Analyzing the Co-Dynamics of COVID-19 Strains with Early Detection Through the Fractional Laplace Adomian Decomposition Method

Abstract

The emergence of multiple COVID-19 strains has been the greatest challenge since the initial outbreak. A fractional-order mathematical model is formulated to investigate the co-dynamics of multiple COVID-19 strains with memory effects through the Caputo fractional derivative. The model is based on an extended SEIR framework including two exposed parameters named as early detection to quarantine compartments to reflect realistic intervention strategies. Fractional Laplace Adomian Decomposition Method (FLADM) has been used to solve the resulting system and analyzed the behavior for various values of the fractional order α. The results show strong agreement between LADM and the fractional Runge-Kutta fourth-order method at  which is indicating the accuracy and efficiency of our approach. This model provides a refined perspective on short-term epidemic scenarios by capturing the memory-dependent nature of disease transmission and intervention by offering valuable insights for public health planning and control strategies.