Epidemic spreading on modular networks: The fear to declare a pandemic

Area of Science:

• Epidemiology
• Network Science
• Mathematical Modeling

Background:

• Global pandemics pose significant humanitarian and economic threats.
• Increased urbanization and international mobility have led to a rise in pandemic frequency.
• Accurate models are crucial for estimating worldwide pandemic probability.

Purpose of the Study:

• To propose and analyze a model for disease spread in complex, modular networks.
• To investigate the impact of 'bridge nodes' connecting communities on disease propagation.
• To understand how network structure influences pandemic potential.

Main Methods:

• Developed a disease spreading model on a structural modular complex network.
• Analyzed the effect of the number of bridge nodes (n) on disease transmission dynamics.
• Utilized link percolation theory for global-scale pandemic probability estimation.

Main Results:

• Disease spread dynamics at a global scale resemble an infectious transmission process between communities.
• Global infectious and recovery times depend on community structure and the number of bridge nodes (n).
• Increasing 'n' near the critical point leads to widespread disease but low recovery within communities.
• Pandemic probability increases abruptly as n approaches infinity, complicating alert systems.

Conclusions:

• The number of bridge nodes significantly influences global pandemic risk.
• Link percolation theory provides a viable framework for estimating pandemic probability.
• Complex network structures can lead to abrupt increases in pandemic likelihood, posing challenges for public health response.