Convolutional neural networks and temporal CNNs for COVID-19 forecasting in France
| Autor: | Luiz Angelo Steffenel, Lucas Mohimont, François Alin, Michaël Krajecki, Amine Chemchem |
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| Přispěvatelé: | Laboratoire d'Informatique en Calcul Intensif et Image pour la Simulation (LICIIS), Université de Reims Champagne-Ardenne (URCA) |
| Rok vydání: | 2021 |
| Předmět: |
Scheme (programming language)
Coronavirus disease 2019 (COVID-19) Computer science 02 engineering and technology [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE] Machine learning computer.software_genre Convolutional neural network Article [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] Set (abstract data type) Artificial Intelligence 0202 electrical engineering electronic engineering information engineering National level computer.programming_language business.industry Deep learning COVID-19 Transfer learning 3. Good health Quantile regression Convolutional neural networks 020201 artificial intelligence & image processing Artificial intelligence business Transfer of learning Temporal convolutional network computer |
| Zdroj: | Applied Intelligence Applied Intelligence, Springer Verlag (Germany), In press HAL |
| ISSN: | 1573-7497 0924-669X |
| DOI: | 10.1007/s10489-021-02359-6 |
| Popis: | International audience; THIS IS A PREPRINT for Applied Intelligence. The revised version is available here: https://link.springer.com/article/10.1007/s10489-021-02359-6This paper examines multiple CNN-based (Convolutional Neural Network) models for Covid-19 forecast developed by our research team during the French lockdown. In an effort to understand and predict both the epidemic evolution and the impacts of this disease, we conceived models for multiple indicators: daily or cumulative confirmed cases, hospitalizations, hospitalizations with artificial ventilation, recoveries and deaths. In spite of the limited data available when the lockdown was declared, we achieved good short-term performances at the national level with a classical CNN for hospitalizations, leading to its integration into a hospitalizations surveillance tool after the lockdown ended. Also, A Temporal Convolutional Network with quantile regression was found successful at predicting multiple Covid-19 indicators at the national level by using data available at different scales (worldwide, national, regional). The accuracy of the regional predictions was improved by using a hierarchical pre-training scheme, and an efficient parallel implementation allows for quick training of multiple regional models. The resulting set of models represent a powerful tool for short-term Covid-19 forecasting at different geographical scales, complementing the toolboxes used by health organizations in France. |
| Databáze: | OpenAIRE |
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