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Journals / Hittite Journal of Science and Engineering / 2021 / Cilt: 8 - Sayı: 2

Hybrid Machine Learning Model Coupled with School Closure For Forecasting COVID-19 Cases in the Most Affected Countries

Selim Buyrukoğlu
Yıldıran Yılmaz
Pages
123–131
Publication Date
2022-10-18
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Besides, the proposed prediction models can assist countries in terms of taking important precautions to control the spread of Covid-19 in the world.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Coronavirus disease (Covid-19) caused millions of confirmed cases and thousands of deaths worldwide since first appeared in China.
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Key Findings & Data:Therefore, in this research, a new customized hybrid model consisting of Back Propagation-Based Artificial Neural Network (BP-ANN), Correlated Additive Model (CAM) and Auto-Regressive Integrated Moving Average (ARIMA) models were developed to forecast of Covid-19 prevalence in Brazil, US, Russia and India.
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Conclusion & Impact:Besides, the proposed prediction models can assist countries in terms of taking important precautions to control the spread of Covid-19 in the world.
🔬Design:Modeling & Simulation
📍Scope:2020 Verileri
⚙️Variables:Covid-19 · Neural Network · ARIMA

Abstract

Coronavirus disease (Covid-19) caused millions of confirmed cases and thousands of deaths worldwide since first appeared in China. Forecasting methods are essential to take precautions early and control the spread of this rapidly expanding pandemic. Therefore, in this research, a new customized hybrid model consisting of Back Propagation-Based Artificial Neural Network (BP-ANN), Correlated Additive Model (CAM) and Auto-Regressive Integrated Moving Average (ARIMA) models were developed to forecast of Covid-19 prevalence in Brazil, US, Russia and India. Covid-19 dataset is obtained from World Health Organization website from 22 January, 2020 to 6 January, 2021. Various parameters were tested to select the best ARIMA models for these countries based on the lowest MAPE values (5.21, 11.42, 1.45, 2.72) for Brazil, US, Russia and India, respectively. On the other hand, the proposed BP-ANN model itself provided less satisfactory MAPE values. Finally, the developed new customized hybrid model was achieved to obtain the best MAPE results (4.69, 6.4, 0.63, 2.25) for forecasting of Covid-19 prevalence in Brazil, US, Russia and India, respectively. Those results emphasize the validity of our hybrid model. Besides, the proposed prediction models can assist countries in terms of taking important precautions to control the spread of Covid-19 in the world.

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Cite This Article

Buyrukoğlu, S., Yılmaz, Y. (2022). Hybrid Machine Learning Model Coupled with School Closure For Forecasting COVID-19 Cases in the Most Affected Countries. Hittite Journal of Science and Engineering, Cilt: 8 - Sayı: 2, 123–131. https://ajindex.com/articles/537897

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