Modelling the impact of awareness campaigns on HPV transmission dynamics: a neural network-enhanced mathematical model

Abstract

HPV is the most common sexually transmitted infection; most sexually active individuals are exposed to HPV at some point in their lives, and the majority of these infections resolve naturally through the immune system. However, persistent infection with high-risk HPV types is the leading cause of cervical cancer worldwide, underscoring the importance of vaccination and early detection in reducing disease burden. In this study, the effect of an awareness campaign on the dynamics of human papillomavirus infection has been investigated through a mathematical model using neural networks. The basic characteristic, boundedness, the endemic and disease-free equilibrium point, and the reproduction number of the model have been investigated. Using the reproduction number, the stability of the model is also investigated. Moreover, a sensitivity analysis is performed to identify the most sensitive parameter affecting the spread and control of HPV. The deterministic numerical solution of the HPV transmission model was first obtained using the ode45 solver based on the Runge-Kutta method; the resulting solution trajectories were then used as training data for a feedforward neural network, which serves as a surrogate approximation model to validate and reproduce the system dynamics. The numerical simulation examines mean square error, validation performance, gradient, and mu across varying levels of vaccination coverage, public awareness, and cervical infection rates. Numerical simulation suggests that vaccination and awareness about the risk of human papillomavirus infection play a crucial role in controlling the spread of the virus in the population.