Nowcasting Vector Mosquito Abundance and Determining Its Association With Malaria Epidemics in South Korea.

ABSTRACT: Since a resurgence occurred in 1993, malaria has remained an endemic disease in the Republic of Korea (ROK). A major challenge is the inaccessibility of current vector mosquito abundance data due to a 2-week reporting delay, which limits timely implementation of control measures. We aimed to nowcast mosquito abundance and assess its utility by evaluating the predictive value of mosquito abundance for malaria epidemic peaks. We used machine learning models to nowcast mosquito abundance, employing gradient boosting models (GBMs), extreme gradient boosting (XGB), and an ensemble model combining both. Various meteorological factors served as predictors. The models were trained with data from mosquito collection sites between 2009 and 2021 and tested with data from 2022. To evaluate the utility

SUBMITTER: Chang T 

PROVIDER: S-EPMC12016956 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature