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 de...

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Autores principales:	Giphil Cho, Seungheon Lee, Hyojung Lee
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/01764c284e954f4a826c5c1d17b14126
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spelling	oai:doaj.org-article:01764c284e954f4a826c5c1d17b141262021-11-14T12:22:59ZEstimating severe fever with thrombocytopenia syndrome transmission using machine learning methods in South Korea10.1038/s41598-021-01361-92045-2322https://doaj.org/article/01764c284e954f4a826c5c1d17b141262021-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-01361-9https://doaj.org/toc/2045-2322Abstract 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.Giphil ChoSeungheon LeeHyojung LeeNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Giphil Cho Seungheon Lee Hyojung Lee Estimating severe fever with thrombocytopenia syndrome transmission using machine learning methods in South Korea
description	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.
format	article
author	Giphil Cho Seungheon Lee Hyojung Lee
author_facet	Giphil Cho Seungheon Lee Hyojung Lee
author_sort	Giphil Cho
title	Estimating severe fever with thrombocytopenia syndrome transmission using machine learning methods in South Korea
title_short	Estimating severe fever with thrombocytopenia syndrome transmission using machine learning methods in South Korea
title_full	Estimating severe fever with thrombocytopenia syndrome transmission using machine learning methods in South Korea
title_fullStr	Estimating severe fever with thrombocytopenia syndrome transmission using machine learning methods in South Korea
title_full_unstemmed	Estimating severe fever with thrombocytopenia syndrome transmission using machine learning methods in South Korea
title_sort	estimating severe fever with thrombocytopenia syndrome transmission using machine learning methods in south korea
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/01764c284e954f4a826c5c1d17b14126
work_keys_str_mv	AT giphilcho estimatingseverefeverwiththrombocytopeniasyndrometransmissionusingmachinelearningmethodsinsouthkorea AT seungheonlee estimatingseverefeverwiththrombocytopeniasyndrometransmissionusingmachinelearningmethodsinsouthkorea AT hyojunglee estimatingseverefeverwiththrombocytopeniasyndrometransmissionusingmachinelearningmethodsinsouthkorea
_version_	1718429207031185408

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

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