Tuluri Francis, Remata Reddy, Walters Wilbur L, Tchounwou Paul B
Department of Industrial Systems & Technology, Jackson State University, Jackson, MS 39217, USA.
Department of Atmospheric Sciences, Jackson State University, Jackson, MS 39217, USA.
Mathematics (Basel). 2022 Mar 2;10(6). doi: 10.3390/math10060850. Epub 2022 Mar 8.
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 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.
由于新冠病毒病（COVID-19）对人类健康的大规模影响，目前正在进行多项调查，以了解影响该疾病传播和传染的潜在机制。本研究旨在评估2020年1月至2021年8月期间，诸如温度、湿度、露点、风速、气压和降水量等选定环境因素对美国密西西比州COVID-19病例每日新增数量的影响。使用机器学习模型预测COVID-19病例，并在必要时实施预防措施。使用Python编程进行的统计分析表明，湿度范围为56%至78%，COVID-19病例从634例增加到3546例。发现温度与COVID-19发病率之间呈负相关（-0.22），湿度与COVID-19发病率之间也呈负相关（-0.15）。线性回归模型显示模型线性系数分别为0.92和-1.29，截距为55.64。对于测试数据集，R分数为0.053。统计分析和机器学习表明，温度和湿度与COVID-19发病率之间不存在线性相关性。
