Single and Multilayer LSTM Models for Positive COVID-19 Cases Prediction

Asmae Berhich, Fatima-Zahra Belouadha, Asmae El Kassiri

2020

Abstract

COVID-19 is a global pandemic that has been reported first in Wuhan, China in December 2019. According to the World Health Organization (WHO), around 1 out of every 5 people who get COVID-19 get seriously ill and develop difficulty breathing. The virus is spreading from one person to others causing fear and a big struggle in the world. Building accurate learning models for forecasting positive new cases would help to better manage the crisis situation thereby helping to fight COVID-19 and save lives. For this purpose, we use LSTM (Long Short Time Memory) model in Morocco’s case and evaluate its performance according to six architectures. The results demonstrate that the architecture with three cells outperforms the other models and shows the best fitting.

Paper Citation

in Harvard Style

Berhich A., Belouadha F. and Kassiri A. (2020). Single and Multilayer LSTM Models for Positive COVID-19 Cases Prediction.In Proceedings of the 2nd International Conference on Advanced Technologies for Humanity - Volume 1: ICATH, ISBN 978-989-758-514-2, pages 27-34. DOI: 10.5220/0010426900270034

in Bibtex Style

@conference{icath20,
author={Asmae Berhich and Fatima-Zahra Belouadha and Asmae El Kassiri},
title={Single and Multilayer LSTM Models for Positive COVID-19 Cases Prediction},
booktitle={Proceedings of the 2nd International Conference on Advanced Technologies for Humanity - Volume 1: ICATH,},
year={2020},
pages={27-34},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010426900270034},
isbn={978-989-758-514-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Advanced Technologies for Humanity - Volume 1: ICATH,
TI - Single and Multilayer LSTM Models for Positive COVID-19 Cases Prediction
SN - 978-989-758-514-2
AU - Berhich A.
AU - Belouadha F.
AU - Kassiri A.
PY - 2020
SP - 27
EP - 34
DO - 10.5220/0010426900270034