Impact of key meteorological parameters on the spread of COVID-19 in Mumbai: Correlation and Regression Analysis

  1. Sushant Shetty
  2. Amit Gawade
  3. Subodh Deolekar
  4. Vaishali Patil
  5. Rohit Pandharkar
  6. Uday Salunkhe

Reviewed by

Read the full article

Listed in

Log in to save this article

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.

Article activity feed

  1. ScreenIT

    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
    SentencesResources
    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.
    SPSS
    suggested: (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.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2022.02.22.22271376v1 on medRxiv