Integrating fractional-order SEI1I2I3QCR model with awareness and non-pharmaceutical interventions for optimal COVID-19 pandemic.

Abstract

Infectious diseases like COVID-19 continue to pose critical challenges globally, underscoring the need for effective control strategies that go beyond traditional vaccinations and treatments. This study introduces an advanced SEI1I2I3QCR model, uniquely incorporating fractional-order delay differential equations to account for latency periods and dynamic transmission patterns of COVID-19, improving accuracy in capturing disease progression and peak oscillations. Stability analyses of the model reveal the critical role of delay and fractional order parameters in managing disease dynamics. Additionally, we applied optimal control theory to simulate non-pharmaceutical interventions, such as quarantine and awareness campaigns, demonstrating a notable reduction in infection rates. Numerical simulations align the model closely with real-world COVID-19 data from China, validating its utility in guiding pandemic response strategies. Our findings emphasize the significance of integrating time-delay factors and fractional calculus in epidemic modeling, offering a novel framework for pandemic management through targeted, cost-effective control measures.