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Application of Machine Learning to Study the Association between Environmental Factors and COVID-19 Cases in Mississippi, USA

Author

Listed:
  • Francis Tuluri

    (Department of Industrial Systems & Technology, Jackson State University, Jackson, MS 39217, USA)

  • Reddy Remata

    (Department of Atmospheric Sciences, Jackson State University, Jackson, MS 39217, USA)

  • Wilbur L. Walters

    (College of Sciences, Engineering & Technology, Jackson State University, Jackson, MS 39217, USA)

  • Paul. B. Tchounwou

    (Department of Biology, Jackson State University, Jackson, MS 39217, USA)

Abstract

Because of the large-scale impact of COVID-19 on human health, several investigations are being conducted to understand the underlying mechanisms affecting the spread and transmission of the disease. The present study aimed to assess the effects of selected environmental factors such as temperature, humidity, dew point, wind speed, pressure, and precipitation on the daily increase in COVID-19 cases in Mississippi, USA, during the period from January 2020 to August 2021. A machine learning model was used to predict COVID-19 cases and implement preventive measures if necessary. A statistical analysis using Python programming showed that the humidity ranged from 56% to 78%, and COVID-19 cases increased from 634 to 3546. Negative correlations were found between temperature and COVID-19 incidence rate (−0.22) and between humidity and COVID-19 incidence rate (−0.15). The linear regression model showed the model linear coefficients to be 0.92 and −1.29, respectively, with the intercept being 55.64. For the test dataset, the R 2 score was 0.053. The statistical analysis and machine learning show that there is no linear dependence of temperature and humidity with the COVID-19 incidence rate.

Suggested Citation

  • Francis Tuluri & Reddy Remata & Wilbur L. Walters & Paul. B. Tchounwou, 2022. "Application of Machine Learning to Study the Association between Environmental Factors and COVID-19 Cases in Mississippi, USA," Mathematics, MDPI, vol. 10(6), pages 1-9, March.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:6:p:850-:d:766279
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    References listed on IDEAS

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    1. Colin J. Carlson & Ana C. R. Gomez & Shweta Bansal & Sadie J. Ryan, 2020. "Misconceptions about weather and seasonality must not misguide COVID-19 response," Nature Communications, Nature, vol. 11(1), pages 1-4, December.
    2. Malki, Zohair & Atlam, El-Sayed & Hassanien, Aboul Ella & Dagnew, Guesh & Elhosseini, Mostafa A. & Gad, Ibrahim, 2020. "Association between weather data and COVID-19 pandemic predicting mortality rate: Machine learning approaches," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
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