Deep learning based an efficient hybrid prediction model for Covid-19 cross-country spread among E7 and G7 countries

Anıl Utku

doi:10.31181/dmame060129022023u

Authors

Anıl Utku Department of Computer Engineering, Munzur University, Tunceli, Turkey https://orcid.org/0000-0002-7240-8713

DOI:

https://doi.org/10.31181/dmame060129022023u

Keywords:

COVID-19, machine learning, deep learning, cross-country spread, CNN, RNN

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.

Downloads

Download data is not yet available.

References

Abbasimehr, H., & Paki, R. (2021). Prediction of COVID-19 confirmed cases combining deep learning methods and Bayesian optimization. Chaos, Solitons & Fractals, 142. doi:https://doi.org/10.1016/j.chaos.2020.110511

Ahmad, M., Sadiq, S., Alluhaidan, A., & Umer, M. (2022). Industry 4.0 technologies and their applications in fighting COVID-19 pandemic using deep learning techniques. Computers in Biology and Medicine, 145. doi:https://doi.org/10.1016/j.dsx.2020.04.032

Alakuş, T., & Türkoğlu, İ. (2020). Comparison of deep learning approaches to predict COVID-19 infection. Chaos, Solitons & Fractals, 140. doi:https://doi.org/10.1016/j.chaos.2020.110120

Alassafi, M., Jarrah, M., & Alotaibi, R. (2022). Time series predicting of COVID-19 based on deep learning. Neurocomputing, 468, 335-344. doi:https://doi.org/10.1016/j.neucom.2021.10.035

Ali, M., Prasad, R., Xiang, Y., & Deo, R. (2020). Near real-time significant wave height forecasting with hybridized multiple linear regression algorithms. Renewable and Sustainable Energy Reviews, 132. doi:https://doi.org/10.1016/j.rser.2020.110003

Ayoobi, N., Sharifrazi, D., Alizadehsani, R., Shoeibi, A., Gorriz, J., Moosaei, H., & Mosavi, A. (2021). Time series forecasting of new cases and new deaths rate for COVID-19 using deep learning methods. Results in Physics, 27. doi:https://doi.org/10.1016/j.rinp.2021.104495

Ballabio, C., & Sterlacchini, S. (2012). Support vector machines for landslide susceptibility mapping: The Staffora River Basin case study, Italy. Mathematical geosciences, 44(1), 47-70. doi:https://doi.org/10.1007/s11004-011-9379-9

Basha, S., Dubey, S., Pulabaigari, V., & Mukherjee, S. (2020). Impact of fully connected layers on performance of convolutional neural networks for image classification. Neurocomputing, 378, 112-119. doi:https://doi.org/10.1016/j.neucom.2019.10.008

Bikku, T. (2020). Multi-layered deep learning perceptron approach for health risk prediction. Journal of Big Data, 7(1), 1-14. doi:https://doi.org/10.1186/s40537-020-00316-7

Chamasemani, F., & Singh , Y. (2011). Multi-class support vector machine (SVM) classifiers--an application in hypothyroid detection and classification. 2011 sixth international conference on bio-inspired computing: theories and applications, (s. 351-356). doi:https://doi.org/10.1109/BIC-TA.2011.51

Che Azemin, M., Hassan, R., & Mohd Tamrin, M. (2020). COVID-19 deep learning prediction model using publicly available radiologist-adjudicated chest X-ray images as training data: preliminary findings. International Journal of Biomedical Imaging. doi:https://doi.org/10.1155/2020/8828855

Chen, X., Liu, X., Gales, M., & Woodland, P. (2015). Recurrent neural network language model training with noise contrastive estimation for speech recognition. 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). doi:https://doi.org/10.1109/ICASSP.2015.7179005

Data Smith. (2022, Novamber 06). Experiments in data visualization: http://www.datasmith.org/2018/06/02/a-bold-chord-diagram-generator/ adresinden alındı

Devaraj, J., Elavarasan, R., Pugazhendhi, R., Shafiullah, G., Ganesan, S., Jeysree, A., & Hossain, E. (2021). Forecasting of COVID-19 cases using deep learning models: Is it reliable and practically significant? Results in Physics, 21. doi:https://doi.org/10.1016/j.rinp.2021.103817

El-Khaiary, M. (2008). Least-squares regression of adsorption equilibrium data: comparing the options. Journal of Hazardous Materials, 158(1), 73-87. doi:https://doi.org/10.1016/j.jhazmat.2008.01.052

Elsheikh, A., Saba, A., Abd Elaziz, M., Lu, S., Shanmugan, S., Muthuramalingam, T., & Shehabeldeen, T. (2021). Deep learning-based forecasting model for COVID-19 outbreak in Saudi Arabia. Process Safety and Environmental Protection, 149, 223-233. doi:https://doi.org/10.1016/j.psep.2020.10.048

Fang, L., Karakiulakis, G., & Roth, M. (2020). Are patients with hypertension and diabetes mellitus at increased risk for COVID-19 infection? The lancet respiratory medicine, 8(4), e21. doi:https://doi.org/10.1016/S2213-2600(20)30116-8

Gao, Y., & Glowacka, D. (2016). Deep gate recurrent neural network. In Asian conference on machine learning, 63, s. 350-365.

