Modeling the spread of multiple concurrent contagions on networks

Area of Science:

• Network Science
• Epidemiology
• Computational Social Science

Background:

• Contagion spread dynamics are well-studied in communication networks.
• Existing models often focus on single contagions or specific network types.

Purpose of the Study:

• To propose a general stochastic model for concurrent spread of multiple contagions in complex networks.
• To provide a unified framework encompassing various epidemic and rumor spreading models.

Main Methods:

• Developed a discrete-time stochastic model with spontaneous and contact-induced state change mechanisms.
• Incorporated an approximation for state transition probabilities for numerical tractability.
• Derived corresponding continuous-time equations.

Main Results:

• The model handles multiple concurrent contagions and finite time steps with potential multiple infections.
• It offers a unified approach, including well-known models as special cases.
• The model naturally captures spreading processes in multiplex networks.

Conclusions:

• The proposed general model provides a versatile tool for analyzing complex contagion dynamics.
• It unifies diverse spreading phenomena and is applicable to various network structures, including multiplex networks.