Infectious disease outbreak prediction using media articles with machine learning models

Abstract When a newly emerging infectious disease breaks out in a country, it brings critical damage to both human health conditions and the national economy. For this reason, apprehending which disease will newly emerge, and preparing countermeasures for that disease, are required. Many different t...

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Autores principales:	Juhyeon Kim, Insung Ahn
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/497accacf95044c78f0128a73957f211
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spelling	oai:doaj.org-article:497accacf95044c78f0128a73957f2112021-12-02T13:35:03ZInfectious disease outbreak prediction using media articles with machine learning models10.1038/s41598-021-83926-22045-2322https://doaj.org/article/497accacf95044c78f0128a73957f2112021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-83926-2https://doaj.org/toc/2045-2322Abstract When a newly emerging infectious disease breaks out in a country, it brings critical damage to both human health conditions and the national economy. For this reason, apprehending which disease will newly emerge, and preparing countermeasures for that disease, are required. Many different types of infectious diseases are emerging and threatening global human health conditions. For this reason, the detection of emerging infectious disease pattern is critical. However, as the epidemic spread of infectious disease occurs sporadically and rapidly, it is not easy to predict whether an infectious disease will emerge or not. Furthermore, accumulating data related to a specific infectious disease is not easy. For these reasons, finding useful data and building a prediction model with these data is required. The Internet press releases numerous articles every day that rapidly reflect currently pending issues. Thus, in this research, we accumulated Internet articles from Medisys that were related to infectious disease, to see if news data could be used to predict infectious disease outbreak. Articles related to infectious disease from January to December 2019 were collected. In this study, we evaluated if newly emerging infectious diseases could be detected using the news article data. Support Vector Machine (SVM), Semi-supervised Learning (SSL), and Deep Neural Network (DNN) were used for prediction to examine the use of information embedded in the web articles: and to detect the pattern of emerging infectious disease.Juhyeon KimInsung AhnNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Juhyeon Kim Insung Ahn Infectious disease outbreak prediction using media articles with machine learning models
description	Abstract When a newly emerging infectious disease breaks out in a country, it brings critical damage to both human health conditions and the national economy. For this reason, apprehending which disease will newly emerge, and preparing countermeasures for that disease, are required. Many different types of infectious diseases are emerging and threatening global human health conditions. For this reason, the detection of emerging infectious disease pattern is critical. However, as the epidemic spread of infectious disease occurs sporadically and rapidly, it is not easy to predict whether an infectious disease will emerge or not. Furthermore, accumulating data related to a specific infectious disease is not easy. For these reasons, finding useful data and building a prediction model with these data is required. The Internet press releases numerous articles every day that rapidly reflect currently pending issues. Thus, in this research, we accumulated Internet articles from Medisys that were related to infectious disease, to see if news data could be used to predict infectious disease outbreak. Articles related to infectious disease from January to December 2019 were collected. In this study, we evaluated if newly emerging infectious diseases could be detected using the news article data. Support Vector Machine (SVM), Semi-supervised Learning (SSL), and Deep Neural Network (DNN) were used for prediction to examine the use of information embedded in the web articles: and to detect the pattern of emerging infectious disease.
format	article
author	Juhyeon Kim Insung Ahn
author_facet	Juhyeon Kim Insung Ahn
author_sort	Juhyeon Kim
title	Infectious disease outbreak prediction using media articles with machine learning models
title_short	Infectious disease outbreak prediction using media articles with machine learning models
title_full	Infectious disease outbreak prediction using media articles with machine learning models
title_fullStr	Infectious disease outbreak prediction using media articles with machine learning models
title_full_unstemmed	Infectious disease outbreak prediction using media articles with machine learning models
title_sort	infectious disease outbreak prediction using media articles with machine learning models
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/497accacf95044c78f0128a73957f211
work_keys_str_mv	AT juhyeonkim infectiousdiseaseoutbreakpredictionusingmediaarticleswithmachinelearningmodels AT insungahn infectiousdiseaseoutbreakpredictionusingmediaarticleswithmachinelearningmodels
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Infectious disease outbreak prediction using media articles with machine learning models

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