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http://conacyt.repositorioinstitucional.mx/jspui/handle/1000/8375| Forecasting COVID-19 New Cases Using Transformer Deep Learning Model | |
| Saurabh Patil Parisa Mollaei Amir Barati Farimani | |
| Acceso Abierto | |
| Atribución-NoComercial-SinDerivadas | |
| https://doi.org/10.1101/2023.11.02.23297976 | |
| https://www.medrxiv.org/content/10.1101/2023.11.02.23297976v1 | |
| Making accurate forecasting of COVID-19 cases is essential for healthcare systems, with more than 650 million cases as of 4 January,1 making it one of the worst in history. The goal of this research is to improve the precision of COVID-19 case predictions in Russia, India, and Brazil, a transformer-based model was developed. Several researchers have implemented a combination of CNNs and LSTMs, Long Short-Term Memory (LSTMs), and Convolutional Neural Networks (CNNs) to calculate the total number of COVID-19 cases. In this study, an effort was made to improve the correctness of the models by incorporating recent advancements in attention-based models for time-series forecasting. The resulting model was found to perform better than other existing models and showed improved accuracy in forecasting. Using the data from different countries and adapting it to the model will enhance its ability to support the worldwide effort to combat the pandemic by giving more precise projections of cases. | |
| 03-11-2023 | |
| Preimpreso | |
| Inglés | |
| Público en general | |
| VIRUS RESPIRATORIOS | |
| Aparece en las colecciones: | Materiales de Consulta y Comunicados Técnicos |
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| Fichero | Tamaño | Formato | |
|---|---|---|---|
| Forecasting COVID-19 New Cases Using Transformer Deep Learning Model.pdf | 1.78 MB | Adobe PDF | Visualizar/Abrir |