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...

Full description

Bibliographic Details
Main Authors: Juhyeon Kim, Insung Ahn
Format: Article
Language:English
Published: Nature Publishing Group 2021-02-01
Series:Scientific Reports
Online Access:https://doi.org/10.1038/s41598-021-83926-2
id doaj-497accacf95044c78f0128a73957f211
record_format Article
spelling doaj-497accacf95044c78f0128a73957f2112021-03-11T12:14:09ZengNature Publishing GroupScientific Reports2045-23222021-02-0111111310.1038/s41598-021-83926-2Infectious disease outbreak prediction using media articles with machine learning modelsJuhyeon Kim0Insung Ahn1Department of Data-Centric Problem Solving Research, Korea Institute of Science and Technology InformationDepartment of Data-Centric Problem Solving Research, Korea Institute of Science and Technology InformationAbstract 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.https://doi.org/10.1038/s41598-021-83926-2
collection DOAJ
language English
format Article
sources DOAJ
author Juhyeon Kim
Insung Ahn
spellingShingle Juhyeon Kim
Insung Ahn
Infectious disease outbreak prediction using media articles with machine learning models
Scientific Reports
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 Publishing Group
series Scientific Reports
issn 2045-2322
publishDate 2021-02-01
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.
url https://doi.org/10.1038/s41598-021-83926-2
work_keys_str_mv AT juhyeonkim infectiousdiseaseoutbreakpredictionusingmediaarticleswithmachinelearningmodels
AT insungahn infectiousdiseaseoutbreakpredictionusingmediaarticleswithmachinelearningmodels
_version_ 1724224571255554048