The study underscores the critical role of symptom awareness and compliance with preventive measures in effectively reducing the transmission rates of infectious diseases.

A novel SEI1I2I3QCR model has been introduced, which utilizes fractional-order delay differential equations to improve the understanding of COVID-19 dynamics.

By allowing for latency periods and the progression of infection, the model provides greater accuracy in simulating disease transmission and its oscillations.

This research aims to address gaps in the literature regarding COVID-19 transmission dynamics and control measures, offering valuable insights for future public health interventions.

Numerical simulations validate the model's effectiveness, as it closely aligns with actual COVID-19 data from China, showcasing its potential to guide pandemic response strategies.

Stability analyses within the study emphasize the significance of fractional order and delay parameters in managing the spread of infectious diseases.

Traditional epidemic models, such as SIR, have been expanded to include additional compartments, allowing for a more detailed representation of disease progression among both asymptomatic and symptomatic individuals.

Recent research highlights the increasing importance of advanced mathematical models in managing infectious disease outbreaks, especially during global health crises.

This model incorporates fractional calculus, which accounts for memory effects and time delays, thereby reflecting the complex transmission dynamics associated with COVID-19.

The application of optimal control theory within the model simulates non-pharmaceutical interventions (NPIs) such as quarantine and awareness campaigns, showing a significant reduction in infection rates.