This research introduces a novel fractional-order mathematical model to simulate the spread of COVID-19, incorporating lockdown measures. The model consists of four compartments: susceptible ($S(t)$), infected ($I(t)$), treated ($T(t)$), and recovered ($R(t)$), with the susceptible category further divided into $S_1(t)$ and $S_2(t)$. The study investigates the positivity and boundedness of the solution, determines equilibrium points, and conducts stability analysis. The Laplace Adomian decomposition method (LADM) is employed to solve the model, and numerical simulations are conducted to compare with real data from Italy. The results show that lockdown measures significantly reduce the spread of the virus, leading to a more stable situation. The fractional-order terms also play a crucial role in stabilizing the system. The study highlights the effectiveness of the proposed model in understanding and simulating the dynamics of the COVID-19 pandemic.This research introduces a novel fractional-order mathematical model to simulate the spread of COVID-19, incorporating lockdown measures. The model consists of four compartments: susceptible ($S(t)$), infected ($I(t)$), treated ($T(t)$), and recovered ($R(t)$), with the susceptible category further divided into $S_1(t)$ and $S_2(t)$. The study investigates the positivity and boundedness of the solution, determines equilibrium points, and conducts stability analysis. The Laplace Adomian decomposition method (LADM) is employed to solve the model, and numerical simulations are conducted to compare with real data from Italy. The results show that lockdown measures significantly reduce the spread of the virus, leading to a more stable situation. The fractional-order terms also play a crucial role in stabilizing the system. The study highlights the effectiveness of the proposed model in understanding and simulating the dynamics of the COVID-19 pandemic.