Impact of key meteorological parameters on the spread of COVID-19 in Mumbai: Correlation and Regression Analysis
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Abstract
Purpose
To understand key meteorological parameters that influence the spread of COVID-19 in Mumbai, India (based on data from April 2020 – April 2021).
Methods
The meteorological parameters chosen were Temperature, Dew Temperature, Humidity, Pressure, Wind Speed. The underlying basic relationships between meteorological parameters and COVID-19 information for Mumbai was understood using Spearman’s rank correlation coefficients. After establishing basic relationships, Linear analysis and Generalized Additive Model’s (GAM) were used to figure out statistically significant weather parameters and model them to explain the best possible variance in the pandemic data.
Results
A model of temperature and windspeed could explain 17.3% and 8.3% of variance in Daily new cases and Daily recoveries respectively. As for deaths occurring due to the virus, a model comprising of only pressure best explains a variance of 17.3% in the data. Non-Linear modelling based on GAM confirms the findings of linear analysis and establishes certain non-linear relationships as well.
Conclusion
SARS-CoV-2 belongs to the class of Human Coronaviruses (HCoV) which show seasonality depending on weather conditions. The above article focuses on understanding the underlying relationships between SARS-CoV-2 and meteorological parameters that would help progress basic research and formulation of policies around the disease for each weather/season.
Competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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SciScore for 10.1101/2022.02.22.22271376: (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 The regression analysis was done using SPSS (version 25.0) and a p-value of less than 0.05 was chosen to establish statistical significance. SPSSsuggested: (SPSS, RRID:SCR_002865)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.
SciScore for 10.1101/2022.02.22.22271376: (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 The regression analysis was done using SPSS (version 25.0) and a p-value of less than 0.05 was chosen to establish statistical significance. SPSSsuggested: (SPSS, RRID:SCR_002865)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: We did not find any issues relating to colormaps.
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.
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