Abstract
We identified four distinct clusters of 151 countries based on COVID-19 prevalence rate from 1 February 2020 to 29 May 2021 by performing nonparametric K-means cluster analysis (KmL). We forecasted future development of the clusters by using a nonlinear 3-parameter logistic (3PL) model, and found that peak points of development are the latest for Cluster I and earliest for Cluster IV. Based on partial least squares structural equation modeling (PLS-SEM) for the first twenty weeks after 1 February 2020, we found that the prevalence rate of COVID-19 has been significantly influenced by major elements of human systems. Better health infrastructure, more restriction of human mobility, higher urban population density, and less urban environmental degradation are associated with lower levels of prevalence rate (PR) of COVID-19. The most striking discovery of this study is that economic development hindered the control of COVID-19 spread among countries in the early stage of the pandemic. Highlights: While richer countries have advantages in health and other urban infrastructures that may alleviate the prevalence rate of COVID-19, the combination of high economic development level and low restriction on human mobility has led to faster spread of the virus in the first 20 weeks after 1 February 2020.
Highlights
Received: 31 January 2022After the first recorded outbreak in Wuhan, China, in December 2019, COVID-19 spread rapidly and became a global pandemic that significantly changed many aspects of the earth, societies, and people
While higher economic development level is likely coupled with more advanced health infrastructure and less urban environmental degradation, with both factors possibly reducing the prevalence rate (PR) of COVID-19 severity, we find an opposite relationship
We identified four distinct clusters of 151 countries based on COVID-19 prevalence trajectories that had distinct dynamics in prevalence rate (PR)
Summary
After the first recorded outbreak in Wuhan, China, in December 2019, COVID-19 spread rapidly and became a global pandemic that significantly changed many aspects of the earth, societies, and people. While the medical community has been actively investigating the underlying mechanisms of the pandemic and has been developing vaccines, the general public, aided by the available data and reporting of the media, became aware of differences in pandemic severity among global nations These differences are often attributed to disparities in human systems at the national level, including health infrastructure, socioeconomic status, built environment characteristics, cultural attitudes, and institutional actions such as government initiatives and policies on restrictions on mobility [1,2,3,4,5]. This paper has two specific objectives: (1) to understand and predict the dynamic evolution of the pandemic at the global level based on the data on confirmed cases in 151 countries from early in the pandemic, 1 February 2020, when data became available daily, to 29 May 2021; and (2) to identify and quantify the co-evolved interrelationships between key human system forces for COVID-19 for the first 20 weeks after 1 February. Details are provided “Data and Methods”, on the cluster analysis, 3PL modeling, and PLS-SEM
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