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Comparative Study
. 2022 Feb 4;12(1):1964.
doi: 10.1038/s41598-022-05859-8.

A hierarchical study for urban statistical indicators on the prevalence of COVID-19 in Chinese city clusters based on multiple linear regression (MLR) and polynomial best subset regression (PBSR) analysis

Ali Cheshmehzangi  1   2   3 , Yujian Li  4 , Haoran Li  5 , Shuyue Zhang  5 , Xiangliang Huang  5 , Xu Chen  5 , Zhaohui Su  6 , Maycon Sedrez  5   7 , Ayotunde Dawodu  5   7
Affiliations
Comparative Study

A hierarchical study for urban statistical indicators on the prevalence of COVID-19 in Chinese city clusters based on multiple linear regression (MLR) and polynomial best subset regression (PBSR) analysis

Ali Cheshmehzangi et al. Sci Rep. .

Abstract

With evidence-based measures, COVID-19 can be effectively controlled by advanced data analysis and prediction. However, while valuable insights are available, there is a shortage of robust and rigorous research on what factors shape COVID-19 transmissions at the city cluster level. Therefore, to bridge the research gap, we adopted a data-driven hierarchical modeling approach to identify the most influential factors in shaping COVID-19 transmissions across different Chinese cities and clusters. The data used in this study are from Chinese officials, and hierarchical modeling conclusions drawn from the analysis are systematic, multifaceted, and comprehensive. To further improve research rigor, the study utilizes SPSS, Python and RStudio to conduct multiple linear regression and polynomial best subset regression (PBSR) analysis for the hierarchical modeling. The regression model utilizes the magnitude of various relative factors in nine Chinese city clusters, including 45 cities at a different level of clusters, to examine these aspects from the city cluster scale, exploring the correlation between various factors of the cities. These initial 12 factors are comprised of 'Urban population ratio', 'Retail sales of consumer goods', 'Number of tourists', 'Tourism Income', 'Ratio of the elderly population (> 60 year old) in this city', 'population density', 'Mobility scale (move in/inbound) during the spring festival', 'Ratio of Population and Health facilities', 'Jobless rate (%)', 'The straight-line distance from original epicenter Wuhan to this city', 'urban per capita GDP', and 'the prevalence of the COVID-19'. The study's results provide rigorously-tested and evidence-based insights on most instrumental factors that shape COVID-19 transmissions across cities and regions in China. Overall, the study findings found that per capita GDP and population mobility rates were the most affected factors in the prevalence of COVID-19 in a city, which could inform health experts and government officials to design and develop evidence-based and effective public health policies that could curb the spread of the COVID-19 pandemic.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Nine representative city clusters highlighted in China’s City Cluster Plan (Source: The map of China comes from the National Platform for Common Geospatial Information Services in China. The map is generated from this available database and relevant clusters operation is completed on Powerpoint).
Figure 2
Figure 2
Overview of methodology and model analysis.
Figure 3
Figure 3
Flow chart of multiple linear regression analysis.
Figure 4
Figure 4
Flow chart for best subset regression analysis by R.
Figure 5
Figure 5
Prevalence rates in 45 cities of nine clusters and different tier level orders.
Figure 6
Figure 6
The Ridge paths of parameters.
See this image and copyright information in PMC

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