Giambartolomei, C., Zhenli, L., Zhang, W., Hauberg, M., Shi, H., Boocock, J., & Roussos, P. (2018). A Bayesian framework for multiple trait colocalization from summary association statistics. Bioinformatics, 2538-2545. doi:https://doi.org/10.1093/bioinformatics/bty147

Hari, S., Sullivan, M., Tsai, T., & Keckler, S. (2021). Making convolutions resilient via algorithm-based error detection techniques. IEEE Transactions on Dependable and Secure Computing, 19(4), 2546-2558. doi:https://doi.org/10.1109/TDSC.2021.3063083

Henrikson, N., Opel, D., Grothaus, L., Nelson, J., Scrol, A., Dunn, J., & Grossman, D. (2015). Physician communication training and parental vaccine hesitancy: a randomized trial. Pediatrics, 136(1), 70-79. doi:https://doi.org/10.1542/peds.2014-3199

Huang, J., Li, J., Yu, D., Deng, L., & Gong, Y. (2013). Cross-language knowledge transfer using multilingual deep neural network with shared hidden layers. 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, (s. 7304-7308). doi:https://doi.org/10.1109/ICASSP.2013.6639081

Jarag, P., Kengar, M., Jadhav, R., Shinde, A., Koli, S., & Honmane, P. (2020). On overview-Corona virus and Hanta virus Disease. Asian Journal of Research in Pharmaceutical Science, 10(3), 178-182.

Jernigan, D. (2020). Update: public health response to the coronavirus disease 2019 outbreak—United States, February 24, 2020. Morbidity and mortality weekly report.

Jozefowicz, R., Zaremba, W., & Sutskever, I. (2015). An empirical exploration of recurrent network architectures. International conference on machine learning, (s. 2342-2350). doi:https://doi.org/10.5555/3045118.3045367

Ketu, S., & Mishra, P. (2022). India perspective: CNN-LSTM hybrid deep learning model-based COVID-19 prediction and current status of medical resource availability. Soft Computing, 26(2), 645-664. doi:https://doi.org/10.1007/s00500-021-06490-x

Kırbaş, İ., Sözen, A., Tuncer, A., & Kazancıoğlu, F. (2020). Comparative analysis and forecasting of COVID-19 cases in various European countries with ARIMA, NARNN and LSTM approaches. Chaos, Solitons & Fractals, 138. doi:https://doi.org/10.1016/j.chaos.2020.110015

Klompas, M., Milton, D., Rhee, C., Baker, M., & Leekha, S. (2021). Current insights into respiratory virus transmission and potential implications for infection control programs: a narrative review. Annals of Internal Medicine, 174(12), 1710-1718. doi:https://doi.org/10.7326/M21-2780

Lee, B., Yang, C., & Huang, B. (2012). Oil price movements and stock markets revisited: A case of sector stock price indexes in the G-7 countries. Energy Economics, 34(5), 1284-1300. doi:https://doi.org/10.1016/j.eneco.2012.06.004

Mahmoudi, J., Arjomand, M., Rezaei, M., & Mohammadi, M. (2016). Predicting the earthquake magnitude using the multilayer perceptron neural network with two hidden layers. Civil engineering journal, 2(1), 1-12. doi:https://doi.org/10.28991/cej-2016-00000008

Mamun, M., & Ullah, I. (2020). COVID-19 suicides in Pakistan, dying off not COVID-19 fear but poverty?–The forthcoming economic challenges for a developing country. Brain, behavior, and immunity, 87, 163-166. doi:https://doi.org/10.1016/j.bbi.2020.05.028

Marzouk, M., Elshaboury, N., Abdel-Latif, A., & Azab, S. (2021). Deep learning model for forecasting COVID-19 outbreak in Egypt. Process Safety and Environmental Protection, 153, 363-375. doi:https://doi.org/10.1016/j.psep.2021.07.034

Masum, A., Khushbu, S., Keya, M., Abujar, S., & Hossain, S. (2020). COVID-19 in Bangladesh: a deeper outlook into the forecast with prediction of upcoming per day cases using time series. Procedia Computer Science, 178, 291-300. doi:https://doi.org/10.1016/j.procs.2020.11.031

Minotti, C., Tirelli, F., Barbieri, E., Giaquinto, C., & Donà, D. (2020). How is immunosuppressive status affecting children and adults in SARS-CoV-2 infection? A systematic review. Journal of Infection, 81(1), 61-66. doi:https://doi.org/10.1016/j.jinf.2020.04.026

