Causality Analysis for COVID-19 among Countries Using Effective Transfer Entropy

Area of Science:

• Complex Systems Science
• Epidemiology
• Network Science

Background:

• The COVID-19 pandemic presented unprecedented global health challenges.
• Understanding international transmission dynamics is crucial for effective pandemic control.
• Existing methods for causality analysis have limitations in quantifying complex, nonlinear relationships.

Purpose of the Study:

• To analyze the causal relationships of COVID-19 spread among 70 countries.
• To quantify the strength and direction of causality using effective transfer entropy.
• To identify central countries and community structures within the global COVID-19 transmission network.

Main Methods:

• Effective transfer entropy was employed to quantify causalities between countries.
• A weighted directed network was constructed, with link weights representing causality strength.
• Network analysis techniques including eigenvector centrality, PageRank, and community detection were applied.

Main Results:

• The study successfully constructed a causality network revealing the strength of COVID-19 transmission links.
• Eigenvector centrality and PageRank identified key influential countries in the global network.
• Community detection grouped countries with densely interconnected transmission patterns.

Conclusions:

• Effective transfer entropy is a powerful tool for analyzing complex epidemiological causalities.
• The network analysis provides insights into the interconnectedness and influential nodes in global COVID-19 spread.
• Understanding these network structures can inform targeted public health interventions and resource allocation.