Malaria remains a substantial risk to global public health. Detecting malaria infections at the earliest and implementing effective surveillance tools are vital steps toward achieving the elimination of malaria in endemic regions like specific areas in Thailand.
The researchers from MORU, developed nine unique anomaly detection algorithms and evaluated their effectiveness in identifying verified outbreaks. The assessment utilized malaria case data from Thailand, covering the period 2012 to 2022. The study was published in the BMC Malaria Journal.
Based on historical averages, this new detection method gave three times fewer alerts than the current method. However, it still correctly identified the same number of confirmed outbreaks. In simpler terms, it's a more efficient way of detecting outbreaks with fewer false alarms. An upgraded early alert system was proposed to boost malaria elimination initiatives in countries sharing a malaria profile similar to that of Thailand.
After a comprehensive comparison, the refined anomaly detection algorithms, have been fine-tuned for seamless integration into the existing malaria surveillance framework. A dashboard was designed for Thailand that focuses on detecting anomalies, aiding in the early identification of unusual malaria patterns. The early warning system improves the process of identifying provinces that are prone to outbreaks, offering seamless integration with Thailand's existing malaria surveillance framework.