DEEP LEARNING BASED AN EFFICIENT HYBRID PREDICTION MODEL FOR COVID-19 CROSS-COUNTRY SPREAD AMONG E7 AND G7 COUNTRIES
Decision Making: Applications in Management and Engineering
; 6(1):502-534, 2023.
Artículo
en Inglés
| Scopus | ID: covidwho-20244096
ABSTRACT
The COVID-19 pandemic has caused the death of many people around the world and has also caused economic problems for all countries in the world. In the literature, there are many studies to analyze and predict the spread of COVID-19 in cities and countries. However, there is no study to predict and analyze the cross-country spread in the world. In this study, a deep learning based hybrid model was developed to predict and analysis of COVID-19 cross-country spread and a case study was carried out for Emerging Seven (E7) and Group of Seven (G7) countries. It is aimed to reduce the workload of healthcare professionals and to make health plans by predicting the daily number of COVID-19 cases and deaths. Developed model was tested extensively using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and R Squared (R2). The experimental results showed that the developed model was more successful to predict and analysis of COVID-19 cross-country spread in E7 and G7 countries than Linear Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM). The developed model has R2 value close to 0.9 in predicting the number of daily cases and deaths in the majority of E7 and G7 countries. © 2023 by the authors.
CNN; COVID-19; cross-country spread; deep learning; machine learning; RNN; Convolutional neural networks; Errors; Forecasting; Forestry; Learning systems; Long short-term memory; Mean square error; Multilayer neural networks; Random forests; Support vector machines; Case-studies; Convolutional neural network; Developed model; Economic problems; Health care professionals; Hybrid model; Hybrid prediction models; Machine-learning
Texto completo:
Disponible
Colección:
Bases de datos de organismos internacionales
Base de datos:
Scopus
Tipo de estudio:
Estudio pronóstico
/
Ensayo controlado aleatorizado
Idioma:
Inglés
Revista:
Decision Making: Applications in Management and Engineering
Año:
2023
Tipo del documento:
Artículo
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