Cover Image

Detecting space-time disease clusters with arbitrary shapes and sizes using a co-clustering approach

Ability to detect potential space-time clusters in spatio-temporal data on disease occurrences is necessary for conducting surveillance and implementing disease prevention policies. Most existing techniques use geometrically shaped (circular, elliptical or square) scanning windows to discover diseas...

Full description

Saved in:
Bibliographic Details
Main Authors: Ullah, S., Daud, H., Dass, S.C., Khan, H.N., Khalil, A.
Format: Article
Published: Page Press Publications 2017
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85033239575&doi=10.4081%2fgh.2017.567&partnerID=40&md5=5948a5c8185bd6ddff13a31452df31ba
http://eprints.utp.edu.my/19820/
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.utp.eprints.19820
record_format eprints
spelling my.utp.eprints.198202018-04-22T12:59:20Z Detecting space-time disease clusters with arbitrary shapes and sizes using a co-clustering approach Ullah, S. Daud, H. Dass, S.C. Khan, H.N. Khalil, A. Ability to detect potential space-time clusters in spatio-temporal data on disease occurrences is necessary for conducting surveillance and implementing disease prevention policies. Most existing techniques use geometrically shaped (circular, elliptical or square) scanning windows to discover disease clusters. In certain situations, where the disease occurrences tend to cluster in very irregularly shaped areas, these algorithms are not feasible in practise for the detection of space-time clusters. To address this problem, a new algorithm is proposed, which uses a co-clustering strategy to detect prospective and retrospective space-time disease clusters with no restriction on shape and size. The proposed method detects space-time disease clusters by tracking the changes in space–time occurrence structure instead of an in-depth search over space. This method was utilised to detect potential clusters in the annual and monthly malaria data in Khyber Pakhtunkhwa Province, Pakistan from 2012 to 2016 visualising the results on a heat map. The results of the annual data analysis showed that the most likely hotspot emerged in three sub-regions in the years 2013-2014. The most likely hotspots in monthly data appeared in the month of July to October in each year and showed a strong periodic trend. © S. Ullah et al., 2017. Page Press Publications 2017 Article PeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85033239575&doi=10.4081%2fgh.2017.567&partnerID=40&md5=5948a5c8185bd6ddff13a31452df31ba Ullah, S. and Daud, H. and Dass, S.C. and Khan, H.N. and Khalil, A. (2017) Detecting space-time disease clusters with arbitrary shapes and sizes using a co-clustering approach. Geospatial Health, 12 (2). pp. 210-216. http://eprints.utp.edu.my/19820/
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description Ability to detect potential space-time clusters in spatio-temporal data on disease occurrences is necessary for conducting surveillance and implementing disease prevention policies. Most existing techniques use geometrically shaped (circular, elliptical or square) scanning windows to discover disease clusters. In certain situations, where the disease occurrences tend to cluster in very irregularly shaped areas, these algorithms are not feasible in practise for the detection of space-time clusters. To address this problem, a new algorithm is proposed, which uses a co-clustering strategy to detect prospective and retrospective space-time disease clusters with no restriction on shape and size. The proposed method detects space-time disease clusters by tracking the changes in space–time occurrence structure instead of an in-depth search over space. This method was utilised to detect potential clusters in the annual and monthly malaria data in Khyber Pakhtunkhwa Province, Pakistan from 2012 to 2016 visualising the results on a heat map. The results of the annual data analysis showed that the most likely hotspot emerged in three sub-regions in the years 2013-2014. The most likely hotspots in monthly data appeared in the month of July to October in each year and showed a strong periodic trend. © S. Ullah et al., 2017.
format Article
author Ullah, S.
Daud, H.
Dass, S.C.
Khan, H.N.
Khalil, A.
spellingShingle Ullah, S.
Daud, H.
Dass, S.C.
Khan, H.N.
Khalil, A.
Detecting space-time disease clusters with arbitrary shapes and sizes using a co-clustering approach
author_facet Ullah, S.
Daud, H.
Dass, S.C.
Khan, H.N.
Khalil, A.
author_sort Ullah, S.
title Detecting space-time disease clusters with arbitrary shapes and sizes using a co-clustering approach
title_short Detecting space-time disease clusters with arbitrary shapes and sizes using a co-clustering approach
title_full Detecting space-time disease clusters with arbitrary shapes and sizes using a co-clustering approach
title_fullStr Detecting space-time disease clusters with arbitrary shapes and sizes using a co-clustering approach
title_full_unstemmed Detecting space-time disease clusters with arbitrary shapes and sizes using a co-clustering approach
title_sort detecting space-time disease clusters with arbitrary shapes and sizes using a co-clustering approach
publisher Page Press Publications
publishDate 2017
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85033239575&doi=10.4081%2fgh.2017.567&partnerID=40&md5=5948a5c8185bd6ddff13a31452df31ba
http://eprints.utp.edu.my/19820/
_version_ 1738656123945222144
score 13.211869

Similar Items