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 R2 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.
Highlights
The virus SARS-CoV-2 is a member of a large family of viruses called coronaviruses [1,2].As the incidence of Coronavirus Disease 2019 (COVID-19) began to increase rapidly across the world [3], the World Health Organization (WHO) declared a global pandemic on11 March 2020 [4].Similar to the coronavirus family, COVID-19 is an infectious disease, and humanto-human contact is the primary factor of transmission of the virus–by touching infected surfaces and mediating the infection through the mouth, nose, or eyes
Some machine learning methods are studied to compare their performance in terms of COVID-19 transmission forecasting [11]
Test the hypothesis that anwe increased out a linear regression model to run these variables so as to test the hypothesis that temperature and humidity would decrease the spread of COVID-19 cases
Summary
The virus SARS-CoV-2 is a member of a large family of viruses called coronaviruses [1,2].As the incidence of Coronavirus Disease 2019 (COVID-19) began to increase rapidly across the world [3], the World Health Organization (WHO) declared a global pandemic on11 March 2020 [4].Similar to the coronavirus family, COVID-19 is an infectious disease, and humanto-human contact is the primary factor of transmission of the virus–by touching infected surfaces and mediating the infection through the mouth, nose, or eyes. Machine learning is a non-invasive tool that acts on a large dataset of observations to find association features among the data. Machine learning can be applied to COVID-19 data to predict useful features from the complex data in contrast to using a traditional computation-based method. Machine learning with COVID-19 data can be used to deduce risk factors related to weather, air quality, social habits, demographics, and location. A recent surveys on applications of machine learning for the COVID-19 pandemic is provided by Kushwaha et al [8]. Hybrid machine learning methods are used to predict the time series of infected individuals and mortality rate [9]. Machine learning is utilized to accurately predict the risk for critical COVID-19 [10]. Some machine learning methods are studied to compare their performance in terms of COVID-19 transmission forecasting [11]
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