IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2021i7p3834-d531027.html
   My bibliography  Save this article

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

Author

Listed:
  • Mohammad Reza Davahli

    (Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL 32816, USA)

  • Krzysztof Fiok

    (Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL 32816, USA)

  • Waldemar Karwowski

    (Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL 32816, USA)

  • Awad M. Aljuaid

    (Department of Industrial Engineering, College of Engineering, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia)

  • Redha Taiar

    (Department of Sports Sciences, MATIM, Université de Reims Champagne-Ardenne, 51100 Reims, France)

Abstract

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 R t 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 R t 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.

Suggested Citation

  • Mohammad Reza Davahli & Krzysztof Fiok & Waldemar Karwowski & Awad M. Aljuaid & Redha Taiar, 2021. "Predicting the Dynamics of the COVID-19 Pandemic in the United States Using Graph Theory-Based Neural Networks," IJERPH, MDPI, vol. 18(7), pages 1-12, April.
  • Handle: RePEc:gam:jijerp:v:18:y:2021:i:7:p:3834-:d:531027
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/7/3834/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/18/7/3834/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Katelyn M Gostic & Lauren McGough & Edward B Baskerville & Sam Abbott & Keya Joshi & Christine Tedijanto & Rebecca Kahn & Rene Niehus & James A Hay & Pablo M De Salazar & Joel Hellewell & Sophie Meaki, 2020. "Practical considerations for measuring the effective reproductive number, Rt," PLOS Computational Biology, Public Library of Science, vol. 16(12), pages 1-21, December.
    2. Mohammad Reza Davahli & Waldemar Karwowski & Sevil Sonmez & Yorghos Apostolopoulos, 2020. "The Hospitality Industry in the Face of the COVID-19 Pandemic: Current Topics and Research Methods," IJERPH, MDPI, vol. 17(20), pages 1-20, October.
    3. Derek A.T. Cummings & Rafael A. Irizarry & Norden E. Huang & Timothy P. Endy & Ananda Nisalak & Kumnuan Ungchusak & Donald S. Burke, 2004. "Travelling waves in the occurrence of dengue haemorrhagic fever in Thailand," Nature, Nature, vol. 427(6972), pages 344-347, January.
    4. Lalmuanawma, Samuel & Hussain, Jamal & Chhakchhuak, Lalrinfela, 2020. "Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yina Yao & Pei Wang & Hui Zhang, 2023. "The Impact of Preventive Strategies Adopted during Large Events on the COVID-19 Pandemic: A Case Study of the Tokyo Olympics to Provide Guidance for Future Large Events," IJERPH, MDPI, vol. 20(3), pages 1-22, January.
    2. Nabin Sapkota & Atsuo Murata & Waldemar Karwowski & Mohammad Reza Davahli & Krzysztof Fiok & Awad M. Aljuaid & Tadeusz Marek & Tareq Ahram, 2022. "The Chaotic Behavior of the Spread of Infection during the COVID-19 Pandemic in Japan," IJERPH, MDPI, vol. 19(19), pages 1-16, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Aldo Carranza & Marcel Goic & Eduardo Lara & Marcelo Olivares & Gabriel Y. Weintraub & Julio Covarrubia & Cristian Escobedo & Natalia Jara & Leonardo J. Basso, 2022. "The Social Divide of Social Distancing: Shelter-in-Place Behavior in Santiago During the Covid-19 Pandemic," Management Science, INFORMS, vol. 68(3), pages 2016-2027, March.
    2. Erickson, Richard A. & Presley, Steven M. & Allen, Linda J.S. & Long, Kevin R. & Cox, Stephen B., 2010. "A dengue model with a dynamic Aedes albopictus vector population," Ecological Modelling, Elsevier, vol. 221(24), pages 2899-2908.
    3. Ortiz-Barrios, Miguel & Arias-Fonseca, Sebastián & Ishizaka, Alessio & Barbati, Maria & Avendaño-Collante, Betty & Navarro-Jiménez, Eduardo, 2023. "Artificial intelligence and discrete-event simulation for capacity management of intensive care units during the Covid-19 pandemic: A case study," Journal of Business Research, Elsevier, vol. 160(C).
    4. Jiao Zhang & Qingcheng Zeng, 2017. "Modelling the volatility of the tanker freight market based on improved empirical mode decomposition," Applied Economics, Taylor & Francis Journals, vol. 49(17), pages 1655-1667, April.
    5. Phaisarn Jeefoo & Nitin Kumar Tripathi & Marc Souris, 2010. "Spatio-Temporal Diffusion Pattern and Hotspot Detection of Dengue in Chachoengsao Province, Thailand," IJERPH, MDPI, vol. 8(1), pages 1-24, December.
