Forecasting Influenza-like Illness in Compulsory Education Schools in Macau Using Google Search Queries and Machine Learning Models

Ying Chen; Jinsong Luo; Deyi Liang; Yewei Xie; Yi Zhou; Dan Wu; Weibin Cheng; Weiming Tang; Fengshi Jing

doi:10.1145/3704198.3704213

Forecasting Influenza-like Illness in Compulsory Education Schools in Macau Using Google Search Queries and Machine Learning Models

Ying Chen (Co-first Author)
, Jinsong Luo (Co-first Author)
, Deyi Liang
, Yewei Xie
, Yi Zhou
, Dan Wu
, Weibin Cheng
, Weiming Tang
, Fengshi Jing^*

^*Corresponding author for this work

Department of Data Science

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

19 Downloads (CityUHK Scholars)

Abstract

This study investigates the predictive utility of Google search queries for forecasting influenza-like illness (ILI) in compulsory education schools in Macau. The increasing availability of online data offers a novel approach to health surveillance, potentially improving the timeliness and accuracy of ILI predictions in educational settings. We employed three machine learning models: extreme gradient boosting (XGBoost), least absolute shrinkage and selection operator (LASSO), and ridge regression (Ridge), to forecast the ILI-caused absence rate in kindergartens, primary schools, and middle schools one and two weeks in advance in Macau. The covariates for these models include Google search queries and historical ILI data. This is Macau's first study to apply machine learning methodologies and Internet big data to the surveillance of ILI in educational institutions. Our approach offers a feasible and computationally efficient method for forecasting ILI in Macau's compulsory education schools. This methodology could be adapted for use in other regions with limited influenza data resources, providing a valuable tool for public health planning and response. © 2024 Copyright held by the owner/author(s).

Original language	English
Title of host publication	ICBBS' 24
Subtitle of host publication	Proceedings of the 2024 13th International Conference on Bioinformatics and Biomedical Science
Publisher	Association for Computing Machinery
Pages	123-128
ISBN (Print)	979-8-4007-1795-6
DOIs	https://doi.org/10.1145/3704198.3704213
Publication status	Published - Oct 2024
Event	13th International Conference on Bioinformatics and Biomedical Science, ICBBS 2024 - Hong Kong, Hong Kong, China Duration: 18 Oct 2024 → 20 Oct 2024

Publication series

Name	ICBBS - Proceedings of the International Conference on Bioinformatics and Biomedical Science

Conference

Conference	13th International Conference on Bioinformatics and Biomedical Science, ICBBS 2024
Place	Hong Kong, China
City	Hong Kong
Period	18/10/24 → 20/10/24

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Research Keywords

Biomedical data analysis
Health informatics
Infectious diseases surveillance
Influenza
Machine learning

Publisher's Copyright Statement

This full text is made available under CC-BY 4.0. https://creativecommons.org/licenses/by/4.0/

Access Output

10.1145/3704198.3704213

277889997.pdfFinal Published version, 2 MBLicence: CC BY

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Chen, Y., Luo, J., Liang, D., Xie, Y., Zhou, Y., Wu, D., Cheng, W., Tang, W., & Jing, F. (2024). Forecasting Influenza-like Illness in Compulsory Education Schools in Macau Using Google Search Queries and Machine Learning Models. In ICBBS' 24: Proceedings of the 2024 13th International Conference on Bioinformatics and Biomedical Science (pp. 123-128). (ICBBS - Proceedings of the International Conference on Bioinformatics and Biomedical Science). Association for Computing Machinery. https://doi.org/10.1145/3704198.3704213

Chen, Ying ; Luo, Jinsong ; Liang, Deyi et al. / Forecasting Influenza-like Illness in Compulsory Education Schools in Macau Using Google Search Queries and Machine Learning Models. ICBBS' 24: Proceedings of the 2024 13th International Conference on Bioinformatics and Biomedical Science. Association for Computing Machinery, 2024. pp. 123-128 (ICBBS - Proceedings of the International Conference on Bioinformatics and Biomedical Science).

@inproceedings{cca204c426754fa7b6e7f12afb77159f,

title = "Forecasting Influenza-like Illness in Compulsory Education Schools in Macau Using Google Search Queries and Machine Learning Models",

abstract = "This study investigates the predictive utility of Google search queries for forecasting influenza-like illness (ILI) in compulsory education schools in Macau. The increasing availability of online data offers a novel approach to health surveillance, potentially improving the timeliness and accuracy of ILI predictions in educational settings. We employed three machine learning models: extreme gradient boosting (XGBoost), least absolute shrinkage and selection operator (LASSO), and ridge regression (Ridge), to forecast the ILI-caused absence rate in kindergartens, primary schools, and middle schools one and two weeks in advance in Macau. The covariates for these models include Google search queries and historical ILI data. This is Macau's first study to apply machine learning methodologies and Internet big data to the surveillance of ILI in educational institutions. Our approach offers a feasible and computationally efficient method for forecasting ILI in Macau's compulsory education schools. This methodology could be adapted for use in other regions with limited influenza data resources, providing a valuable tool for public health planning and response. {\textcopyright} 2024 Copyright held by the owner/author(s).",

keywords = "Biomedical data analysis, Health informatics, Infectious diseases surveillance, Influenza, Machine learning",

author = "Ying Chen and Jinsong Luo and Deyi Liang and Yewei Xie and Yi Zhou and Dan Wu and Weibin Cheng and Weiming Tang and Fengshi Jing",

year = "2024",

month = oct,

doi = "10.1145/3704198.3704213",

language = "English",

isbn = "979-8-4007-1795-6",

series = "ICBBS - Proceedings of the International Conference on Bioinformatics and Biomedical Science",

publisher = "Association for Computing Machinery",

pages = "123--128",

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note = "13th International Conference on Bioinformatics and Biomedical Science, ICBBS 2024 ; Conference date: 18-10-2024 Through 20-10-2024",

