Recurrent High Order Neural Networks Identification for Infectious Diseases

Abstract

Infectious diseases are causes of morbidity and mortality worldwide. Mathematical models can serve as a central tool to predict the kinetic of different infections. However, the development of mechanistic models and their parameter estimation are difficult tasks. Using Recurrent High Order Neural Networks (RHONNs) trained with an algorithm based on the extended Kalman filter (EKF), we separately identified influenza A virus (IAV) and HIV dynamics. To this end, we considered within-host mathematical models of IAV and HIV as unknown signals to the RHONNs. Simulations results reported that for both infections, RHONNs are able to identify the within-host model dynamics. Results provide promising guidelines to tackle the problem of model identification of infectious diseases, serving for future model based control strategies of viral infections.