Cross-sectional analysis and data-driven forecasting of confirmed COVID-19 cases.

Jing, N; Shi, Z; Hu, Y; Yuan, J

Cross-sectional analysis and data-driven forecasting of confirmed COVID-19 cases.

Jing, N Shi, Z Hu, Y Yuan, J

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Publisher:: SPRINGER
Publication Type:: Journal Article
Citation:: Appl Intell (Dordr), 2022, 52, (3), pp. 3303-3318
Issue Date:: 2022

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Field	Value	Language
dc.contributor.author	Jing, N
dc.contributor.author	Shi, Z
dc.contributor.author	Hu, Y
dc.contributor.author	Yuan, J
dc.date.accessioned	2023-07-06T10:27:00Z
dc.date.available	2021-06-07
dc.date.available	2023-07-06T10:27:00Z
dc.date.issued	2022
dc.identifier.citation	Appl Intell (Dordr), 2022, 52, (3), pp. 3303-3318
dc.identifier.issn	0924-669X
dc.identifier.issn	1573-7497
dc.identifier.uri	http://hdl.handle.net/10453/171267
dc.description.abstract	The coronavirus disease 2019 (COVID-19) is rapidly becoming one of the leading causes for mortality worldwide. Various models have been built in previous works to study the spread characteristics and trends of the COVID-19 pandemic. Nevertheless, due to the limited information and data source, the understanding of the spread and impact of the COVID-19 pandemic is still restricted. Therefore, within this paper not only daily historical time-series data of COVID-19 have been taken into account during the modeling, but also regional attributes, e.g., geographic and local factors, which may have played an important role on the confirmed COVID-19 cases in certain regions. In this regard, this study then conducts a comprehensive cross-sectional analysis and data-driven forecasting on this pandemic. The critical features, which has the significant influence on the infection rate of COVID-19, is determined by employing XGB (eXtreme Gradient Boosting) algorithm and SHAP (SHapley Additive exPlanation) and the comparison is carried out by utilizing the RF (Random Forest) and LGB (Light Gradient Boosting) models. To forecast the number of confirmed COVID-19 cases more accurately, a Dual-Stage Attention-Based Recurrent Neural Network (DA-RNN) is applied in this paper. This model has better performance than SVR (Support Vector Regression) and the encoder-decoder network on the experimental dataset. And the model performance is evaluated in the light of three statistic metrics, i.e. MAE, RMSE and R 2. Furthermore, this study is expected to serve as meaningful references for the control and prevention of the COVID-19 pandemic.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	SPRINGER
dc.relation.ispartof	Appl Intell (Dordr)
dc.relation.isbasedon	10.1007/s10489-021-02616-8
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.classification	46 Information and computing sciences
dc.title	Cross-sectional analysis and data-driven forecasting of confirmed COVID-19 cases.
dc.type	Journal Article
utslib.citation.volume	52
utslib.location.activity	Netherlands
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.copyright.status	closed_access	*
dc.date.updated	2023-07-06T10:26:56Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	52
utslib.citation.issue	3

Abstract:

The coronavirus disease 2019 (COVID-19) is rapidly becoming one of the leading causes for mortality worldwide. Various models have been built in previous works to study the spread characteristics and trends of the COVID-19 pandemic. Nevertheless, due to the limited information and data source, the understanding of the spread and impact of the COVID-19 pandemic is still restricted. Therefore, within this paper not only daily historical time-series data of COVID-19 have been taken into account during the modeling, but also regional attributes, e.g., geographic and local factors, which may have played an important role on the confirmed COVID-19 cases in certain regions. In this regard, this study then conducts a comprehensive cross-sectional analysis and data-driven forecasting on this pandemic. The critical features, which has the significant influence on the infection rate of COVID-19, is determined by employing XGB (eXtreme Gradient Boosting) algorithm and SHAP (SHapley Additive exPlanation) and the comparison is carried out by utilizing the RF (Random Forest) and LGB (Light Gradient Boosting) models. To forecast the number of confirmed COVID-19 cases more accurately, a Dual-Stage Attention-Based Recurrent Neural Network (DA-RNN) is applied in this paper. This model has better performance than SVR (Support Vector Regression) and the encoder-decoder network on the experimental dataset. And the model performance is evaluated in the light of three statistic metrics, i.e. MAE, RMSE and R 2. Furthermore, this study is expected to serve as meaningful references for the control and prevention of the COVID-19 pandemic.

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http://hdl.handle.net/10453/171267