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The Coexistence of Infection Spread Patterns in the Global Dynamics of COVID-19 Dissemination

Hiroyasu Inoue, Wataru Souma, Yoshi Fujiwara

TL;DR

The results reveal that, although factors that increase infection risk lead to certain trends at the beginning of the pandemic, these trends dynamically changes over time due to socioeconomic factors, especially the introduction of countermeasures.

Abstract

The novel coronavirus SARS-CoV-2, commonly referred to as COVID-19, triggered the global pandemic. Although the nature of the international spread of infection is an important issue, extracting diffusion networks from observations is challenging because of its inherent complexity. In this paper, we investigate the process of infection worldwide, including time delays, based on global infection case data collected from January 3, 2020 to December 31, 2022. We approach the data with a complex Hilbert principal component analysis, which can consider not only the concurrent relationships between elements, but also the leading and lagging relationships. Then, we examine the interactions among countries by considering six factors: geography, population, GDP, stringency of countermeasures, vaccination rates, and government type. The results show two primary trends occurring in 2020 and in 2021-2022 and they interchange with each other. Specifically, European, highly populated, and democratic countries, i.e., countries with high mobility rates, show leading trends in 2020. In contrast, African and nondemocratic countries show leading trends in 2021-2022, followed by countries with high vaccination rates and advanced countermeasures. The results reveal that, although factors that increase infection risk lead to certain trends at the beginning of the pandemic, these trends dynamically changes over time due to socioeconomic factors, especially the introduction of countermeasures. The findings suggest that international efforts to promote countermeasures in developing countries can contribute to pandemic containment.

The Coexistence of Infection Spread Patterns in the Global Dynamics of COVID-19 Dissemination

TL;DR

The results reveal that, although factors that increase infection risk lead to certain trends at the beginning of the pandemic, these trends dynamically changes over time due to socioeconomic factors, especially the introduction of countermeasures.

Abstract

The novel coronavirus SARS-CoV-2, commonly referred to as COVID-19, triggered the global pandemic. Although the nature of the international spread of infection is an important issue, extracting diffusion networks from observations is challenging because of its inherent complexity. In this paper, we investigate the process of infection worldwide, including time delays, based on global infection case data collected from January 3, 2020 to December 31, 2022. We approach the data with a complex Hilbert principal component analysis, which can consider not only the concurrent relationships between elements, but also the leading and lagging relationships. Then, we examine the interactions among countries by considering six factors: geography, population, GDP, stringency of countermeasures, vaccination rates, and government type. The results show two primary trends occurring in 2020 and in 2021-2022 and they interchange with each other. Specifically, European, highly populated, and democratic countries, i.e., countries with high mobility rates, show leading trends in 2020. In contrast, African and nondemocratic countries show leading trends in 2021-2022, followed by countries with high vaccination rates and advanced countermeasures. The results reveal that, although factors that increase infection risk lead to certain trends at the beginning of the pandemic, these trends dynamically changes over time due to socioeconomic factors, especially the introduction of countermeasures. The findings suggest that international efforts to promote countermeasures in developing countries can contribute to pandemic containment.
Paper Structure (17 sections, 11 equations, 5 figures)

This paper contains 17 sections, 11 equations, 5 figures.

Table of Contents

  1. Introduction
  2. Data
  3. Methods
  4. Results and Discussion
  5. Conclusion
  6. Acknowledgements
  7. Author contributions statement

Figures (5)

  • Figure 1: Number of infected cases. Panel (a) shows the daily total number of infections worldwide. Panel (b) shows the number of cases in the top 20 most populous countries after removing the weekly trends with the state-space model, and Panel (c) shows the log ratio of the results in Panel (b). Panel (d) shows the legend for Panels (b) and (c). For Panels (b) and (c), the same figures for all countries are shown in SI Figures 1 and 2.
  • Figure 2: Scree plot of CHPCA and the RRS. The data cover the entire period. The error bars for the RRS indicate 1% significance level, i.e., 2.33 times the standard error from the mean.
  • Figure 3: Barycentres for each region in the first eigenvectors. The abscissa corresponds to the argument, and the ordinate corresponds to the absolute value (amplitude) of the eigenvector. Panels (a-d) represent the entire period, 2020, 2021, and 2022, respectively. Note that time progresses from right to left.
  • Figure 4: Barycentres for five population groups based on rank. The abscissa corresponds to the argument, and the ordinate corresponds to the absolute value (amplitude) of the eigenvector. Panels (a-d) represent the entire period, 2020, 2021, and 2022, respectively. Note that time progresses from right to left.
  • Figure 5: Barycentres for each of five groups by democracy index based on rank. The abscissa corresponds to the argument, and the ordinate corresponds to the absolute value (amplitude) of the eigenvector. Panels (a-d) represent the entire period, 2020, 2021, and 2022, respectively. Note that time progresses from right to left.