Artificial Intelligence-Based Model for Predicting the Effect of Governments’ Measures on Community Mobility

Mobility is considered one of the main reasons for the COVID-19 spread. Predicting the effect of control measures on mobility is essential to apply effective decisions. This work proposes an AI-based model for mobility changes prediction. The proposed CNN-LSTM with Autoregression has achieved the be...

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Detalles Bibliográficos
Autores principales: Osman, Nada, Torki, Marwan, ElNainay, Mustafa, AlHaidari, Abdulrahman, Nabil, Emad
Formato: Online Artículo Texto
Lenguaje:English
Publicado: THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University. 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8788186/
http://dx.doi.org/10.1016/j.aej.2021.02.029
Descripción
Sumario:Mobility is considered one of the main reasons for the COVID-19 spread. Predicting the effect of control measures on mobility is essential to apply effective decisions. This work proposes an AI-based model for mobility changes prediction. The proposed CNN-LSTM with Autoregression has achieved the best results compared to other investigated models. Results show that the proposed model can predict the effect of precaution control measures on future community mobility with minimum loss. The mean absolute error over all countries in the study is [Formula: see text]. For Egypt and Saudi Arabia, the model achieved an MAE loss of [Formula: see text] and [Formula: see text] consecutively.

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