Parameter estimation for nonlinear disease dynamical system using particle filter

M. Javvad Ur Rehman; Sarat Chandra Dass; Vijanth Sagayan Asirvadam; Ahmed Adly

doi:10.1109/IC3INA.2015.7377762

Parameter estimation for nonlinear disease dynamical system using particle filter

M. Javvad Ur Rehman, Sarat Chandra Dass, Vijanth Sagayan Asirvadam, Ahmed Adly

School of Mathematical & Computer Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	2015 International Conference on Computer, Control, Informatics and its Applications (IC3INA)
Editors	Arnida L. Latifah
Publisher	IEEE
Pages	143-147
Number of pages	5
ISBN (Electronic)	9781479987733
DOIs	https://doi.org/10.1109/IC3INA.2015.7377762
Publication status	Published - 11 Jan 2016
Event	2015 International Conference on Computer, Control, Informatics and its Applications - Bandung, Indonesia Duration: 5 Oct 2015 → 7 Oct 2015

Conference

Conference	2015 International Conference on Computer, Control, Informatics and its Applications
Abbreviated title	IC3INA 2015
Country/Territory	Indonesia
City	Bandung
Period	5/10/15 → 7/10/15

Keywords

Dynamical System
Parameter Estimation
Particle Filter

ASJC Scopus subject areas

Computer Networks and Communications
Computer Science Applications
Information Systems
Control and Systems Engineering

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/IC3INA.2015.7377762

Cite this

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title = "Parameter estimation for nonlinear disease dynamical system using particle filter",

abstract = "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.",

keywords = "Dynamical System, Parameter Estimation, Particle Filter",

author = "{Ur Rehman}, {M. Javvad} and Dass, {Sarat Chandra} and Asirvadam, {Vijanth Sagayan} and Ahmed Adly",

year = "2016",

month = jan,

day = "11",

doi = "10.1109/IC3INA.2015.7377762",

language = "English",

pages = "143--147",

editor = "Latifah, {Arnida L.}",

booktitle = "2015 International Conference on Computer, Control, Informatics and its Applications (IC3INA)",

publisher = "IEEE",

address = "United States",

note = "2015 International Conference on Computer, Control, Informatics and its Applications, IC3INA 2015 ; Conference date: 05-10-2015 Through 07-10-2015",

}

Ur Rehman, MJ, Dass, SC, Asirvadam, VS & Adly, A 2016, Parameter estimation for nonlinear disease dynamical system using particle filter. in AL Latifah (ed.), 2015 International Conference on Computer, Control, Informatics and its Applications (IC3INA). IEEE, pp. 143-147, 2015 International Conference on Computer, Control, Informatics and its Applications, Bandung, Indonesia, 5/10/15. https://doi.org/10.1109/IC3INA.2015.7377762

Parameter estimation for nonlinear disease dynamical system using particle filter. / Ur Rehman, M. Javvad; Dass, Sarat Chandra; Asirvadam, Vijanth Sagayan et al.
2015 International Conference on Computer, Control, Informatics and its Applications (IC3INA). ed. / Arnida L. Latifah. IEEE, 2016. p. 143-147.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Parameter estimation for nonlinear disease dynamical system using particle filter

AU - Ur Rehman, M. Javvad

AU - Dass, Sarat Chandra

AU - Asirvadam, Vijanth Sagayan

AU - Adly, Ahmed

PY - 2016/1/11

Y1 - 2016/1/11

N2 - 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.

AB - 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.

KW - Dynamical System

KW - Parameter Estimation

KW - Particle Filter

UR - http://www.scopus.com/inward/record.url?scp=84963645981&partnerID=8YFLogxK

U2 - 10.1109/IC3INA.2015.7377762

DO - 10.1109/IC3INA.2015.7377762

M3 - Conference contribution

AN - SCOPUS:84963645981

SP - 143

EP - 147

BT - 2015 International Conference on Computer, Control, Informatics and its Applications (IC3INA)

A2 - Latifah, Arnida L.

PB - IEEE

T2 - 2015 International Conference on Computer, Control, Informatics and its Applications

Y2 - 5 October 2015 through 7 October 2015

ER -

Parameter estimation for nonlinear disease dynamical system using particle filter

Abstract

Conference

Keywords

ASJC Scopus subject areas

UN SDGs

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