Cocirculation of infectious diseases on networks

Area of Science:

• Epidemiology
• Network Science
• Mathematical Modeling

Background:

• Understanding the spread of multiple infectious diseases in populations is crucial.
• Existing models often struggle to capture the complex interactions and immunity dynamics between co-circulating diseases.
• Network structures significantly influence disease transmission patterns.

Purpose of the Study:

• To develop a simplified mathematical model for analyzing the dynamics of multiple diseases spreading simultaneously in a network.
• To investigate how disease-specific transmission and recovery rates, along with cross-immunity, affect overall infection dynamics.
• To demonstrate the model's adaptability beyond infectious diseases, such as for belief or technology adoption.

Main Methods:

• Utilized a static configuration model network to represent population structure.
• Developed a low-dimensional system of ordinary differential equations (ODEs) to describe disease spread.
• Incorporated disease-specific transmission and recovery rates.
• Modeled permanent immunity conferred by one disease against others.

Main Results:

• A parsimonious ODE model was derived that effectively captures the global dynamics of multiple competing diseases.
• The model highlights the strong dependence of infection dynamics on initial conditions.
• Demonstrated the model's flexibility by illustrating the spread of an infectious disease preventable by behavior change.

Conclusions:

• The developed ODE model provides a powerful and adaptable tool for studying multi-disease dynamics in networks.
• The model's simplicity allows for intuitive understanding of complex epidemiological processes.
• Findings have implications for public health interventions and understanding the spread of various social or technological contagions.