Mathematical Modeling of Influenza Dynamics: Integrating Seasonality and Gradual Waning Immunity

Area of Science:

• Epidemiology
• Mathematical Biology
• Infectious Disease Modeling

Background:

• Influenza virus dynamics are complex, influenced by seasonality and immunity.
• Previous models often addressed these factors separately.
• Accurate modeling is crucial for understanding and controlling influenza outbreaks.

Purpose of the Study:

• To develop a mathematical model that simultaneously incorporates seasonality and gradual waning immunity for influenza.
• To create a novel seasonal SIRn model for this purpose.
• To demonstrate the model's applicability to real-world influenza data.

Main Methods:

• Constructed a seasonal SIRn model with a periodic transmission rate to represent seasonality.
• Incorporated waning immunity by using multiple recovered subpopulations with age-dependent susceptibility.
• Calibrated the model using influenza infection data from 2010-2020.

Main Results:

• The developed mathematical model successfully integrates seasonality and waning immunity.
• Model calibration with real-world data demonstrated its feasibility and accuracy.
• The approach provides valuable insights into influenza transmission dynamics.

Conclusions:

• The proposed mathematical modeling approach is effective for studying influenza dynamics.
• Simultaneously considering seasonality and waning immunity enhances epidemiological models.
• This integrated approach can improve influenza surveillance and control strategies.