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

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 ave...

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Veröffentlicht in:International journal of environmental research and public health 2022-01, Vol.19 (2), p.738
Hauptverfasser: Alamrouni, Abdelgader, Aslanova, Fidan, Mati, Sagiru, Maccido, Hamza Sabo, Jibril, Afaf A, Usman, A G, Abba, S I
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container_title International journal of environmental research and public health
container_volume 19
creator Alamrouni, Abdelgader
Aslanova, Fidan
Mati, Sagiru
Maccido, Hamza Sabo
Jibril, Afaf A
Usman, A G
Abba, S I
description 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.
doi_str_mv 10.3390/ijerph19020738
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subjects Autocorrelation functions
Autoregressive models
Coronaviruses
COVID-19
Decision making
Deep Learning
Disease transmission
Forecasting
Humans
Learning
Least squares method
Long short-term memory
Machine learning
Maximum likelihood method
Medical research
Model accuracy
Models, Statistical
Neural networks
Pandemics
SARS-CoV-2
Seafood
Severe acute respiratory syndrome coronavirus 2
Time series
title Multi-Regional Modeling of Cumulative COVID-19 Cases Integrated with Environmental Forest Knowledge Estimation: A Deep Learning Ensemble Approach
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