Visualization of dengue incidences for vulnerability using K-means

Nirbhay Mathur; Vijanth S. Asirvadam; Sarat C. Dass; Balvinder Singh Gill

doi:10.1109/ICSIPA.2015.7412255

Visualization of dengue incidences for vulnerability using K-means

Nirbhay Mathur^*, Vijanth S. Asirvadam, Sarat C. Dass, Balvinder Singh Gill

^*Corresponding author for this work

School of Mathematical & Computer Sciences

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

4 Citations (Scopus)

Abstract

Dengue is the world's most rapidly spreading and geographically widespread arthropod-borne disease. Dengue epidemics are observed to be larger, more frequent and associated with more severe disease than they were in the past. To control the incidence of the disease, it is important to be able to identify the hot-spots localized regions of high incidences. This work focuses on identifying hot-spots of dengue using the K-means clustering algorithm. Data is collected from the state of Selangor in Malaysia from 2013 to 2014. Visualization of dengue vulnerability is obtained via Gaussian mixture models fitted using K-means algorithm. Results demonstrate the ability to render visualization for the vulnerability of dengue incidences on the basis of high density and low density cluster using Gaussian mixture and K-means algorithm.

Original language	English
Title of host publication	IEEE 2015 International Conference on Signal and Image Processing Applications, ICSIPA 2015 - Proceedings
Publisher	IEEE
Pages	569-573
Number of pages	5
ISBN (Electronic)	9781479989966
DOIs	https://doi.org/10.1109/ICSIPA.2015.7412255
Publication status	Published - 25 Feb 2016
Event	4th IEEE International Conference on Signal and Image Processing Applications 2015 - Kuala Lumpur, Malaysia Duration: 19 Oct 2015 → 21 Oct 2015

Conference

Conference	4th IEEE International Conference on Signal and Image Processing Applications 2015
Abbreviated title	ICSIPA 2015
Country/Territory	Malaysia
City	Kuala Lumpur
Period	19/10/15 → 21/10/15

Keywords

clustering
Dengue
Gaussian Mixture model
Geographical Information System (GIS)
K-means

ASJC Scopus subject areas

Computer Science Applications
Signal Processing

UN SDGs

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

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10.1109/ICSIPA.2015.7412255

Cite this

@inproceedings{90881462469f4692bc92691c21ed9423,

title = "Visualization of dengue incidences for vulnerability using K-means",

abstract = "Dengue is the world's most rapidly spreading and geographically widespread arthropod-borne disease. Dengue epidemics are observed to be larger, more frequent and associated with more severe disease than they were in the past. To control the incidence of the disease, it is important to be able to identify the hot-spots localized regions of high incidences. This work focuses on identifying hot-spots of dengue using the K-means clustering algorithm. Data is collected from the state of Selangor in Malaysia from 2013 to 2014. Visualization of dengue vulnerability is obtained via Gaussian mixture models fitted using K-means algorithm. Results demonstrate the ability to render visualization for the vulnerability of dengue incidences on the basis of high density and low density cluster using Gaussian mixture and K-means algorithm.",

keywords = "clustering, Dengue, Gaussian Mixture model, Geographical Information System (GIS), K-means",

author = "Nirbhay Mathur and Asirvadam, {Vijanth S.} and Dass, {Sarat C.} and Gill, {Balvinder Singh}",

year = "2016",

month = feb,

day = "25",

doi = "10.1109/ICSIPA.2015.7412255",

language = "English",

pages = "569--573",

booktitle = "IEEE 2015 International Conference on Signal and Image Processing Applications, ICSIPA 2015 - Proceedings",

publisher = "IEEE",

address = "United States",

note = "4th IEEE International Conference on Signal and Image Processing Applications 2015, ICSIPA 2015 ; Conference date: 19-10-2015 Through 21-10-2015",

}

Mathur, N, Asirvadam, VS, Dass, SC & Gill, BS 2016, Visualization of dengue incidences for vulnerability using K-means. in IEEE 2015 International Conference on Signal and Image Processing Applications, ICSIPA 2015 - Proceedings. IEEE, pp. 569-573, 4th IEEE International Conference on Signal and Image Processing Applications 2015, Kuala Lumpur, Malaysia, 19/10/15. https://doi.org/10.1109/ICSIPA.2015.7412255

TY - GEN

T1 - Visualization of dengue incidences for vulnerability using K-means

AU - Mathur, Nirbhay

AU - Asirvadam, Vijanth S.

AU - Dass, Sarat C.

AU - Gill, Balvinder Singh

PY - 2016/2/25

Y1 - 2016/2/25

N2 - Dengue is the world's most rapidly spreading and geographically widespread arthropod-borne disease. Dengue epidemics are observed to be larger, more frequent and associated with more severe disease than they were in the past. To control the incidence of the disease, it is important to be able to identify the hot-spots localized regions of high incidences. This work focuses on identifying hot-spots of dengue using the K-means clustering algorithm. Data is collected from the state of Selangor in Malaysia from 2013 to 2014. Visualization of dengue vulnerability is obtained via Gaussian mixture models fitted using K-means algorithm. Results demonstrate the ability to render visualization for the vulnerability of dengue incidences on the basis of high density and low density cluster using Gaussian mixture and K-means algorithm.

AB - Dengue is the world's most rapidly spreading and geographically widespread arthropod-borne disease. Dengue epidemics are observed to be larger, more frequent and associated with more severe disease than they were in the past. To control the incidence of the disease, it is important to be able to identify the hot-spots localized regions of high incidences. This work focuses on identifying hot-spots of dengue using the K-means clustering algorithm. Data is collected from the state of Selangor in Malaysia from 2013 to 2014. Visualization of dengue vulnerability is obtained via Gaussian mixture models fitted using K-means algorithm. Results demonstrate the ability to render visualization for the vulnerability of dengue incidences on the basis of high density and low density cluster using Gaussian mixture and K-means algorithm.

KW - clustering

KW - Dengue

KW - Gaussian Mixture model

KW - Geographical Information System (GIS)

KW - K-means

UR - http://www.scopus.com/inward/record.url?scp=84971607364&partnerID=8YFLogxK

U2 - 10.1109/ICSIPA.2015.7412255

DO - 10.1109/ICSIPA.2015.7412255

M3 - Conference contribution

AN - SCOPUS:84971607364

SP - 569

EP - 573

BT - IEEE 2015 International Conference on Signal and Image Processing Applications, ICSIPA 2015 - Proceedings

PB - IEEE

T2 - 4th IEEE International Conference on Signal and Image Processing Applications 2015

Y2 - 19 October 2015 through 21 October 2015

ER -

Visualization of dengue incidences for vulnerability using K-means

Abstract

Conference

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

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