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Chen, Y, Luo, J, Liang, D, Xie, Y, Zhou, Y, Wu, D, Cheng, W, Tang, W & Jing, F 2024, Forecasting Influenza-like Illness in Compulsory Education Schools in Macau Using Google Search Queries and Machine Learning Models. in ICBBS' 24: Proceedings of the 2024 13th International Conference on Bioinformatics and Biomedical Science. ICBBS - Proceedings of the International Conference on Bioinformatics and Biomedical Science, Association for Computing Machinery, pp. 123-128, 13th International Conference on Bioinformatics and Biomedical Science, ICBBS 2024, Hong Kong, Hong Kong, China, 18/10/24. https://doi.org/10.1145/3704198.3704213

Forecasting Influenza-like Illness in Compulsory Education Schools in Macau Using Google Search Queries and Machine Learning Models. / Chen, Ying (Co-first Author); Luo, Jinsong (Co-first Author); Liang, Deyi et al.
ICBBS' 24: Proceedings of the 2024 13th International Conference on Bioinformatics and Biomedical Science. Association for Computing Machinery, 2024. p. 123-128 (ICBBS - Proceedings of the International Conference on Bioinformatics and Biomedical Science).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

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T1 - Forecasting Influenza-like Illness in Compulsory Education Schools in Macau Using Google Search Queries and Machine Learning Models

AU - Chen, Ying

AU - Luo, Jinsong

AU - Liang, Deyi

AU - Xie, Yewei

AU - Zhou, Yi

AU - Wu, Dan

AU - Cheng, Weibin

AU - Tang, Weiming

AU - Jing, Fengshi

PY - 2024/10

Y1 - 2024/10

N2 - This study investigates the predictive utility of Google search queries for forecasting influenza-like illness (ILI) in compulsory education schools in Macau. The increasing availability of online data offers a novel approach to health surveillance, potentially improving the timeliness and accuracy of ILI predictions in educational settings. We employed three machine learning models: extreme gradient boosting (XGBoost), least absolute shrinkage and selection operator (LASSO), and ridge regression (Ridge), to forecast the ILI-caused absence rate in kindergartens, primary schools, and middle schools one and two weeks in advance in Macau. The covariates for these models include Google search queries and historical ILI data. This is Macau's first study to apply machine learning methodologies and Internet big data to the surveillance of ILI in educational institutions. Our approach offers a feasible and computationally efficient method for forecasting ILI in Macau's compulsory education schools. This methodology could be adapted for use in other regions with limited influenza data resources, providing a valuable tool for public health planning and response. © 2024 Copyright held by the owner/author(s).

AB - This study investigates the predictive utility of Google search queries for forecasting influenza-like illness (ILI) in compulsory education schools in Macau. The increasing availability of online data offers a novel approach to health surveillance, potentially improving the timeliness and accuracy of ILI predictions in educational settings. We employed three machine learning models: extreme gradient boosting (XGBoost), least absolute shrinkage and selection operator (LASSO), and ridge regression (Ridge), to forecast the ILI-caused absence rate in kindergartens, primary schools, and middle schools one and two weeks in advance in Macau. The covariates for these models include Google search queries and historical ILI data. This is Macau's first study to apply machine learning methodologies and Internet big data to the surveillance of ILI in educational institutions. Our approach offers a feasible and computationally efficient method for forecasting ILI in Macau's compulsory education schools. This methodology could be adapted for use in other regions with limited influenza data resources, providing a valuable tool for public health planning and response. © 2024 Copyright held by the owner/author(s).

KW - Biomedical data analysis

KW - Health informatics

KW - Infectious diseases surveillance

KW - Influenza

KW - Machine learning

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U2 - 10.1145/3704198.3704213

DO - 10.1145/3704198.3704213

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 979-8-4007-1795-6

T3 - ICBBS - Proceedings of the International Conference on Bioinformatics and Biomedical Science

SP - 123

EP - 128

BT - ICBBS' 24

PB - Association for Computing Machinery

T2 - 13th International Conference on Bioinformatics and Biomedical Science, ICBBS 2024

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ER -

Chen Y, Luo J, Liang D, Xie Y, Zhou Y, Wu D et al. Forecasting Influenza-like Illness in Compulsory Education Schools in Macau Using Google Search Queries and Machine Learning Models. In ICBBS' 24: Proceedings of the 2024 13th International Conference on Bioinformatics and Biomedical Science. Association for Computing Machinery. 2024. p. 123-128. (ICBBS - Proceedings of the International Conference on Bioinformatics and Biomedical Science). doi: 10.1145/3704198.3704213

Forecasting Influenza-like Illness in Compulsory Education Schools in Macau Using Google Search Queries and Machine Learning Models

Abstract

Publication series

Conference

UN SDGs

Research Keywords

Publisher's Copyright Statement

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