Vaccination Impact on Artificial Neural Networking of Infectious Diseases

We develop and rigorously examine a deterministic compartmental model for HBV vaccination. The optimal control problem associated with the co-infection model, To mitigate both infections and the related costs of protective and treatment interventions, an optimal control analysis was conducted for the HBV and TB co-infection model, utilizing Pontryagin’s minimum principle. Following this, numerical simulations with various combinations of efforts were executed to evaluate the effectiveness of protective measures alongside treatment for both diseases. The simulations highlight the critical need for programs that enhance protection for both diseases in conjunction with treatment to effectively reduce the prevalence of co-infection in the population. The results suggest that a synergistic approach combining protective and treatment- focused optimal control strategies can significantly lower the incidence of HBV and TB co-infection, with protective measures demonstrating greater efficacy than treatment measures for individuals affected by both conditions. Additionally, a sensitivity analysis was conducted to assess the significance of revealing sensitive factor concerning the effective transmission rate of HBV and is crucial for controlling the disease’s spread. The analysis conducted through Latin Hypercube sampling and PRCC indicates that the disease transmission rate, the proportion of acutely infected individuals who progress to chronic infection, the development rate of symptomatic chronic carriers, and the occurrence of disease complications are the most significant factors affecting disease dynamics.