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

Jing, Nan; Shi, Zijing; Hu, Yi; Yuan, Ji

Jing, Nan; Shi, Zijing; Hu, Yi; Yuan, Ji.

Afiliação

Jing N; SHU-UTS SILC Business School, Shanghai University, Shanghai, 201800 China.
Shi Z; SHU-UTS SILC Business School, Shanghai University, Shanghai, 201800 China.
Hu Y; SHU-UTS SILC Business School, Shanghai University, Shanghai, 201800 China.
Yuan J; Onewo Space-Tech Service Co., Ltd., Shenzhen, 518049 China.

Appl Intell (Dordr) ; 52(3): 3303-3318, 2022.

Article em En | MEDLINE | ID: mdl-34764608

Palavras-chave

Coronavirus disease 2019 (COVID-19); Dual-stage attention-based recurrent neural network (DA-RNN); SHapley additive exPlanation (SHAP); eXtreme gradient boosting (XGB)

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prevalence_studies / Prognostic_studies Idioma: En Revista: Appl Intell (Dordr) Ano de publicação: 2022 Tipo de documento: Article País de publicação: Holanda

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