A new RNN based machine learning model to forecast COVID-19 incidence, enhanced by the use of mobility data from the bike-sharing service in Madrid.

Muñoz-Organero, Mario; Callejo, Patricia; Hombrados-Herrera, Miguel Ángel

Muñoz-Organero, Mario; Callejo, Patricia; Hombrados-Herrera, Miguel Ángel.

Afiliación

Muñoz-Organero M; Telematic Engineering Department, Universidad Carlos III de Madrid, Leganes, 28911, Madrid, Spain.
Callejo P; Telematic Engineering Department, Universidad Carlos III de Madrid, Leganes, 28911, Madrid, Spain.
Hombrados-Herrera MÁ; Telematic Engineering Department, Universidad Carlos III de Madrid, Leganes, 28911, Madrid, Spain.

Heliyon ; 9(6): e17625, 2023 Jun.

Article en En | MEDLINE | ID: mdl-37389062

Palabras clave

COVID-19 short term forecast; LSTM machine learning models; Mobility enhanced models; Open data driven models

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Texto completo: 1 Base de datos: MEDLINE Tipo de estudio: Incidence_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Heliyon Año: 2023 Tipo del documento: Article

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