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Year 2021, , 123 - 131, 30.06.2021

Yıldıran Yılmaz , Selim Buyrukoğlu

https://doi.org/10.17350/HJSE19030000222
Cited By: 6

Abstract

References

  • [1] H. Lu, C. W. Stratton, and Y. W. Tang, “Outbreak of pneumonia of unknown etiology in Wuhan, China: The mystery and the miracle,” J. Med. Virol., vol. 92, no. 4, pp. 401–402, 2020, doi: 10.1002/jmv.25678.
  • [1] H. Lu, C. W. Stratton, and Y. W. Tang, “Outbreak of pneumonia of unknown etiology in Wuhan, China: The mystery and the miracle,” J. Med. Virol., vol. 92, no. 4, pp. 401–402, 2020, doi: 10.1002/jmv.25678.
  • [2] Worldometers.info, “COVID-19 CORONAVIRUS PANDEMIC,” 2020. https://www.worldometers.info/coronavirus/?utm_campaign=CSauthorbio? (accessed Aug. 10, 2020).
  • [2] Worldometers.info, “COVID-19 CORONAVIRUS PANDEMIC,” 2020. https://www.worldometers.info/coronavirus/?utm_campaign=CSauthorbio? (accessed Aug. 10, 2020).
  • [3] M. Rypdal and G. Sugihara, “Inter-outbreak stability reflects the size of the susceptible pool and forecasts magnitudes of seasonal epidemics,” Nat. Commun., vol. 10, no. 1, 2019, doi: 10.1038/s41467-019-10099-y.
  • [3] M. Rypdal and G. Sugihara, “Inter-outbreak stability reflects the size of the susceptible pool and forecasts magnitudes of seasonal epidemics,” Nat. Commun., vol. 10, no. 1, 2019, doi: 10.1038/s41467-019-10099-y.
  • [4] J. K. Davis et al., “A genetic algorithm for identifying spatially-varying environmental drivers in a malaria time series model,” Environ. Model. Softw., vol. 119, no. February, pp. 275–284, 2019, doi: 10.1016/j.envsoft.2019.06.010.
  • [4] J. K. Davis et al., “A genetic algorithm for identifying spatially-varying environmental drivers in a malaria time series model,” Environ. Model. Softw., vol. 119, no. February, pp. 275–284, 2019, doi: 10.1016/j.envsoft.2019.06.010.
  • [5] J. M. Scavuzzo et al., “Modeling Dengue vector population using remotely sensed data and machine learning,” Acta Trop., vol. 185, no. October 2017, pp. 167–175, 2018, doi: 10.1016/j.actatropica.2018.05.003.
  • [5] J. M. Scavuzzo et al., “Modeling Dengue vector population using remotely sensed data and machine learning,” Acta Trop., vol. 185, no. October 2017, pp. 167–175, 2018, doi: 10.1016/j.actatropica.2018.05.003.
  • [6] R. Vaishya, M. Javaid, I. H. Khan, and A. Haleem, “Artificial Intelligence (AI) applications for COVID-19 pandemic,” Diabetes Metab. Syndr. Clin. Res. Rev., vol. 14, no. 4, pp. 337–339, 2020, doi: 10.1016/j.dsx.2020.04.012.
  • [6] R. Vaishya, M. Javaid, I. H. Khan, and A. Haleem, “Artificial Intelligence (AI) applications for COVID-19 pandemic,” Diabetes Metab. Syndr. Clin. Res. Rev., vol. 14, no. 4, pp. 337–339, 2020, doi: 10.1016/j.dsx.2020.04.012.
  • [7] S. P. Kaur and V. Gupta, “COVID-19 Vaccine: A comprehensive status report,” Virus Res., vol. 288, no. August, p. 198114, 2020, doi: 10.1016/j.virusres.2020.198114.
  • [7] S. P. Kaur and V. Gupta, “COVID-19 Vaccine: A comprehensive status report,” Virus Res., vol. 288, no. August, p. 198114, 2020, doi: 10.1016/j.virusres.2020.198114.
  • [8] D. Kang, H. Choi, J. H. Kim, and J. Choi, “Spatial epidemic dynamics of the COVID-19 outbreak in China,” Int. J. Infect. Dis., vol. 94, no. January, pp. 96–102, 2020, doi: 10.1016/j.ijid.2020.03.076.
