Department of Fundamental & Applied Sciences, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, Tronoh Perak, Malaysia.
Department of Biostatistics, University of Oslo, Oslo, Norway.
PLoS One. 2018 Jun 19;13(6):e0199176. doi: 10.1371/journal.pone.0199176. eCollection 2018.
Identifying the abnormally high-risk regions in a spatiotemporal space that contains an unexpected disease count is helpful to conduct surveillance and implement control strategies. The EigenSpot algorithm has been recently proposed for detecting space-time disease clusters of arbitrary shapes with no restriction on the distribution and quality of the data, and has shown some promising advantages over the state-of-the-art methods. However, the main problem with the EigenSpot method is that it cannot be adapted to detect more than one spatiotemporal hotspot. This is an important limitation, since, in reality, we may have multiple hotspots, sometimes at the same level of importance. We propose an extension of the EigenSpot algorithm, called Multi-EigenSpot that is able to handle multiple hotspots by iteratively removing previously detected hotspots and re-running the algorithm until no more hotspots are found. In addition, a visualization tool (heatmap) has been linked to the proposed algorithm to visualize multiple clusters with different colors. We evaluated the proposed method using the monthly data on measles cases in Khyber-Pakhtunkhwa, Pakistan (Jan 2016- Dec 2016), and the efficiency was compared with the state-of-the-art methods: EigenSpot and Space-time scan statistic (SaTScan). The results showed the effectiveness of the proposed method for detecting multiple clusters in a spatiotemporal space.
识别时空空间中异常高风险区域，该区域包含异常疾病计数，有助于进行监测和实施控制策略。EigenSpot 算法最近被提出，用于检测任意形状的时空疾病集群，对数据的分布和质量没有限制，并且与最先进的方法相比显示出一些有希望的优势。然而，EigenSpot 方法的主要问题是它不能适应检测多个时空热点。这是一个重要的限制，因为在现实中，我们可能有多个热点，有时处于相同的重要性水平。我们提出了 EigenSpot 算法的扩展，称为 Multi-EigenSpot，它能够通过迭代删除先前检测到的热点并重新运行算法来处理多个热点，直到不再发现热点。此外，已经将可视化工具（热图）链接到所提出的算法中，以便用不同的颜色可视化多个集群。我们使用巴基斯坦开伯尔-普赫图赫瓦省（2016 年 1 月至 2016 年 12 月）每月的麻疹病例数据评估了所提出的方法，并将效率与最先进的方法进行了比较：EigenSpot 和时空扫描统计（SaTScan）。结果表明，该方法在时空空间中检测多个集群是有效的。
