Detecting the geographical tendency for the presence of a disease or incident is, particularly at an early stage, a key challenge for preventing severe consequences. Given recent rapid advancements in information technologies, it is required a comprehensive framework that enables simultaneous detection of multiple spatial clusters, whether disease cases are randomly scattered or clustered around specific epicenters on a larger scale. We develop a new methodology that detects multiple spatial disease clusters and evaluates its performance compared to existing other methods. A novel framework for spatial multiple-cluster detection is developed. The framework directly stands on the integrated bases of scan statistics and generalized linear models, adopting a new information criterion that selects the appropriate number of disease clusters. We evaluated the proposed approach using a real dataset, the hospital admission for chronic obstructive pulmonary disease (COPD) in England, and simulated data, whether the approach tends to select the correct number of clusters. A case study and simulation studies conducted both confirmed that the proposed method performed better compared to conventional cluster detection procedures, in terms of higher sensitivity. We proposed a new statistical framework that simultaneously detects and evaluates multiple disease clusters in a large study space, with high detection power compared to conventional approaches.

中文翻译：

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检测多个空间疾病群：信息标准和扫描统计方法。

尤其是在早期阶段，检测疾病或突发事件的地理趋势是防止严重后果的关键挑战。鉴于信息技术的最新飞速发展，需要一个能够同时检测多个空间簇的综合框架，无论疾病病例是随机散布还是大范围聚集在特定震中。我们开发了一种新的方法，可以检测多个空间疾病簇，并与现有的其他方法进行比较来评估其性能。开发了一种用于空间多聚类检测的新颖框架。该框架直接基于扫描统计数据和广义线性模型的集成基础，采用新的信息标准来选择适当数量的疾病簇。我们使用真实的数据集，英格兰的慢性阻塞性肺疾病（COPD）住院入院以及模拟数据评估了该方法的可行性，该方法是否倾向于选择正确的聚类数目。进行的案例研究和模拟研究均证实，与传统的聚类检测程序相比，该方法在更高的灵敏度方面表现更好。我们提出了一个新的统计框架，该框架可以同时检测和评估大型研究空间中的多种疾病簇，与传统方法相比具有较高的检测能力。进行的案例研究和模拟研究均证实，与传统的聚类检测程序相比，该方法在更高的灵敏度方面表现更好。我们提出了一个新的统计框架，该框架可以同时检测和评估大型研究空间中的多种疾病簇，与传统方法相比具有较高的检测能力。进行的案例研究和模拟研究均证实，与传统的聚类检测程序相比，该方法在更高的灵敏度方面表现更好。我们提出了一个新的统计框架，该框架可以同时检测和评估大型研究空间中的多种疾病簇，与传统方法相比具有较高的检测能力。