Modeling impact of Covid-19 pandemic on spatial temporal human mobility

•A novel computational and time efficient methodology for forecasting human mobility under conditions of NPIs imposition is developed.•The methodology combines data quality improvement and dimensionality reduction techniques with a DNN algorithm.•Training employs grid search hyperparameter tuning an...

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Veröffentlicht in:	Expert systems with applications Jg. 297; S. 128972
1. Verfasser:	Titus-Glover, Leslie
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.02.2026
Schlagworte:	Artificial intelligence Connection weight relative attribute importance method Deep neural networks Epidemiological surveillance, resilience, vulnerability, and non-pharmacological intervention attributes Pearson and Cramér’s V correlation analysis Spatial temporal human mobility ANN CNN DNN NPI GLM KNN GPS SVM Spatial temporal human mobility SVR MLP DBN RNN IHME Connection weight relative attribute importance method SEIR Epidemiological surveillance, resilience, vulnerability, and non-pharmacological intervention attributes ML WHO R2 Deep neural networks LSTM Covid-19 AAE OxCGRT AI UMA Pearson and Cramér’s V correlation analysis MLPNN RF ARIMA U.S FAST Artificial intelligence MRRG
ISSN:	0957-4174
Online-Zugang:	Volltext
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