背景:登革热是一种蚊子传播的疾病,每年在全球范围内引起3亿多人感染,没有特定的治疗方法。疫情检测和资源分配需要有效的监测系统。常用的空间簇检测方法,但是没有关于登革热监测最合适方法的一般指导。因此,需要进行综合研究,以评估不同的方法,并为登革热监测计划提供指导.
方法:为了评估不同聚类检测方法对登革热监测的有效性,我们选择并评估了常用的方法:GetisOrd[公式:见正文],当地的Moran,SaTScan,和贝叶斯建模。我们进行了一项仿真研究,以比较它们在检测集群方面的性能,并将所有方法应用于2019年泰国登革热监测的案例研究,以进一步评估其实用性。
结果:在模拟研究中,GetisOrd[公式:见文字]和LocalMoran有类似的表现,大多数误检测发生在集群边界和孤立的热点。SaTScan显示出更好的精度,但在检测内部异常值方面效果较差,尽管它在大规模疫情中表现良好。贝叶斯卷积建模在仿真研究中具有最高的整体精度。在泰国的登革热案例研究中,GetisOrd[公式:参见文字]和LocalMoran错过了大多数疾病集群,而SaTScan主要能够检测到大型集群。贝叶斯疾病图谱似乎是最有效的,具有不规则形状的疾病异常的适应性检测。
结论:贝叶斯建模被证明是最有效的方法,在自适应识别不规则形状的疾病异常方面表现出最佳准确性。相比之下,SaTScan擅长检测大规模爆发和定期表格。本研究为泰国登革热监测选择合适的工具提供了经验证据,在类似的环境中具有对其他疾病控制计划的潜在适用性。
BACKGROUND: Dengue is a mosquito-borne disease that causes over 300 million infections worldwide each year with no specific treatment available. Effective surveillance systems are needed for outbreak detection and resource allocation. Spatial cluster detection methods are commonly used, but no general guidance exists on the most appropriate method for dengue surveillance. Therefore, a comprehensive study is needed to assess different methods and provide guidance for dengue surveillance programs.
METHODS: To evaluate the effectiveness of different cluster detection methods for dengue surveillance, we selected and assessed commonly used methods: Getis Ord [Formula: see text], Local Moran, SaTScan, and Bayesian modeling. We conducted a simulation study to compare their performance in detecting clusters, and applied all methods to a case study of dengue surveillance in Thailand in 2019 to further evaluate their practical utility.
RESULTS: In the simulation study, Getis Ord [Formula: see text] and Local Moran had similar performance, with most misdetections occurring at cluster boundaries and isolated hotspots. SaTScan showed better precision but was less effective at detecting inner outliers, although it performed well on large outbreaks. Bayesian convolution modeling had the highest overall precision in the simulation study. In the dengue case study in Thailand, Getis Ord [Formula: see text] and Local Moran missed most disease clusters, while SaTScan was mostly able to detect a large cluster. Bayesian disease mapping seemed to be the most effective, with adaptive detection of irregularly shaped disease anomalies.
CONCLUSIONS: Bayesian modeling showed to be the most effective method, demonstrating the best accuracy in adaptively identifying irregularly shaped disease anomalies. In contrast, SaTScan excelled in detecting large outbreaks and regular forms. This study provides empirical evidence for the selection of appropriate tools for dengue surveillance in Thailand, with potential applicability to other disease control programs in similar settings.