Generating Vulnerability Maps of Dengue Incidences for Petaling District in Malaysia

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

doi:10.1109/CSPA.2016.7515836

Generating Vulnerability Maps of Dengue Incidences for Petaling District in Malaysia

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

School of Mathematical & Computer Sciences

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

3 Citations (Scopus)

Abstract

Dengue has been reported as a major cause of morbidity and mortality for the last 40 years worldwide including Malaysia. This work develops and implements visualization and predictive modelling for dengue in Malaysia. The incidences are visualized using Geographical Information System (GIS) which provides information on coordinates (latitude and longitude) based on the occurrence of incidences registered with the Disease Control Division of the Ministry of Health, Malaysia. Clustering is performed on this data using centroid and distribution models that are representatives of K-means and the Expectation Maximization (EM) algorithms, respectively. The results are validated for Petaling district of Selangor and are shown to possess good performance in predicting the dengue incidences. The proposed method is able to localize the incidences which can further be utilized for vector disease control processes.

Original language	English
Title of host publication	2016 IEEE 12th International Colloquium on Signal Processing & Its Applications (CSPA)
Publisher	IEEE
Pages	227-232
Number of pages	6
ISBN (Electronic)	9781467387804
DOIs	https://doi.org/10.1109/CSPA.2016.7515836
Publication status	Published - 21 Jul 2016

Keywords

dengue
clustering algorithm
GIS
K-means
EM algorithm

UN SDGs

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

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10.1109/CSPA.2016.7515836

Cite this

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title = "Generating Vulnerability Maps of Dengue Incidences for Petaling District in Malaysia",

abstract = "Dengue has been reported as a major cause of morbidity and mortality for the last 40 years worldwide including Malaysia. This work develops and implements visualization and predictive modelling for dengue in Malaysia. The incidences are visualized using Geographical Information System (GIS) which provides information on coordinates (latitude and longitude) based on the occurrence of incidences registered with the Disease Control Division of the Ministry of Health, Malaysia. Clustering is performed on this data using centroid and distribution models that are representatives of K-means and the Expectation Maximization (EM) algorithms, respectively. The results are validated for Petaling district of Selangor and are shown to possess good performance in predicting the dengue incidences. The proposed method is able to localize the incidences which can further be utilized for vector disease control processes.",

keywords = "dengue, clustering algorithm, GIS, K-means, EM algorithm",

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

year = "2016",

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doi = "10.1109/CSPA.2016.7515836",

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booktitle = "2016 IEEE 12th International Colloquium on Signal Processing & Its Applications (CSPA)",

publisher = "IEEE",

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TY - GEN

T1 - Generating Vulnerability Maps of Dengue Incidences for Petaling District in Malaysia

AU - Mathur, Nirbhay

AU - Asirvadam, Vijanth S.

AU - Dass, Sarat C.

AU - Gill, Balvinder Singh

PY - 2016/7/21

Y1 - 2016/7/21

N2 - Dengue has been reported as a major cause of morbidity and mortality for the last 40 years worldwide including Malaysia. This work develops and implements visualization and predictive modelling for dengue in Malaysia. The incidences are visualized using Geographical Information System (GIS) which provides information on coordinates (latitude and longitude) based on the occurrence of incidences registered with the Disease Control Division of the Ministry of Health, Malaysia. Clustering is performed on this data using centroid and distribution models that are representatives of K-means and the Expectation Maximization (EM) algorithms, respectively. The results are validated for Petaling district of Selangor and are shown to possess good performance in predicting the dengue incidences. The proposed method is able to localize the incidences which can further be utilized for vector disease control processes.

AB - Dengue has been reported as a major cause of morbidity and mortality for the last 40 years worldwide including Malaysia. This work develops and implements visualization and predictive modelling for dengue in Malaysia. The incidences are visualized using Geographical Information System (GIS) which provides information on coordinates (latitude and longitude) based on the occurrence of incidences registered with the Disease Control Division of the Ministry of Health, Malaysia. Clustering is performed on this data using centroid and distribution models that are representatives of K-means and the Expectation Maximization (EM) algorithms, respectively. The results are validated for Petaling district of Selangor and are shown to possess good performance in predicting the dengue incidences. The proposed method is able to localize the incidences which can further be utilized for vector disease control processes.

KW - dengue

KW - clustering algorithm

KW - GIS

KW - K-means

KW - EM algorithm

U2 - 10.1109/CSPA.2016.7515836

DO - 10.1109/CSPA.2016.7515836

M3 - Conference contribution

SP - 227

EP - 232

BT - 2016 IEEE 12th International Colloquium on Signal Processing & Its Applications (CSPA)

PB - IEEE

ER -

Generating Vulnerability Maps of Dengue Incidences for Petaling District in Malaysia

Abstract

Keywords

UN SDGs

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