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 accuracy of the novel model, the performance of modified-RMML was improved by reducing the MSE for the test data as 12.6–52.2%, compared to the RMML using five machine learning methods. During the period of increasing the SFTS cases from May to October, the modified-RMML could give more accurate estimation. Computing the feature importance, it is clearly observed that climate factors such as average maximum temperature, precipitation as well as mountain visitors, and the estimation of SFTS occurrence obtained from CMML had high Gini importance. The novel model incorporating CMML and RMML models improves the accuracy of the estimation of SFTS cases. Using the model, climate factors, including temperature, relative humidity, and mountain visitors play important roles in transmitting SFTS in Korea. Our findings highlighted that the guidelines for mountain visitors to prevent SFTS transmissions should be addressed. Moreover, it provides important insights for establishing control interventions that predict early identification of SFTS cases.

Global epidemiology of severe fever with thrombocytopenia syndrome virus in human and animals: a systematic review and meta-analysis

“…This understanding can not only provide key information for early monitoring and prevention of the epidemic, but also guide the formulation of public health policies, in order to establish a more sensitive disease monitoring system and emergency response mechanisms on a global scale. Previously, data on human infections were mostly published at the level of individual countries, 5 , 15 , 18 , 19 and data comparisons from a global perspective were only updated until 2018. 20 , 21 Further updates are needed on the reporting status of SFTS in human.…”

Applications of Artificial Intelligence in Thrombocytopenia

“…The BTR ML model from the modified RMML was superior to all others. The AUC for the BTR model was >0.9 at predicting the occurrence of SFTS [ 19 ].…”

Time series models in prediction of severe fever with thrombocytopenia syndrome cases in Shandong province, China

“…Previous studies have mainly focused on multivariate analysis methods to predict the incidence of SFTS. These studies analyzed various environmental, meteorological, and social factors, among others, to identify incidence-related risk factors and establish prediction models based on these factors ( Cho et al., 2021 ; Deng et al., 2022 ; Ding et al., 2014 ; Sun et al., 2018 , 2021 ; Wu et al., 2020 ). However, few studies have reported models for prediction of SFTS incidence just based on the historical incidence data, which may be more convenient and practical.…”