Correlation between the environmental parameters with outbreak pattern of COVID-19: A district level investigation based on yearlong period in India
This article has been Reviewed by the following groups
Listed in
- Evaluated articles (ScreenIT)
Abstract
The present study has investigated the role of regional meteorology and air quality parameters in the outbreak pattern of COVID-19 pandemic in India. Using the remote sensing based dataset of 12 environmental variables we correlated infective case counts at a district level in India. Our investigation carried out on the circumstantial data from more than 300 major affected districts in India and found that air quality parameters are playing very crucial role in this outbreak. Among the air pollutants, O 3 was better correlating with infection counts followed by AOD, CO, NO 2 , BC and SO 2 . We also observed that among the weather parameters air temperature, incoming shortwave radiation, wind speed are positively and significantly associate with outbreak pattern and precipitation and humidity are negatively correlated with confirmed cases; only cloud cover has no significant relation. We noted that coastal districts in the both coast of India and districts located in the plain and low-lying areas have experienced bitter situation during this pandemic. Our study suggests that improving air quality with proper strict regulations and complete lockdown during the peak of pandemic could reduce the misfortune in all over India.
Article activity feed
-
SciScore for 10.1101/2021.06.28.21259631: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources Monthly mean of these twelve environmental variables were further processed in ArcGIS software, adjoined and related with the monthly cumulative counts of confirmed and recovery cases for each months for each of those selected districts. ArcGISsuggested: (ArcGIS for Desktop Basic, RRID:SCR_011081)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. …SciScore for 10.1101/2021.06.28.21259631: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources Monthly mean of these twelve environmental variables were further processed in ArcGIS software, adjoined and related with the monthly cumulative counts of confirmed and recovery cases for each months for each of those selected districts. ArcGISsuggested: (ArcGIS for Desktop Basic, RRID:SCR_011081)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: Please consider improving the rainbow (“jet”) colormap(s) used on page 5. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
-
