Department of Tropical Hygiene, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand.
Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand.
BMC Med Res Methodol. 2024 Jan 13;24(1):10. doi: 10.1186/s12874-024-02141-5.
Dengue infection ranges from asymptomatic to severe and life-threatening, with no specific treatment available. Vector control is crucial for interrupting its transmission cycle. Accurate estimation of outbreak timing and location is essential for efficient resource allocation. Timely and reliable notification systems are necessary to monitor dengue incidence, including spatial and temporal distributions, to detect outbreaks promptly and implement effective control measures.
We proposed an integrated two-step methodology for real-time spatiotemporal cluster detection, accounting for reporting delays. In the first step, we employed space-time nowcasting modeling to compensate for lags in the reporting system. Subsequently, anomaly detection methods were applied to assess adverse risks. To illustrate the effectiveness of these detection methods, we conducted a case study using weekly dengue surveillance data from Thailand.
The developed methodology demonstrated robust surveillance effectiveness. By combining space-time nowcasting modeling and anomaly detection, we achieved enhanced detection capabilities, accounting for reporting delays and identifying clusters of elevated risk in real-time. The case study in Thailand showcased the practical application of our methodology, enabling timely initiation of disease control activities.
Our integrated two-step methodology provides a valuable approach for real-time spatiotemporal cluster detection in dengue surveillance. By addressing reporting delays and incorporating anomaly detection, it complements existing surveillance systems and forecasting efforts. Implementing this methodology can facilitate the timely initiation of disease control activities, contributing to more effective prevention and control strategies for dengue in Thailand and potentially other regions facing similar challenges.
登革热的感染范围从无症状到严重甚至危及生命，目前尚无特效治疗方法。病媒控制对于阻断其传播周期至关重要。准确估计疫情的时间和地点对于有效分配资源至关重要。及时可靠的通知系统对于监测登革热发病率（包括时空分布）、及时发现疫情并实施有效控制措施是必要的。
我们提出了一种集成的两步法，用于实时时空聚类检测，同时考虑到报告延迟。在第一步中，我们采用时空即时预测模型来弥补报告系统的滞后。随后，应用异常检测方法来评估不良风险。为了说明这些检测方法的有效性，我们使用来自泰国的每周登革热监测数据进行了案例研究。
所开发的方法具有强大的监测效果。通过结合时空即时预测模型和异常检测，我们实现了增强的检测能力，同时考虑了报告延迟，并实时识别出高风险的聚类。泰国的案例研究展示了我们方法的实际应用，能够及时启动疾病控制活动。
我们的集成两步法为登革热监测中的实时时空聚类检测提供了有价值的方法。通过解决报告延迟问题并纳入异常检测，该方法补充了现有的监测系统和预测工作。实施这种方法可以促进疾病控制活动的及时启动，有助于在泰国及其他面临类似挑战的地区制定更有效的登革热预防和控制策略。
