Pasha Deepro F, Lundeen Alex, Yeasmin Dilruba, Pasha M Fayzul K
Clovis North Educational Center (CNEC), Fresno, CA, 93730, USA.
Department of Civil and Geomatics Engineering, California State University, Fresno, CA, 93740, USA.
Case Stud Chem Environ Eng. 2021 Jun;3:100067. doi: 10.1016/j.cscee.2020.100067. Epub 2020 Dec 11.
Considering system theory, the socio-economic variables that constitute a society should be able to capture the system response such as the number of weekly COVID-19 cases. A numerical approach has been presented in this paper to answer two vital questions; which variables are more important and how many variables are needed to capture the dynamics of the spread. Using the theory of least squares regression, two types of problems have been set up and solved using multilinear regression (MLR) and nonlinear powered function known as NLR in this study. Numerical techniques were applied to pre- and post-process the data and the vast number of outputs. Total 43 socio-economic and meteorological variables from 31 counties in California in the United States resulted about 37.4 millions of combinations for the analysis. Results show that variables related to total population, household income, occupation, and transportation are more important than the others. The frequency of having higher correlation for a variable increases as more variables are combined with it. Similarly, correlation increases as the number of variables in a combination increases. Some 5- variable combinations can capture the dynamics of the spread with higher accuracy having correlation coefficient as high as 0.985.
从系统理论的角度来看，构成一个社会的社会经济变量应该能够反映系统响应，比如每周的新冠病毒病例数。本文提出了一种数值方法来回答两个关键问题：哪些变量更为重要，以及需要多少变量来捕捉传播动态。利用最小二乘回归理论，本研究设置并使用多元线性回归（MLR）和非线性幂函数（本研究中称为NLR）解决了两类问题。应用数值技术对数据和大量输出结果进行预处理和后处理。美国加利福尼亚州31个县的43个社会经济和气象变量产生了约3740万种组合用于分析。结果表明，与总人口、家庭收入、职业和交通相关的变量比其他变量更为重要。一个变量与更多变量组合时，其具有较高相关性的频率会增加。同样，随着组合中变量数量的增加，相关性也会增加。一些包含5个变量的组合能够以高达0.985的相关系数更准确地捕捉传播动态。
