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It is significant to investigate the transmission dynamics of vector-borne infection because it has a global impact, can help predict and prevent future outbreaks, is important for understanding the impact of climate change on public health, can lead to more effective control strategies, and can improve our comprehension of the dynamics of these infections. Our paper presents a new model for chikungunya virus infection, which considers treatment and vaccination, using the Atangana-Baleanu derivative within the framework of the Caputo definition. First of all, we examine the positivity and uniqueness of the solution for the model. Then, we find out the fundamental results of the suggested model, such as the steady-state without infection and the R0 value that indicates endemicity pertaining to the system. Furthermore, we verify the local asymptotic stability of the infection-free steady-state. Through the application of fixed-point theory, we demonstrate the existence of the solution and propose a numerical methodology to investigate the dynamic behavior of the model. Finally, we performed simulations to demonstrate the effects of vaccination, transmission probability, treatment, and index of memory on the system's solution pathways. The findings of this research suggest the most sensitive parameters of the system responsible for the control and prevention of the infection. Emphasizing the significance of the memory index as an attractive parameter, we propose its consideration to the policy makers as a control parameter for the prevention of the infection.
Jan et al. (Wed,) studied this question.