Detecting space-time disease clusters with arbitrary shapes and sizes using a co-clustering approach
Accepted: 21 May 2017
HTML: 690
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
A Correction has been published | View
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.
Supporting Agencies
Universiti Teknologi PETRONAS MalaysiaHow to Cite
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.
-
C. Biernacki, J. Jacques, C. KeribinJournal of Classification : 2023
-
Xiaojing WuISPRS International Journal of Geo-Information : 2022
-
Qian Liu, Xinqi Zheng, H. Eugene Stanley, Fei Xiao, Wenchao LiuIEEE Access : 2021
-
Ankita Wadhwa, Manish Kumar ThakurArabian Journal for Science and Engineering : 2022
-
Yuzhou Zhang, Hua Sun, Guang Gao, Lidan Shou, Dun WuScientific Reports : 2022
-
Xiaojing Wu, Changxiu Cheng, Raul Zurita-Milla, Changqing SongInternational Journal of Geographical Information Science : 2020
-
Sami Ullah, Hanita Daud, Sarat C. Dass, Hadi Fanaee-T, Alamgir Khalil, Mohammad AliPLOS ONE : 2018
-
M.R. Desjardins, A. Whiteman, I. Casas, E. DelmelleActa Tropica : 2018
-
Michael Desjardins, Alexander Hohl, Eric Delmelle, Irene Casas
-
The PublisherGeospatial Health : 2023
-
Xiaojing Wu, Donghai ZhengISPRS International Journal of Geo-Information : 2020
-
Xiaojing Wu, Ate Poorthuis, Raul Zurita-Milla, Menno-Jan KraakComputers & Geosciences : 2020
-
Haiqi Wang, Haoran Kong, Bin Yan, Liuke Li, Jianbo Xu, Zhihai Wang, Qiong WangIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing : 2021