IDEAS home Printed from https://ideas.repec.org/a/pal/palcom/v9y2022i1d10.1057_s41599-022-01208-2.html
   My bibliography  Save this article

Impact of social determinants on COVID-19 infections: a comprehensive study from Saudi Arabia governorates

Author

Listed:
  • Abdallah S. A. Yaseen

    (National Centre for Social and Criminological Research)

Abstract

The last two years have been marked by the emergence of Coronavirus. The pandemic has spread in most countries, causing substantial changes all over the world. Many studies sought to analyze phenomena related to the pandemic from different perspectives. This study analyzes data from the governorates of the Kingdom of Saudi Arabia (the KSA), proposing a broad analysis that addresses three different research objectives. The first is to identify the main factors affecting the variations between KSA governorates in the cumulative number of COVID-19 infections. The study uses principal component regression. Results highlight the significant positive effects of the number of schools in each governorate, and classroom density within each school on the number of infections in the KSA. The second aim of this study is to use the number of COVID-19 infections, in addition to its significant predictors, to classify KSA governorates using the K-mean cluster method. Findings show that all KSA governorates can be grouped into two clusters. The first cluster includes 31 governorates that can be considered at greater risk of Covid infections as they have higher values in all the significant determinants of Covid infections. The last objective is to compare between traditional statistical methods and artificial intelligence techniques in predicting the future number of COVID-19 infections, with the aim of determining the method that provides the highest accuracy. Results also show that multilayer perceptron neural network outperforms others in forecasting the future number of COVID-19. Finally, the future number of infections for each cluster is predicted using multilayer perceptron neural network method.

Suggested Citation

  • Abdallah S. A. Yaseen, 2022. "Impact of social determinants on COVID-19 infections: a comprehensive study from Saudi Arabia governorates," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-9, December.
    • Handle: RePEc:pal:palcom:v:9:y:2022:i:1:d:10.1057_s41599-022-01208-2
    DOI: 10.1057/s41599-022-01208-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/s41599-022-01208-2
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1057/s41599-022-01208-2?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Tadeusz Kufel, 2020. "ARIMA-based forecasting of the dynamics of confirmed Covid-19 cases for selected European countries," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 15(2), pages 181-204, June.
    2. Gaetano Perone, 2020. "An ARIMA model to forecast the spread and the final size of COVID-2019 epidemic in Italy," Health, Econometrics and Data Group (HEDG) Working Papers 20/07, HEDG, c/o Department of Economics, University of York.
    3. Pena, Edsel A. & Slate, Elizabeth H., 2006. "Global Validation of Linear Model Assumptions," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 341-354, March.
    4. Abolfazl Mollalo & Kiara M. Rivera & Behzad Vahedi, 2020. "Artificial Neural Network Modeling of Novel Coronavirus (COVID-19) Incidence Rates across the Continental United States," IJERPH, MDPI, vol. 17(12), pages 1-13, June.
    5. Jena, Pradyot Ranjan & Majhi, Ritanjali & Kalli, Rajesh & Managi, Shunsuke & Majhi, Babita, 2021. "Impact of COVID-19 on GDP of major economies: Application of the artificial neural network forecaster," Economic Analysis and Policy, Elsevier, vol. 69(C), pages 324-339.
    6. de Oliveira, Erick Meira & Cyrino Oliveira, Fernando Luiz, 2018. "Forecasting mid-long term electric energy consumption through bagging ARIMA and exponential smoothing methods," Energy, Elsevier, vol. 144(C), pages 776-788.
    Full references (including those not matched with items on IDEAS)

    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. Gaetano Perone, 2022. "Using the SARIMA Model to Forecast the Fourth Global Wave of Cumulative Deaths from COVID-19: Evidence from 12 Hard-Hit Big Countries," Econometrics, MDPI, vol. 10(2), pages 1-23, April.
    2. Emerson Abraham Jackson, 2021. "Forecasting COVID-19 Daily Contraction in Sierra Leone with Implications for Policy Formulation," Journal of Economic Policy Researches, Istanbul University, Faculty of Economics, vol. 8(1), pages 29-43, January.
    3. Pin Li & Jinsuo Zhang, 2019. "Is China’s Energy Supply Sustainable? New Research Model Based on the Exponential Smoothing and GM(1,1) Methods," Energies, MDPI, vol. 12(2), pages 1-30, January.
    4. Orietta Nicolis & Jean Paul Maidana & Fabian Contreras & Danilo Leal, 2024. "Analyzing the Impact of COVID-19 on Economic Sustainability: A Clustering Approach," Sustainability, MDPI, vol. 16(4), pages 1-30, February.
    5. Zhang, Meng & Guo, Huan & Sun, Ming & Liu, Sifeng & Forrest, Jeffrey, 2022. "A novel flexible grey multivariable model and its application in forecasting energy consumption in China," Energy, Elsevier, vol. 239(PE).
    6. Wang, Delu & Gan, Jun & Mao, Jinqi & Chen, Fan & Yu, Lan, 2023. "Forecasting power demand in China with a CNN-LSTM model including multimodal information," Energy, Elsevier, vol. 263(PE).
    7. Lu, Ran & Xu, Wen & Zeng, Hongjun & Zhou, Xiangjing, 2023. "Volatility connectedness among the Indian equity and major commodity markets under the COVID-19 scenario," Economic Analysis and Policy, Elsevier, vol. 78(C), pages 1465-1481.
    8. Beata Bieszk-Stolorz & Iwona Markowicz, 2022. "The Impact of the COVID-19 Pandemic on the Situation of the Unemployed in Poland. A Study Using Survival Analysis Methods," Sustainability, MDPI, vol. 14(19), pages 1-19, October.
    9. Oreshkin, Boris N. & Dudek, Grzegorz & Pełka, Paweł & Turkina, Ekaterina, 2021. "N-BEATS neural network for mid-term electricity load forecasting," Applied Energy, Elsevier, vol. 293(C).
    10. Meira, Erick & Cyrino Oliveira, Fernando Luiz & de Menezes, Lilian M., 2022. "Forecasting natural gas consumption using Bagging and modified regularization techniques," Energy Economics, Elsevier, vol. 106(C).
    11. Munazza Fatima & Kara J. O’Keefe & Wenjia Wei & Sana Arshad & Oliver Gruebner, 2021. "Geospatial Analysis of COVID-19: A Scoping Review," IJERPH, MDPI, vol. 18(5), pages 1-14, February.
    12. Yugang He & Ziqian Zhang, 2022. "Energy and Economic Effects of the COVID-19 Pandemic: Evidence from OECD Countries," Sustainability, MDPI, vol. 14(19), pages 1-13, September.
    13. Yothin Jinjarak & Ilan Noy & Quy Ta, 2022. "Pandemics and Economic Growth: Evidence from the 1968 H3N2 Influenza," Economics of Disasters and Climate Change, Springer, vol. 6(1), pages 73-93, March.
    14. Herziger, Atar & Claborn, Kelly A. & Brooks, Jeremy S., 2020. "Is There Hope for the Double Dividend? How Social Context Can Shape Synergies and Tradeoffs between Sustainable Consumption and Well-Being," Ecological Economics, Elsevier, vol. 176(C).
    15. Gomes Adriana Maria Silva & de Sousa Paulo Sérgio Amaral & Moreira Maria do Rosário Alves, 2023. "Having a better environmental performance translates into a better financial performance: A study of the European food industry," Environmental & Socio-economic Studies, Sciendo, vol. 11(3), pages 1-13, September.
    16. Chabouni, Naima & Belarbi, Yacine & Benhassine, Wassim, 2020. "Electricity load dynamics, temperature and seasonality Nexus in Algeria," Energy, Elsevier, vol. 200(C).
    17. Marina Yu. Malkina, 2021. "How the 2020 pandemic affected tax revenues in Russian regions?," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 16(2), pages 239-260, June.
    18. Andriy Stavytskyy & Ganna Kharlamova & Olena Komendant & Jarosław Andrzejczak & Joanna Nakonieczny, 2021. "Methodology for Calculating the Energy Security Index of the State: Taking into Account Modern Megatrends," Energies, MDPI, vol. 14(12), pages 1-19, June.
    19. Xin Chen & Liangwen Xu & Zhigeng Pan, 2022. "Design and Preliminary Realization of a Screening and Early Warning Health Management System for Populations at High Risk for Depression," IJERPH, MDPI, vol. 19(6), pages 1-12, March.
    20. Wang, Qiang & Jiang, Feng, 2019. "Integrating linear and nonlinear forecasting techniques based on grey theory and artificial intelligence to forecast shale gas monthly production in Pennsylvania and Texas of the United States," Energy, Elsevier, vol. 178(C), pages 781-803.

    More about this item

    Statistics

    Access and download statistics

    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:pal:palcom:v:9:y:2022:i:1:d:10.1057_s41599-022-01208-2. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: https://www.nature.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.