Mirzaei, S., Kang, J., & Chu, K. (2022). A comparative study on long short-term memory and gated recurrent unit neural networks in fault diagnosis for chemical processes using visualization. Journal of the Taiwan Institute of Chemical Engineers, 130. doi:https://doi.org/10.1016/j.jtice.2021.08.016

Mize, T. (2019). Best practices for estimating, interpreting, and presenting nonlinear interaction effects. Sociological Science, 6, 81-117. doi:https://doi.org/10.15195/v6.a4

Naghibi, S., Pourghasemi, H., & Dixon, B. (2016). GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran. Environmental monitoring and assessment, 188(1), 1-27. doi:https://doi.org/10.1007/s10661-015-5049-6

Ramadevi, R., Sheela Rani, B., & Prakash, V. (2012). Role of hidden neurons in an elman recurrent neural network in classification of cavitation signals. International Journal of Computer Applications, 37(7), 9-13. doi:https://doi.org/10.5120/4618-6626

Salehi, H., & Burgueño, R. (2018). Emerging artificial intelligence methods in structural engineering. Engineering structures, 171, 170-189. doi:https://doi.org/10.1016/j.engstruct.2018.05.084

Samra, M., Abed, B., Zaqout, H., & Abu-Naser, S. (2020). ANN Model for Predicting Protein Localization Sites in Cells. International Journal of Academic and Applied Research (IJAAR), 4(9), 43-50.

Satu, M., Howlader, K., Mahmud, M., Kaiser, M., Shariful Islam, S., Quinn, J., & Moni, M. (2021). Short-term prediction of COVID-19 cases using machine learning models. Applied Sciences, 11(9). doi:https://doi.org/10.3390/app11094266

Schijven, J., Vermeulen, L., Swart, A., Meijer, A., Duizer, E., & de Roda Husman, A. (2021). Quantitative microbial risk assessment for airborne transmission of SARS-CoV-2 via breathing, speaking, singing, coughing, and sneezing. Environmental health perspectives, 129(4). doi:https://doi.org/10.1289/EHP7886

Shahid, F., Zameer, A., & Muneeb, M. (2020). Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM. Chaos, Solitons & Fractals, 140. doi:https://doi.org/10.1016/j.chaos.2020.110212

Shastri, S., Singh, K., Kumar, S., Kour, P., & Mansotra, V. (2020). Time series forecasting of Covid-19 using deep learning models: India-USA comparative case study. Chaos, Solitons & Fractals, 140. doi:https://doi.org/10.1016/j.chaos.2020.110227

Speiser, J., Miller, M., Tooze, J., & Ip, E. (2019). A comparison of random forest variable selection methods for classification prediction modeling. Expert systems with applications, 134, 93-101. doi:https://doi.org/10.1016/j.eswa.2019.05.028

Sun, H., Burton, H., & Huang, H. (2021). Machine learning applications for building structural design and performance assessment: State-of-the-art review. Journal of Building Engineering, 33. doi:https://doi.org/10.1016/j.jobe.2020.101816

Tiwari, P., Pant, B., Elarabawy, M., Abd-Elnaby, M., Mohd, N., Dhiman, G., & Sharma, S. (2022). Cnn based multiclass brain tumor detection using medical imaging. Computational Intelligence and Neuroscience. doi:https://doi.org/10.1155/2022/1830010

Toğaçar, M., Ergen, B., & Cömert, Z. (2020). COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches. Computers in biology and medicine, 121. doi:https://doi.org/10.1016/j.compbiomed.2020.103805

Tong, T., Ortiz, J., Xu, C., & Li, F. (2020). Economic growth, energy consumption, and carbon dioxide emissions in the E7 countries: a bootstrap ARDL bound test. Energy, Sustainability and Society, 10(1), 1-17. doi:https://doi. org/10.1186/s13705-020-00253-6

Verma, H., Mandal, S., & Gupta, A. (2022). Temporal deep learning architecture for prediction of COVID-19 cases in India. Expert Systems with Applications, 195. doi:https://doi.org/10.1016/j.eswa.2022.116611

Wang, S., Lv, Y., Sui, Y., Liu, S., Wang, S., & Zhang, Y. (2018). Alcoholism detection by data augmentation and convolutional neural network with stochastic pooling. Journal of medical systems, 42(1), 1-11. doi:https://doi.org/10.1007/s10916-017-0845-x

Xu, X., & Zhu, D. (2021). New method for solving Ivanov regularization-based support vector machine learning. Computers & Operations Research, 136. doi:https://doi.org/10.1016/j.cor.2021.105504

Zeroual, A., Harrou, F., Dairi, A., & Sun, Y. (2020). Deep learning methods for forecasting COVID-19 time-Series data: A Comparative study. Chaos, Solitons & Fractals, 140. doi:https://doi.org/10.1016/j.chaos.2020.110121