%D 2016
%R 10.1109/IC3INA.2015.7377762
%O cited By 4; Conference of International Conference on Computer, Control, Informatics and Its Applications, IC3INA 2015 ; Conference Date: 5 October 2015 Through 7 October 2015; Conference Code:118992
%L scholars7264
%J Proceeding - 2015 International Conference on Computer, Control, Informatics and Its Applications: Emerging Trends in the Era of Internet of Things, IC3INA 2015
%K Bandpass filters; Distributed computer systems; Dynamical systems; Information science; Markov processes; Monte Carlo methods; Nonlinear dynamical systems; Signal filtering and prediction; Target tracking, Bayesian frameworks; Density evaluation; Disease incidence; Estimation methodologies; Metropolis-Hastings algorithm; Metropolis-Hastings step; Particle filter; Posterior distributions, Parameter estimation
%X We address the issue of parameter estimation for nonlinear dynamical systems obtained as a model for dengue disease incidence. A Bayesian framework of estimation is adopted. Parameter estimation is performed using a Metropolis Hastings algorithm in which the target distribution of the resulting Markov chain equals the posterior distribution of unknown parameters. Intermediate predictive and filtering density evaluations required, within each Metropolis-Hastings step are evaluated using the particle filters (PF). The methodology is used to estimate unknown parameters governing the evolution of an underlying state space representing the dynamics of the force of infection. We illustrate our estimation methodology on dengue incidences collected from 2009 - 2014 for the district of Gombak in Selangor, Malaysia. © 2015 IEEE.
%P 143-147
%T Parameter estimation for nonlinear disease dynamical system using particle filter
%A M.J. Ur Rehman
%A S.C. Dass
%A V.S. Asirvadam
%A A. Adly
%I Institute of Electrical and Electronics Engineers Inc.