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Journal Articles Applied Intelligence Year : 2021

Convolutional Neural Networks and Temporal CNNs for Covid-19 Forecasting in France

(1) , , , ,
1
Amine Chemchem
Francois Alin
Michaël · Krajecki
Luiz Angelo Steffenel

Abstract

THIS IS A PREPRINT for Applied Intelligence. The revised version is available here: https://link.springer.com/article/10.1007/s10489-021-02359-6 This 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.
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Dates and versions

hal-02990153 , version 1 (05-11-2020)

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Lucas Mohimont, Amine Chemchem, Francois Alin, Michaël · Krajecki, Luiz Angelo Steffenel. Convolutional Neural Networks and Temporal CNNs for Covid-19 Forecasting in France. Applied Intelligence, 2021, ⟨10.1007/s10489-021-02359-6⟩. ⟨hal-02990153⟩

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