Abstract
•A hybrid algorithm of Genetics Optimization and Sequential Quadratic Programming is designed.•Constructed and analyzed the mathematical model for the dynamics of Lassa fever in Nigeria.•The log sigmoid function as an objective function based on mean squared error is constructed.•The trustworthiness, resilience, and effectiveness of the recommended framework is evaluated.
This paper presents an innovative artificial neural networks (ANNs) based hybrid algorithm of genetics optimization and sequential quadratic programming (AGOSQP) to construct the mathematical model for the dynamics of Lassa fever (DLF) in Nigeria. The model designated by the transmission of disease between two populations: human population i.e. susceptible Sh, exposed Eh, infectious Ih and recovered Rh humans and rodent population i.e. susceptible Sr and infectious Ir rodents. The log sigmoid function as an objective function based on mean squared error is constructed to optimize AGOSQP where genetic algorithm work as global searching optimization and SQP serve as the local searching optimization. To assess the correctness, robustness and convergence stability, the comparison between state of art Adam method and proposed AGOSQP is established. The Theil’s inequality coefficient (TIC), root mean square error (RMSE) and mean absolute deviation (MAD) are also computed to authenticate the efficiency of proposed AGOSQP to solve the model for the dynamics of Lassa fever.