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Multi-Regional Modeling of Cumulative COVID-19 Cases Integrated with Environmental Forest Knowledge Estimation: A Deep Learning Ensemble Approach

Author

Listed:
  • Abdelgader Alamrouni

    (Department of Environmental Education and Management, Faculty of Education, Near East University, Nicosia 700006, Cyprus)

  • Fidan Aslanova

    (Department of Environmental Engineering, Faculty of Civil and Environmental Engineering, Near East University, Nicosia 700006, Cyprus)

  • Sagiru Mati

    (Department of Economics, Yusuf Maitama Sule University, Kano 700282, Nigeria)

  • Hamza Sabo Maccido

    (Department of Electrical and Computer Engineering, Faculty of Engineering, Baze University, Abuja 900288, Nigeria)

  • Afaf. A. Jibril

    (Faculty of Clinical Sciences, Bayero University, Kano 700006, Nigeria)

  • A. G. Usman

    (Department of Analytical Chemistry, Faculty of Pharmacy, Near East University, TRNC, Mersin 99138, Turkey)

  • S. I. Abba

    (Interdisciplinary Research Center for Membrane and Water Security, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
    Department of Civil Engineering, Faculty of Engineering, Baze University, Abuja 900288, Nigeria)

Abstract

Reliable modeling of novel commutative cases of COVID-19 (CCC) is essential for determining hospitalization needs and providing the benchmark for health-related policies. The current study proposes multi-regional modeling of CCC cases for the first scenario using autoregressive integrated moving average (ARIMA) based on automatic routines (AUTOARIMA), ARIMA with maximum likelihood (ARIMAML), and ARIMA with generalized least squares method (ARIMAGLS) and ensembled (ARIMAML-ARIMAGLS). Subsequently, different deep learning (DL) models viz: long short-term memory (LSTM), random forest (RF), and ensemble learning (EML) were applied to the second scenario to predict the effect of forest knowledge (FK) during the COVID-19 pandemic. For this purpose, augmented Dickey–Fuller (ADF) and Phillips–Perron (PP) unit root tests, autocorrelation function (ACF), partial autocorrelation function (PACF), Schwarz information criterion (SIC), and residual diagnostics were considered in determining the best ARIMA model for cumulative COVID-19 cases (CCC) across multi-region countries. Seven different performance criteria were used to evaluate the accuracy of the models. The obtained results justified both types of ARIMA model, with ARIMAGLS and ensemble ARIMA demonstrating superiority to the other models. Among the DL models analyzed, LSTM-M1 emerged as the best and most reliable estimation model, with both RF and LSTM attaining more than 80% prediction accuracy. While the EML of the DL proved merit with 96% accuracy. The outcomes of the two scenarios indicate the superiority of ARIMA time series and DL models in further decision making for FK.

Suggested Citation

  • Abdelgader Alamrouni & Fidan Aslanova & Sagiru Mati & Hamza Sabo Maccido & Afaf. A. Jibril & A. G. Usman & S. I. Abba, 2022. "Multi-Regional Modeling of Cumulative COVID-19 Cases Integrated with Environmental Forest Knowledge Estimation: A Deep Learning Ensemble Approach," IJERPH, MDPI, vol. 19(2), pages 1-22, January.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:2:p:738-:d:721409
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    References listed on IDEAS

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    1. J. -P. Vandamme & N. Meskens & J. -F. Superby, 2007. "Predicting Academic Performance by Data Mining Methods," Education Economics, Taylor & Francis Journals, vol. 15(4), pages 405-419.
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