  • [8] D. Kang, H. Choi, J. H. Kim, and J. Choi, “Spatial epidemic dynamics of the COVID-19 outbreak in China,” Int. J. Infect. Dis., vol. 94, no. January, pp. 96–102, 2020, doi: 10.1016/j.ijid.2020.03.076.
  • [9] M. A. A. Al-Qaness, A. A. Ewees, H. Fan, and M. A. El Aziz, “Optimization method for forecasting confirmed cases of COVID-19 in China,” Appl. Sci., vol. 9, no. 3, 2020, doi: 10.3390/JCM9030674.
  • [9] M. A. A. Al-Qaness, A. A. Ewees, H. Fan, and M. A. El Aziz, “Optimization method for forecasting confirmed cases of COVID-19 in China,” Appl. Sci., vol. 9, no. 3, 2020, doi: 10.3390/JCM9030674.
  • [10] A. J. Kucharski et al., “Early dynamics of transmission and control of COVID-19: a mathematical modelling study,” Lancet Infect. Dis., vol. 20, no. 5, pp. 553–558, 2020, doi: 10.1016/S1473-3099(20)30144-4.
  • [10] A. J. Kucharski et al., “Early dynamics of transmission and control of COVID-19: a mathematical modelling study,” Lancet Infect. Dis., vol. 20, no. 5, pp. 553–558, 2020, doi: 10.1016/S1473-3099(20)30144-4.
  • [11] P. Lauret, F. Heymes, S. Forestier, L. Aprin, A. Pey, and M. Perrin, “Forecasting powder dispersion in a complex environment using Artificial Neural Networks,” Process Saf. Environ. Prot., vol. 110, pp. 71–76, 2017, doi: 10.1016/j.psep.2017.02.003.
  • [11] P. Lauret, F. Heymes, S. Forestier, L. Aprin, A. Pey, and M. Perrin, “Forecasting powder dispersion in a complex environment using Artificial Neural Networks,” Process Saf. Environ. Prot., vol. 110, pp. 71–76, 2017, doi: 10.1016/j.psep.2017.02.003.
  • [12] A. Narin, C. Kaya, and Z. Pamuk, “Department of Biomedical Engineering, Zonguldak Bulent Ecevit University, 67100, Zonguldak, Turkey.,” arXiv Prepr. arXiv2003.10849., 2020.
  • [12] A. Narin, C. Kaya, and Z. Pamuk, “Department of Biomedical Engineering, Zonguldak Bulent Ecevit University, 67100, Zonguldak, Turkey.,” arXiv Prepr. arXiv2003.10849., 2020.
  • [13] H. A. Babikir et al., “Noise prediction of axial piston pump based on different valve materials using a modified artificial neural network model,” Alexandria Eng. J., vol. 58, no. 3, pp. 1077–1087, 2019, doi: 10.1016/j.aej.2019.09.010.
  • [13] H. A. Babikir et al., “Noise prediction of axial piston pump based on different valve materials using a modified artificial neural network model,” Alexandria Eng. J., vol. 58, no. 3, pp. 1077–1087, 2019, doi: 10.1016/j.aej.2019.09.010.
  • [14] M. A. Elaziz, A. H. Elsheikh, and S. W. Sharshir, “Improved prediction of oscillatory heat transfer coefficient for a thermoacoustic heat exchanger using modified adaptive neuro-fuzzy inference system,” Int. J. Refrig., vol. 102, pp. 47–54, 2019, doi: 10.1016/j.ijrefrig.2019.03.009.
  • [14] M. A. Elaziz, A. H. Elsheikh, and S. W. Sharshir, “Improved prediction of oscillatory heat transfer coefficient for a thermoacoustic heat exchanger using modified adaptive neuro-fuzzy inference system,” Int. J. Refrig., vol. 102, pp. 47–54, 2019, doi: 10.1016/j.ijrefrig.2019.03.009.
  • [15] A. H. Elsheikh, S. W. Sharshir, M. Abd Elaziz, A. E. Kabeel, W. Guilan, and Z. Haiou, “Modeling of solar energy systems using artificial neural network: A comprehensive review,” Sol. Energy, vol. 180, no. January, pp. 622–639, 2019, doi: 10.1016/j.solener.2019.01.037.
