviation were positively associated. These models validated the crucial role of spatial proximity in diffusion of the pandemic
Cluster (spacecraft · Context (archaeology · Coronavirus disease 2019 (COVID-19 · Disease · Economic growth · Geography · Infectious disease (medical specialty · Outbreak · Pandemic · Socioeconomics · Tamil · Urbanization · COVID-19 epidemiological studies · COVID-19 Pandemic Impacts · Demography · Medicine · Zoonotic diseases and public health
Mapping the spatial patterns of Covid-19 case fatality ratio in India
Geoepidemiological perspective on Covid-19 pandemic review, an insight into the global impact
Estimation of disease outbreak vulnerability index (Dovi) and resilience index (Dori) at household-level among states of India, (2019–21)
Crisis, Urban Fabrics, and the Public Interest
Temporal variation of spatial autocorrelation of Covid-19 cases identified in Poland during the year from the beginning of the pandemic
The Gendered Consequences of Covid-19 for Internal Migration
Density or Connectivity
Strategies comparison in response to the two waves of Covid-19 in the United States and India
Landscape Political Ecology
Advancing indoor risk mapping for virus transmission of infectious diseases through geographic scenario simulation
Factors that most expose countries to Covid-19
| Citations per year | 2.2 |
|---|---|
| Citation span | 2021 - 2026 (6) |
| Highly cited | No |