tetano
Editor, Senior Moderator
Environ Dev Sustain
. 2020 Oct 30;1-29.
doi: 10.1007/s10668-020-01055-8. Online ahead of print.
Nexus between population density and novel coronavirus (COVID-19) pandemic in the south Indian states: A geo-statistical approach
Mohammad Arif[SUP] 1 [/SUP], Soumita Sengupta[SUP] 2 [/SUP]
Affiliations
Abstract
The unprecedented growth of the novel coronavirus (SARS-CoV-2) as a severe acute respiratory syndrome escalated to the coronavirus disease 2019 (COVID-19) pandemic. It has created an unanticipated global public health crisis that is spreading rapidly in India as well, posing a serious threat to 1350 million persons. Among the factors, population density is foremost in posing a challenge in controlling the COVID-19 contagion. In such extraordinary times, evidence-based knowledge is the prime requisite for pacifying the effect. In this piece, we have studied the district wise transmissions of the novel coronavirus in five south Indian states until 20th July 2020 and its relationship with their respective population density. The five states are purposefully selected for their records in better healthcare infrastructure vis-?-vis other states in India. The study uses Pearson's correlation coefficient to account for the direct impact of population density on COVID-19 transmission rate. Response surface methodology approach is used to validate the correlation between density and transmission rate and spatiotemporal dynamics is highlighted using Thiessen polygon method. The analysis has found that COVID-19 transmission in four states (Kerala, Tamil Nadu, Karnataka and Telangana) strongly hinges upon the spatial distribution of population density. In addition, the results indicate that the long-term impacts of the COVID-19 crisis are likely to differ with demographic density. In conclusion, those at the helm of affairs must take cognizance of the vulnerability clusters together across districts.
Keywords: COVID-19; Infections; Population density; Response surface methodology; South India; Thiessen polygon.
. 2020 Oct 30;1-29.
doi: 10.1007/s10668-020-01055-8. Online ahead of print.
Nexus between population density and novel coronavirus (COVID-19) pandemic in the south Indian states: A geo-statistical approach
Mohammad Arif[SUP] 1 [/SUP], Soumita Sengupta[SUP] 2 [/SUP]
Affiliations
- PMID: 33144832
- PMCID: PMC7596317
- DOI: 10.1007/s10668-020-01055-8
Abstract
The unprecedented growth of the novel coronavirus (SARS-CoV-2) as a severe acute respiratory syndrome escalated to the coronavirus disease 2019 (COVID-19) pandemic. It has created an unanticipated global public health crisis that is spreading rapidly in India as well, posing a serious threat to 1350 million persons. Among the factors, population density is foremost in posing a challenge in controlling the COVID-19 contagion. In such extraordinary times, evidence-based knowledge is the prime requisite for pacifying the effect. In this piece, we have studied the district wise transmissions of the novel coronavirus in five south Indian states until 20th July 2020 and its relationship with their respective population density. The five states are purposefully selected for their records in better healthcare infrastructure vis-?-vis other states in India. The study uses Pearson's correlation coefficient to account for the direct impact of population density on COVID-19 transmission rate. Response surface methodology approach is used to validate the correlation between density and transmission rate and spatiotemporal dynamics is highlighted using Thiessen polygon method. The analysis has found that COVID-19 transmission in four states (Kerala, Tamil Nadu, Karnataka and Telangana) strongly hinges upon the spatial distribution of population density. In addition, the results indicate that the long-term impacts of the COVID-19 crisis are likely to differ with demographic density. In conclusion, those at the helm of affairs must take cognizance of the vulnerability clusters together across districts.
Keywords: COVID-19; Infections; Population density; Response surface methodology; South India; Thiessen polygon.