Estimating severe fever with thrombocytopenia syndrome transmission using machine learning methods in South Korea.

ABSTRACT: Severe fever with thrombocytopenia syndrome (SFTS) is an emerging tick-borne infectious disease in China, Japan, and Korea. This study aimed to estimate the monthly SFTS occurrence and the monthly number of SFTS cases in the geographical area in Korea using epidemiological data including demographic, geographic, and meteorological factors. Important features were chosen through univariate feature selection. Two models using machine learning methods were analyzed: the classification model in machine learning (CMML) and regression model in machine learning (RMML). We developed a novel model incorporating the CMML results into RMML, defined as modified-RMML. Feature importance was computed to assess the contribution of estimating the number of SFTS cases using modified-RMML. Aspect to the acc

SUBMITTER: Cho G 

PROVIDER: S-EPMC8575988 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature