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Article

Predicting the Dynamics of the COVID-19 Pandemic in the United States Using Graph Theory-Based Neural Networks

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Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL 32816, USA
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Department of Industrial Engineering, College of Engineering, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
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Department of Sports Sciences, MATIM, Université de Reims Champagne-Ardenne, 51100 Reims, France
*
Author to whom correspondence should be addressed.
Academic Editor: Paul B. Tchounwou
Int. J. Environ. Res. Public Health 2021, 18(7), 3834; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18073834
Received: 18 March 2021 / Revised: 28 March 2021 / Accepted: 3 April 2021 / Published: 6 April 2021
The COVID-19 pandemic has had unprecedented social and economic consequences in the United States. Therefore, accurately predicting the dynamics of the pandemic can be very beneficial. Two main elements required for developing reliable predictions include: (1) a predictive model and (2) an indicator of the current condition and status of the pandemic. As a pandemic indicator, we used the effective reproduction number (Rt), which is defined as the number of new infections transmitted by a single contagious individual in a population that may no longer be fully susceptible. To bring the pandemic under control, Rt must be less than one. To eliminate the pandemic, Rt should be close to zero. Therefore, this value may serve as a strong indicator of the current status of the pandemic. For a predictive model, we used graph neural networks (GNNs), a method that combines graphical analysis with the structure of neural networks. We developed two types of GNN models, including: (1) graph-theory-based neural networks (GTNN) and (2) neighborhood-based neural networks (NGNN). The nodes in both graphs indicated individual states in the United States. While the GTNN model’s edges document functional connectivity between states, those in the NGNN model link neighboring states to one another. We trained both models with Rt numbers collected over the previous four days and asked them to predict the following day for all states in the United States. The performance of these models was evaluated with the datasets that included Rt values reflecting conditions from 22 January through 26 November 2020 (before the start of COVID-19 vaccination in the United States). To determine the efficiency, we compared the results of two models with each other and with those generated by a baseline Long short-term memory (LSTM) model. The results indicated that the GTNN model outperformed both the NGNN and LSTM models for predicting Rt. View Full-Text
Keywords: artificial intelligence; COVID-19 pandemic; graph neural networks; time series analysis artificial intelligence; COVID-19 pandemic; graph neural networks; time series analysis
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MDPI and ACS Style

Davahli, M.R.; Fiok, K.; Karwowski, W.; Aljuaid, A.M.; Taiar, R. Predicting the Dynamics of the COVID-19 Pandemic in the United States Using Graph Theory-Based Neural Networks. Int. J. Environ. Res. Public Health 2021, 18, 3834. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18073834

AMA Style

Davahli MR, Fiok K, Karwowski W, Aljuaid AM, Taiar R. Predicting the Dynamics of the COVID-19 Pandemic in the United States Using Graph Theory-Based Neural Networks. International Journal of Environmental Research and Public Health. 2021; 18(7):3834. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18073834

Chicago/Turabian Style

Davahli, Mohammad R., Krzysztof Fiok, Waldemar Karwowski, Awad M. Aljuaid, and Redha Taiar. 2021. "Predicting the Dynamics of the COVID-19 Pandemic in the United States Using Graph Theory-Based Neural Networks" International Journal of Environmental Research and Public Health 18, no. 7: 3834. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18073834

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