An immuno-epidemiological model with non-exponentially distributed disease stage on complex networks

Area of Science:

• Epidemiology
• Mathematical Biology
• Network Science

Background:

• Traditional epidemic models often use exponential distributions for disease phases, simplifying analysis but limiting accuracy.
• Exponential assumptions struggle to represent complex within-host dynamics like drug concentration and viral load interactions.

Purpose of the Study:

• To develop and analyze an immuno-epidemiological model on complex networks that incorporates non-exponential disease duration distributions.
• To investigate the influence of within-host viral load on disease-induced mortality and its connection to between-host transmission.

Main Methods:

• Formulation of a hybrid model using ordinary differential equations and integral equations to link within- and between-host dynamics.
• Mathematical analysis to determine the existence and stability of model equilibria, dependent on the basic reproduction number.
• Numerical simulations to explore the impact of non-exponential distributions and network topology on epidemic patterns.

Main Results:

• The model demonstrates that non-exponential disease duration distributions significantly impact epidemic predictions.
• Network topology plays a crucial role in shaping the overall patterns of disease spread.
• The linkage between within-host viral load and disease-induced death is a key factor in transmission dynamics.

Conclusions:

• Non-exponential disease phase durations are essential for accurate epidemic modeling, especially when considering within-host processes.
• Complex network structures and their properties significantly influence epidemic outcomes.
• The developed model provides a more realistic framework for understanding disease dynamics and informing public health strategies.