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A novel bidirectional LSTM deep learning approach for COVID-19 forecasting
COVID-19 has resulted in significant morbidity and mortality globally. We develop a model that uses data from thirty days before a fixed time point to forecast the daily number of new COVID-19 cases fourteen days later in the early stages of the pandemic. Various time-dependent factors including the...
| Autores principales: | , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Nature Publishing Group UK
2023
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10589260/ https://www.ncbi.nlm.nih.gov/pubmed/37863921 http://dx.doi.org/10.1038/s41598-023-44924-8 |