Speaker
Description
Malaria remains a major global health concern. In the Republic of Korea, Plasmodium vivax is the dominant parasite and is characterized by both short- and long-incubation periods. Climate change has altered the mosquito habitats and expanded outbreak areas. However, the current malaria warning system in Korea is activated only when Plasmodium parasites are detected in captured mosquitoes. This study aims to improve the early detection of malaria outbreaks.
We utilized malaria case data from the Korea Disease Control Prevention and Control Agency (KDCA) and meteorological data from the Korea Meteorological Administration (KMA). First, we estimated infections at the time of infection using a back-calculation approach based on incubation period distributions. Second, we compared random forest, XGBoost, and LSTM models using meteorological variables from 2012 to 2020 and selected the best-performing model to predict infections. Finally, we identified outbreak periods using change point detection methods.
The XGBoost model showed the best performance in predicting infections. The outbreak onset estimated from long-incubation cases was detected earlier than the time when cases started to rise sharply. These findings suggest that change points derived from estimated long-incubation cases can serve as an effective tool for the early detection of P. vivax outbreaks. Overall, this approach may serve as a useful basis for determining the timing of preventive interventions.
