Application of Machine Learning to Study the Association between Environmental Factors and COVID-19 Cases in Mississippi, USA
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,...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/10/6/850 |
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| author | Francis Tuluri Reddy Remata Wilbur L. Walters Paul. B. Tchounwou |
| author_facet | Francis Tuluri Reddy Remata Wilbur L. Walters Paul. B. Tchounwou |
| author_sort | Francis Tuluri |
| collection | DOAJ |
| description | 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<sup>2</sup> 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. |
| first_indexed | 2024-03-09T13:25:58Z |
| format | Article |
| id | doaj.art-16162953a1c5451d9cae7fd64b083ced |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-09T13:25:58Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-16162953a1c5451d9cae7fd64b083ced2023-11-30T21:23:03ZengMDPI AGMathematics2227-73902022-03-0110685010.3390/math10060850Application of Machine Learning to Study the Association between Environmental Factors and COVID-19 Cases in Mississippi, USAFrancis Tuluri0Reddy Remata1Wilbur L. Walters2Paul. B. Tchounwou3Department of Industrial Systems & Technology, Jackson State University, Jackson, MS 39217, USADepartment of Atmospheric Sciences, Jackson State University, Jackson, MS 39217, USACollege of Sciences, Engineering & Technology, Jackson State University, Jackson, MS 39217, USADepartment of Biology, Jackson State University, Jackson, MS 39217, USABecause 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<sup>2</sup> 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.https://www.mdpi.com/2227-7390/10/6/850Python programmingmachine learninglinear correlationlinear regression modelCOVID-19 |
| spellingShingle | Francis Tuluri Reddy Remata Wilbur L. Walters Paul. B. Tchounwou Application of Machine Learning to Study the Association between Environmental Factors and COVID-19 Cases in Mississippi, USA Mathematics Python programming machine learning linear correlation linear regression model COVID-19 |
| title | Application of Machine Learning to Study the Association between Environmental Factors and COVID-19 Cases in Mississippi, USA |
| title_full | Application of Machine Learning to Study the Association between Environmental Factors and COVID-19 Cases in Mississippi, USA |
| title_fullStr | Application of Machine Learning to Study the Association between Environmental Factors and COVID-19 Cases in Mississippi, USA |
| title_full_unstemmed | Application of Machine Learning to Study the Association between Environmental Factors and COVID-19 Cases in Mississippi, USA |
| title_short | Application of Machine Learning to Study the Association between Environmental Factors and COVID-19 Cases in Mississippi, USA |
| title_sort | application of machine learning to study the association between environmental factors and covid 19 cases in mississippi usa |
| topic | Python programming machine learning linear correlation linear regression model COVID-19 |
| url | https://www.mdpi.com/2227-7390/10/6/850 |
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