Dengue infection varies from mild to severe, lacking specific treatment. Accurately estimating outbreak timing and location is crucial for allocating the resources efficiently. Timely notification systems are essential to monitor dengue incidence, detect outbreaks promptly, and implement effective control measures.
The case study in Thailand showcased the practical application of this methodology, enabling the timely initiation of disease control activities. In dengue monitoring in countries such as Thailand, the information about dengue cases may not be reported promptly. This lag caused by the delays and incomplete information, is a critical concern for disease control planning in surveillance systems as it hampers early warning and real-time outbreak detection. The platform developed by the MORU researchers was able to identify elevated risk clusters in real-time, including reporting delays.
The study was published in the BMC Medical Research Methodology. The two-step method of this platform is valuable for real-time detection of dengue clusters. By dealing with delays in reporting information and using a method to identify unusual patterns, this new approach makes existing surveillance systems more effective and enhances their ability to predict dengue. The efforts made in this study complement the existing surveillance systems as well as the forecasting methods. The platform has the potential for various uses, including addressing the challenges in monitoring other infectious or emerging diseases.