Modeling the Impact of Superspreaders in Respiratory Disease Transmission Dynamics

Respiratory diseases and the severity associated with infections by human respiratory viruses contribute significantly to global morbidity, mortality, and economic burden. In particular, superspreader individuals pose a significant public health challenge in the transmission of respiratory viruses. This study focuses on the formulation, analysis, and simulation of a deterministic SI1I2HCR compartmental model to explore the effect of superspreaders on the dynamics of respiratory disease. The qualitative analysis begins with the determination of DFE and EE, as well as stability, and proceeds to investigate the bifurcation and sensitivity analysis of the model. The simulation was performed using the Runge–Kutta method of order four–five. The results of the study show that reducing the transmission rate of superinfectious individuals and increasing the hospitalization rate of infected individuals as well as recovery rates significantly reduce disease spread. In addition, increasing population immunity and the treatment administered to patients in the ICU and hospitalized individuals also control the spread of the disease. We recommend that policymakers and government stakeholders prioritize immunization and treatment programs, increase public awareness, and enhance early detection through genome sequencing and data-driven surveillance to identify superspreaders and reduce the burden of disease outbreaks.