Fractional-Order SEIQRDP Model for Simulating the Dynamics of COVID-19 Epidemic

Area of Science:

• Epidemiology
• Mathematical Biology
• Fractional Calculus

Background:

• Coronavirus disease (COVID-19) is a highly contagious disease that rapidly spread globally starting in late 2019.
• Understanding and forecasting the pandemic's trajectory is crucial for public health.
• Existing epidemiological models are essential tools for tracking infectious disease dynamics.

Purpose of the Study:

• To propose and analyze a novel fractional-order Susceptible-Exposed-Infected-Quarantined-Recovered-Death-Insusceptible (SEIQRDP) model for COVID-19.
• To leverage the benefits of fractional calculus for characterizing pandemic growth.
• To investigate the memory effect and sub-diffusion processes in disease transmission.

Main Methods:

• Development of a fractional-order SEIQRDP mathematical model.
• Application of fractional differential equations to epidemiological modeling.
• Validation of the model using real-world COVID-19 data from China, Italy, and France.

Main Results:

• The fractional-order SEIQRDP model demonstrated potential in predicting COVID-19 pandemic dynamics.
• Model validation with regional data confirmed its applicability.
• The study highlights the significance of fractional calculus in capturing disease spread characteristics.

Conclusions:

• Fractional-order epidemiological models offer a robust framework for analyzing and predicting infectious diseases like COVID-19.
• These models can provide valuable insights for developing effective pandemic control measures.
• The non-locality and memory effects inherent in fractional calculus enhance pandemic modeling accuracy.