    6. Reese Richardson & Emile Jorgensen & Philip Arevalo & Tobias M. Holden & Katelyn M. Gostic & Massimo Pacilli & Isaac Ghinai & Shannon Lightner & Sarah Cobey & Jaline Gerardin, 2022. "Tracking changes in SARS-CoV-2 transmission with a novel outpatient sentinel surveillance system in Chicago, USA," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    7. Anders Håkansson & Karin Moesch & Caroline Jönsson & Göran Kenttä, 2020. "Potentially Prolonged Psychological Distress from Postponed Olympic and Paralympic Games during COVID-19—Career Uncertainty in Elite Athletes," IJERPH, MDPI, vol. 18(1), pages 1-9, December.
    8. Quirine A ten Bosch & Brajendra K Singh & Muhammad R A Hassan & Dave D Chadee & Edwin Michael, 2016. "The Role of Serotype Interactions and Seasonality in Dengue Model Selection and Control: Insights from a Pattern Matching Approach," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 10(5), pages 1-25, May.
    9. Maria Bekker‐Nielsen Dunbar & Felix Hofmann & Leonhard Held & the SUSPend modelling consortium, 2022. "Assessing the effect of school closures on the spread of COVID‐19 in Zurich," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(S1), pages 131-142, November.
    10. Marcel Lucas Chee & Marcus Eng Hock Ong & Fahad Javaid Siddiqui & Zhongheng Zhang & Shir Lynn Lim & Andrew Fu Wah Ho & Nan Liu, 2021. "Artificial Intelligence Applications for COVID-19 in Intensive Care and Emergency Settings: A Systematic Review," IJERPH, MDPI, vol. 18(9), pages 1-15, April.
    11. James, Nick & Menzies, Max, 2023. "Collective infectivity of the pandemic over time and association with vaccine coverage and economic development," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).
    12. Palatella, Luigi & Vanni, Fabio & Lambert, David, 2021. "A phenomenological estimate of the true scale of CoViD-19 from primary data," Chaos, Solitons & Fractals, Elsevier, vol. 146(C).
    13. Jonas E. Arias & Jesús Fernández-Villaverde & Juan F. Rubio-Ramirez & Minchul Shin, 2021. "Bayesian Estimation of Epidemiological Models: Methods, Causality, and Policy Trade-Offs," Working Papers 21-18, Federal Reserve Bank of Philadelphia.
    14. Srinka Basu & Sugata Sen, 2023. "COVID 19 Pandemic, Socio-Economic Behaviour and Infection Characteristics: An Inter-Country Predictive Study Using Deep Learning," Computational Economics, Springer;Society for Computational Economics, vol. 61(2), pages 645-676, February.
    15. Johann Maria, 2023. "CSR strategy in the hospitality industry: from the COVID-19 pandemic crisis to recovery," International Journal of Contemporary Management, Sciendo, vol. 59(1), pages 1-11, March.
    16. Catherine A. Lippi & Anna M. Stewart-Ibarra & Ángel G. Muñoz & Mercy J. Borbor-Cordova & Raúl Mejía & Keytia Rivero & Katty Castillo & Washington B. Cárdenas & Sadie J. Ryan, 2018. "The Social and Spatial Ecology of Dengue Presence and Burden during an Outbreak in Guayaquil, Ecuador, 2012," IJERPH, MDPI, vol. 15(4), pages 1-15, April.
    17. Sini V. Pillai & Ranjith S. Kumar, 2021. "The role of data-driven artificial intelligence on COVID-19 disease management in public sphere: a review," DECISION: Official Journal of the Indian Institute of Management Calcutta, Springer;Indian Institute of Management Calcutta, vol. 48(4), pages 375-389, December.
    18. Diana Rose E. Ranoa & Robin L. Holland & Fadi G. Alnaji & Kelsie J. Green & Leyi Wang & Richard L. Fredrickson & Tong Wang & George N. Wong & Johnny Uelmen & Sergei Maslov & Zachary J. Weiner & Alexei, 2022. "Mitigation of SARS-CoV-2 transmission at a large public university," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
    19. Bradley S Price & Maryam Khodaverdi & Adam Halasz & Brian Hendricks & Wesley Kimble & Gordon S Smith & Sally L Hodder, 2021. "Predicting increases in COVID-19 incidence to identify locations for targeted testing in West Virginia: A machine learning enhanced approach," PLOS ONE, Public Library of Science, vol. 16(11), pages 1-16, November.
    20. Faizeh Hatami & Shi Chen & Rajib Paul & Jean-Claude Thill, 2022. "Simulating and Forecasting the COVID-19 Spread in a U.S. Metropolitan Region with a Spatial SEIR Model," IJERPH, MDPI, vol. 19(23), pages 1-16, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:18:y:2021:i:7:p:3834-:d:531027. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.