Influenza-Like Illness Patients Forecasting by Fusing Internet Public Opinion

Yu Chih Wei; Yan Ling Ou; Jianqiang Li; Wei Chen Wu

doi:10.1007/978-981-16-8052-6_15

Influenza-Like Illness Patients Forecasting by Fusing Internet Public Opinion

Yu Chih Wei
, Yan Ling Ou
, Jianqiang Li
, Wei Chen Wu

Department of Information and Finance Management

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Due to rapid change in influenza viruses, a prediction model for outbreaks of influenza-like illnesses helps to find out the spread of the illnesses in real time. In addition to using traditional hydrological and atmospheric data, popular search keywords on Google Trends are used as features in this research. Google Trends are popular keyword searches on the Google search engine. Popular keywords used in discussions of influenza-like symptoms at specific regions within specific periods are used in this research. Public holiday information in Taiwan, the population density, air quality indices, and the numbers of COVID-19 confirmed cases are also used as features in this research. An Ensemble Learning model, combining Random Forest and XGBoost, is used in this research. It can be confirmed from the actual experimental results in this research that the use of the ensemble learning prediction model proposed in this research can accurately predict the trend of influenza-like cases. The evaluation results show that the mean RMSLE of our proposed model is 0.2 in comparison with the actual number of influenza-like cases.

Original language	English
Title of host publication	Frontier Computing - Proceedings of FC 2021
Editors	Jason C. Hung, Neil Y. Yen, Jia-Wei Chang
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	143-151
Number of pages	9
ISBN (Print)	9789811680519
DOIs	https://doi.org/10.1007/978-981-16-8052-6_15
State	Published - 2022
Event	11th International Conference on Frontier Computing, FC 2021 - Virtual, Online Duration: 14 Jul 2021 → 14 Jul 2021

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	827 LNEE
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	11th International Conference on Frontier Computing, FC 2021
City	Virtual, Online
Period	14/07/21 → 14/07/21

Bibliographical note

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Keywords

Influenza-like illnesses
Monitoring and early warning

Access to Document

10.1007/978-981-16-8052-6_15

Cite this

Wei, Y. C., Ou, Y. L., Li, J., & Wu, W. C. (2022). Influenza-Like Illness Patients Forecasting by Fusing Internet Public Opinion. In J. C. Hung, N. Y. Yen, & J.-W. Chang (Eds.), Frontier Computing - Proceedings of FC 2021 (pp. 143-151). (Lecture Notes in Electrical Engineering; Vol. 827 LNEE). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-16-8052-6_15

@inproceedings{3514c86ab0e548f190c648b5051b4033,

title = "Influenza-Like Illness Patients Forecasting by Fusing Internet Public Opinion",

abstract = "Due to rapid change in influenza viruses, a prediction model for outbreaks of influenza-like illnesses helps to find out the spread of the illnesses in real time. In addition to using traditional hydrological and atmospheric data, popular search keywords on Google Trends are used as features in this research. Google Trends are popular keyword searches on the Google search engine. Popular keywords used in discussions of influenza-like symptoms at specific regions within specific periods are used in this research. Public holiday information in Taiwan, the population density, air quality indices, and the numbers of COVID-19 confirmed cases are also used as features in this research. An Ensemble Learning model, combining Random Forest and XGBoost, is used in this research. It can be confirmed from the actual experimental results in this research that the use of the ensemble learning prediction model proposed in this research can accurately predict the trend of influenza-like cases. The evaluation results show that the mean RMSLE of our proposed model is 0.2 in comparison with the actual number of influenza-like cases.",

keywords = "Influenza-like illnesses, Monitoring and early warning",

author = "Wei, \{Yu Chih\} and Ou, \{Yan Ling\} and Jianqiang Li and Wu, \{Wei Chen\}",

