Ullah Sami, Daud Hanita, Dass Sarat C, Khan Habib Nawaz, Khalil Alamgir
Department of Fundamental and Applied Sciences, Universiti Teknologi PETRONAS, Seri Iskandar.
Geospat Health. 2017 Nov 6;12(2):567. doi: 10.4081/gh.2017.567.
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. A Correction has been published: https://doi.org/10.4081/gh.2023.1232
检测疾病发生的时空数据中潜在的时空聚集区的能力对于开展监测和实施疾病预防政策至关重要。大多数现有技术使用几何形状（圆形、椭圆形或方形）的扫描窗口来发现疾病聚集区。在某些情况下，疾病发生往往聚集在形状非常不规则的区域，这些算法在实际检测时空聚集区时并不可行。为解决这一问题，提出了一种新算法，该算法使用共聚类策略来检测前瞻性和回顾性时空疾病聚集区，对形状和大小没有限制。所提出的方法通过跟踪时空发生结构的变化来检测时空疾病聚集区，而不是在空间上进行深入搜索。该方法被用于检测2012年至2016年巴基斯坦开伯尔-普赫图赫瓦省年度和月度疟疾数据中的潜在聚集区，并将结果可视化在热图上。年度数据分析结果表明，最有可能的热点出现在2013 - 2014年的三个子区域。月度数据中最有可能的热点出现在每年的7月至10月，并呈现出强烈的周期性趋势。已发布勘误：https://doi.org/10.4081/gh.2023.1232
