COVID-19 dynamics and immune response: Linking within-host and between-host dynamics.

Area of Science:

• Epidemiology
• Mathematical Biology
• Immunology

Background:

• COVID-19 pandemics necessitate robust mathematical models for control.
• Existing models often fail to integrate within-host and between-host disease dynamics.
• Understanding the interplay between host immunity and transmission is critical.

Purpose of the Study:

• To develop a novel mathematical model linking within-host and between-host factors of COVID-19.
• To incorporate immune response dynamics into the epidemiological model.
• To evaluate the efficacy of various intervention strategies.

Main Methods:

• A compartmental mathematical model utilizing a fractional derivative (Caputo-Fabrizio) was developed.
• Model properties were analyzed to ensure a unique solution.
• Parameter estimation was performed using real-world data, followed by sensitivity analysis.
• Numerical simulations were conducted to assess intervention impacts.

Main Results:

• The model demonstrates that within-host dynamics significantly influence human-to-human transmission.
• Sensitivity analysis identified key parameters affecting disease spread.
• Simulations compared various intervention strategies based on averted deaths.

Conclusions:

• No single intervention strategy is sufficient to control COVID-19 spread.
• A combination of treatments boosting the immune system, inhibiting viral production, and adhering to public health protocols is essential.
• The integrated model provides a framework for understanding and managing infectious disease dynamics.