Early Warning Systems For Malaria Outbreaks in Thailand: An Anomaly Detection Approach

Abstract

Abstract Background Malaria continues to pose a significant health threat. Rapid identification of malaria infections and the deployment of active surveillance tools are crucial for achieving malaria elimination in Thailand. In this study, we introduce an anomaly detection system as an early warning mechanism for potential malaria outbreaks in the country. Methods We developed and compared statistical, machine learning, and threshold-based anomaly detection algorithms to identify atypical malaria activity in Thailand. Additionally, we designed a user interface tailored for anomaly detection, enabling the Thai malaria surveillance team to utilize these algorithms and visualize regions exhibiting unusual malaria patterns. Results We formulated nine distinct anomaly detection algorithms. Their efficacy in pinpointing verified outbreaks was assessed using malaria case data from Thailand spanning 2012 to 2022. The historical average threshold-based anomaly detection method triggered three times fewer alerts, while correctly identifying the same number of verified outbreaks. A limitation of this analysis is the small number of verified outbreaks; further consultation with the Division of Vector Borne Disease could help identify more verified outbreaks. The developed dashboard, designed specifically for anomaly detection, allows disease surveillance professionals to easily identify and visualise unusual malaria activity at a provincial level across Thailand. Conclusion We propose an enhanced early warning system to bolster malaria elimination efforts in Thailand. The developed anomaly detection algorithms, after thorough comparison, have been optimized for seamless integration with the current malaria surveillance infrastructure. An anomaly detection dashboard for Thailand is built and supports early detection of abnormal malaria activity. In summary, our proposed early warning system enhances the identification process for provinces at risk of outbreaks and offers easy integration with Thailand’s established malaria surveillance framework.