Analysis of a Delayed Mathematical Model for Secondary DENV Dynamics With Multitarget Cells and Treatment Strategies

Dengue fever, transmitted primarily by Aedes mosquitoes, poses a significant public health challenge worldwide. This study introduces an analytical framework for examining the dynamics of subsequent dengue virus infections, considering multiple target cells and discrete time delays. Secondary infections are known to influence disease severity and transmission dynamics, emphasizing the importance of understanding their dynamics. Two distinct categories of discrete time delays are incorporated into the model to capture the period needed for the generation of infected cells as well as the maturation interval of newly produced virions. We established the model’s well-posedness and determined three threshold parameters defining the existence and stability conditions of its four steady states. Through the formulation of a Lyapunov function and the application of the Lyapunov–LaSalle asymptotic stability theorem, we conducted a global stability analysis of all steady states. The proposed framework incorporates two hypothetical therapeutic interventions: one inhibiting viral entry into susceptible cells and another suppressing viral production from infected cells. Numerical simulations support the theoretical analysis and indicate that their combined use enhances viral reduction by limiting replication and promoting clearance. The results further show that incorporating time delays reduces the basic reproduction number and slows viral expansion, suggesting that delaying infection progression and virion maturation may facilitate viral elimination. This suggests that therapeutic strategies aimed at prolonging intracellular delay phases may reduce the reproduction number below unity, thereby promoting viral elimination within the host. This also provides insights into developing therapeutic strategies aimed at prolonging intracellular delay phases. The minimum antiviral efficacy required for viral elimination is derived. Notably, neglecting multiple classes of target cells may underestimate the required treatment level, whereas omitting time delays may lead to its overestimation. These findings highlight the importance of including both features for more accurate modeling.