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 11th International Conference on Frontier Computing, FC 2021 ; Conference date: 14-07-2021 Through 14-07-2021",

year = "2022",

doi = "10.1007/978-981-16-8052-6\_15",

language = "???core.languages.en\_GB???",

isbn = "9789811680519",

series = "Lecture Notes in Electrical Engineering",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "143--151",

editor = "Hung, \{Jason C.\} and Yen, \{Neil Y.\} and Jia-Wei Chang",

booktitle = "Frontier Computing - Proceedings of FC 2021",

}

Wei, YC, Ou, YL, Li, J & Wu, WC 2022, Influenza-Like Illness Patients Forecasting by Fusing Internet Public Opinion. in JC Hung, NY Yen & J-W Chang (eds), Frontier Computing - Proceedings of FC 2021. Lecture Notes in Electrical Engineering, vol. 827 LNEE, Springer Science and Business Media Deutschland GmbH, pp. 143-151, 11th International Conference on Frontier Computing, FC 2021, Virtual, Online, 14/07/21. https://doi.org/10.1007/978-981-16-8052-6_15

Influenza-Like Illness Patients Forecasting by Fusing Internet Public Opinion. / Wei, Yu Chih; Ou, Yan Ling; Li, Jianqiang et al.
Frontier Computing - Proceedings of FC 2021. ed. / Jason C. Hung; Neil Y. Yen; Jia-Wei Chang. Springer Science and Business Media Deutschland GmbH, 2022. p. 143-151 (Lecture Notes in Electrical Engineering; Vol. 827 LNEE).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Influenza-Like Illness Patients Forecasting by Fusing Internet Public Opinion

AU - Wei, Yu Chih

AU - Ou, Yan Ling

AU - Li, Jianqiang

AU - Wu, Wei Chen

PY - 2022

Y1 - 2022

N2 - Due to rapid change in influenza viruses, a prediction model for outbreaks of influenza-like illnesses helps to find out the spread of the illnesses in real time. In addition to using traditional hydrological and atmospheric data, popular search keywords on Google Trends are used as features in this research. Google Trends are popular keyword searches on the Google search engine. Popular keywords used in discussions of influenza-like symptoms at specific regions within specific periods are used in this research. Public holiday information in Taiwan, the population density, air quality indices, and the numbers of COVID-19 confirmed cases are also used as features in this research. An Ensemble Learning model, combining Random Forest and XGBoost, is used in this research. It can be confirmed from the actual experimental results in this research that the use of the ensemble learning prediction model proposed in this research can accurately predict the trend of influenza-like cases. The evaluation results show that the mean RMSLE of our proposed model is 0.2 in comparison with the actual number of influenza-like cases.

AB - Due to rapid change in influenza viruses, a prediction model for outbreaks of influenza-like illnesses helps to find out the spread of the illnesses in real time. In addition to using traditional hydrological and atmospheric data, popular search keywords on Google Trends are used as features in this research. Google Trends are popular keyword searches on the Google search engine. Popular keywords used in discussions of influenza-like symptoms at specific regions within specific periods are used in this research. Public holiday information in Taiwan, the population density, air quality indices, and the numbers of COVID-19 confirmed cases are also used as features in this research. An Ensemble Learning model, combining Random Forest and XGBoost, is used in this research. It can be confirmed from the actual experimental results in this research that the use of the ensemble learning prediction model proposed in this research can accurately predict the trend of influenza-like cases. The evaluation results show that the mean RMSLE of our proposed model is 0.2 in comparison with the actual number of influenza-like cases.

KW - Influenza-like illnesses

KW - Monitoring and early warning

UR - https://www.scopus.com/pages/publications/85131884623

U2 - 10.1007/978-981-16-8052-6_15

DO - 10.1007/978-981-16-8052-6_15

M3 - ???researchoutput.researchoutputtypes.contributiontobookanthology.conference???

AN - SCOPUS:85131884623

SN - 9789811680519

T3 - Lecture Notes in Electrical Engineering

SP - 143

EP - 151

BT - Frontier Computing - Proceedings of FC 2021

A2 - Hung, Jason C.

A2 - Yen, Neil Y.

A2 - Chang, Jia-Wei

PB - Springer Science and Business Media Deutschland GmbH

T2 - 11th International Conference on Frontier Computing, FC 2021

Y2 - 14 July 2021 through 14 July 2021

ER -

Influenza-Like Illness Patients Forecasting by Fusing Internet Public Opinion

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Cite this