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Nesting the SIRV model with NAR, LSTM and statistical methods to fit and predict COVID-19 epidemic trend in Africa.
Liu, Xu-Dong; Wang, Wei; Yang, Yi; Hou, Bo-Han; Olasehinde, Toba Stephen; Feng, Ning; Dong, Xiao-Ping.
Afiliación
  • Liu XD; Faculty of Information Technology, Beijing University of Technology, Chaoyang District, Beijing, 100124, P. R. China.
  • Wang W; Key Laboratory of Computational Intelligence and Intelligent Systems, Beijing University of Technology, Chaoyang District, Beijing, 100124, P. R. China.
  • Yang Y; Faculty of Information Technology, Beijing University of Technology, Chaoyang District, Beijing, 100124, P. R. China.
  • Hou BH; Key Laboratory of Computational Intelligence and Intelligent Systems, Beijing University of Technology, Chaoyang District, Beijing, 100124, P. R. China.
  • Olasehinde TS; Faculty of Information Technology, Beijing University of Technology, Chaoyang District, Beijing, 100124, P. R. China.
  • Feng N; Faculty of Information Technology, Beijing University of Technology, Chaoyang District, Beijing, 100124, P. R. China.
  • Dong XP; Institute of Agricultural Economics and Development, Graduate School of Chinese Academy of Agricultural Sciences, 12 Zhongguancun South Street, Haidian District, Beijing, 100098, P. R. China.
BMC Public Health ; 23(1): 138, 2023 01 19.
Article en En | MEDLINE | ID: mdl-36658494
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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Epidemias / COVID-19 Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans País/Región como asunto: Africa Idioma: En Revista: BMC Public Health Asunto de la revista: SAUDE PUBLICA Año: 2023 Tipo del documento: Article
Texto completo
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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Epidemias / COVID-19 Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans País/Región como asunto: Africa Idioma: En Revista: BMC Public Health Asunto de la revista: SAUDE PUBLICA Año: 2023 Tipo del documento: Article