®®®® SIIA Público

Título del libro: Proceedings Of The International Joint Conference On Neural Networks
Título del capítulo: Recurrent High Order Neural Networks Identification for Infectious Diseases

Autores UNAM:
ESTEBAN ABELARDO HERNANDEZ VARGAS;
Autores externos:

Idioma:
Inglés
Año de publicación:
2018
Palabras clave:

Computer viruses; Disease control; Diseases; Extended Kalman filters; Viruses; Future models; HIV dynamics; Infectious disease; Influenza A virus; Mechanistic models; Model identification; Recurrent high-order neural networks; Viral infections; Recurrent neural networks


Resumen:

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. © 2018 IEEE.


Entidades citadas de la UNAM: