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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Main Authors:	Nada Osman, Marwan Torki, Mustafa ElNainay, Abdulrahman AlHaidari, Emad Nabil
Format:	Article
Language:	English
Published:	Elsevier 2021-08-01
Series:	Alexandria Engineering Journal
Subjects:	COVID-19 Community mobility Time series prediction Autoregression Convolution neural network Long short-term memory network
Online Access:	http://www.sciencedirect.com/science/article/pii/S1110016821001010

id	doaj-c164c1de42ee4e2099872b081156c31c
record_format	Article
spelling	doaj-c164c1de42ee4e2099872b081156c31c2021-06-02T15:30:33ZengElsevierAlexandria Engineering Journal1110-01682021-08-0160436793692Artificial Intelligence-Based Model for Predicting the Effect of Governments’ Measures on Community MobilityNada Osman0Marwan Torki1Mustafa ElNainay2Abdulrahman AlHaidari3Emad Nabil4Department of Computer and Systems Engineering, Alexandria University, Alexandria, EgyptDepartment of Computer and Systems Engineering, Alexandria University, Alexandria, EgyptFaculty of Computer Science and Information Systems, Islamic University of Madinah, Madinah, Saudi Arabia; Department of Computer and Systems Engineering, Alexandria University, Alexandria, Egypt; Faculty of Computer Science and Engineering, AlAlamein International University, Matrouh, EgyptFaculty of Computer Science and Information Systems, Islamic University of Madinah, Madinah, Saudi ArabiaFaculty of Computer Science and Information Systems, Islamic University of Madinah, Madinah, Saudi Arabia; Faculty of Computers and Artificial Intelligence, Cairo University, Giza, EgyptMobility 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 5.3. For Egypt and Saudi Arabia, the model achieved an MAE loss of 4.6 and 3.7 consecutively.http://www.sciencedirect.com/science/article/pii/S1110016821001010COVID-19Community mobilityTime series predictionAutoregressionConvolution neural networkLong short-term memory network
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Nada Osman Marwan Torki Mustafa ElNainay Abdulrahman AlHaidari Emad Nabil
spellingShingle	Nada Osman Marwan Torki Mustafa ElNainay Abdulrahman AlHaidari Emad Nabil Artificial Intelligence-Based Model for Predicting the Effect of Governments’ Measures on Community Mobility Alexandria Engineering Journal COVID-19 Community mobility Time series prediction Autoregression Convolution neural network Long short-term memory network
author_facet	Nada Osman Marwan Torki Mustafa ElNainay Abdulrahman AlHaidari Emad Nabil
author_sort	Nada Osman
title	Artificial Intelligence-Based Model for Predicting the Effect of Governments’ Measures on Community Mobility
title_short	Artificial Intelligence-Based Model for Predicting the Effect of Governments’ Measures on Community Mobility
title_full	Artificial Intelligence-Based Model for Predicting the Effect of Governments’ Measures on Community Mobility
title_fullStr	Artificial Intelligence-Based Model for Predicting the Effect of Governments’ Measures on Community Mobility
title_full_unstemmed	Artificial Intelligence-Based Model for Predicting the Effect of Governments’ Measures on Community Mobility
title_sort	artificial intelligence-based model for predicting the effect of governments’ measures on community mobility
publisher	Elsevier
series	Alexandria Engineering Journal
issn	1110-0168
publishDate	2021-08-01
description	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 5.3. For Egypt and Saudi Arabia, the model achieved an MAE loss of 4.6 and 3.7 consecutively.
topic	COVID-19 Community mobility Time series prediction Autoregression Convolution neural network Long short-term memory network
url	http://www.sciencedirect.com/science/article/pii/S1110016821001010
work_keys_str_mv	AT nadaosman artificialintelligencebasedmodelforpredictingtheeffectofgovernmentsmeasuresoncommunitymobility AT marwantorki artificialintelligencebasedmodelforpredictingtheeffectofgovernmentsmeasuresoncommunitymobility AT mustafaelnainay artificialintelligencebasedmodelforpredictingtheeffectofgovernmentsmeasuresoncommunitymobility AT abdulrahmanalhaidari artificialintelligencebasedmodelforpredictingtheeffectofgovernmentsmeasuresoncommunitymobility AT emadnabil artificialintelligencebasedmodelforpredictingtheeffectofgovernmentsmeasuresoncommunitymobility
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Artificial Intelligence-Based Model for Predicting the Effect of Governments’ Measures on Community Mobility

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