  • [15] A. H. Elsheikh, S. W. Sharshir, M. Abd Elaziz, A. E. Kabeel, W. Guilan, and Z. Haiou, “Modeling of solar energy systems using artificial neural network: A comprehensive review,” Sol. Energy, vol. 180, no. January, pp. 622–639, 2019, doi: 10.1016/j.solener.2019.01.037.
  • [16] T. A. Shehabeldeen et al., “A Novel Method for Predicting Tensile Strength of Friction Stir Welded AA6061 Aluminium Alloy Joints Based on Hybrid Random Vector Functional Link and Henry Gas Solubility Optimization,” IEEE Access, vol. 8, pp. 79896–79907, 2020, doi: 10.1109/ACCESS.2020.2990137.
  • [16] T. A. Shehabeldeen et al., “A Novel Method for Predicting Tensile Strength of Friction Stir Welded AA6061 Aluminium Alloy Joints Based on Hybrid Random Vector Functional Link and Henry Gas Solubility Optimization,” IEEE Access, vol. 8, pp. 79896–79907, 2020, doi: 10.1109/ACCESS.2020.2990137.
  • [17] N. Hasan, “A Methodological Approach for Predicting COVID-19 Epidemic Using EEMD-ANN Hybrid Model,” Internet of Things, vol. 11, p. 100228, 2020, doi: 10.1016/j.iot.2020.100228.
  • [17] N. Hasan, “A Methodological Approach for Predicting COVID-19 Epidemic Using EEMD-ANN Hybrid Model,” Internet of Things, vol. 11, p. 100228, 2020, doi: 10.1016/j.iot.2020.100228.
  • [18] T. Chakraborty and I. Ghosh, “Real-time forecasts and risk assessment of novel coronavirus (COVID-19) cases: A data-driven analysis,” Chaos, Solitons and Fractals, vol. 135, 2020, doi: 10.1016/j.chaos.2020.109850.
  • [18] T. Chakraborty and I. Ghosh, “Real-time forecasts and risk assessment of novel coronavirus (COVID-19) cases: A data-driven analysis,” Chaos, Solitons and Fractals, vol. 135, 2020, doi: 10.1016/j.chaos.2020.109850.
  • [19] G. Pinter, I. Felde, A. Mosavi, P. Ghamisi, and R. Gloaguen, “COVID-19 Pandemic Prediction for Hungary ; a Hybrid Machine Learning Approach,” 2020.
  • [19] G. Pinter, I. Felde, A. Mosavi, P. Ghamisi, and R. Gloaguen, “COVID-19 Pandemic Prediction for Hungary ; a Hybrid Machine Learning Approach,” 2020.
  • [20] S. Singh, K. S. Parmar, J. Kumar, and S. J. S. Makkhan, “Development of new hybrid model of discrete wavelet decomposition and autoregressive integrated moving average (ARIMA) models in application to one month forecast the casualties cases of COVID-19,” Chaos, Solitons and Fractals, vol. 135, pp. 1–8, 2020, doi: 10.1016/j.chaos.2020.109866.
  • [20] S. Singh, K. S. Parmar, J. Kumar, and S. J. S. Makkhan, “Development of new hybrid model of discrete wavelet decomposition and autoregressive integrated moving average (ARIMA) models in application to one month forecast the casualties cases of COVID-19,” Chaos, Solitons and Fractals, vol. 135, pp. 1–8, 2020, doi: 10.1016/j.chaos.2020.109866.
  • [21] A. Altan and S. Karasu, “Recognition of COVID-19 disease from X-ray images by hybrid model consisting of 2D curvelet transform, chaotic salp swarm algorithm and deep learning technique,” Chaos, Solitons and Fractals, vol. 140, 2020, doi: 10.1016/j.chaos.2020.110071.
  • [21] A. Altan and S. Karasu, “Recognition of COVID-19 disease from X-ray images by hybrid model consisting of 2D curvelet transform, chaotic salp swarm algorithm and deep learning technique,” Chaos, Solitons and Fractals, vol. 140, 2020, doi: 10.1016/j.chaos.2020.110071.
  • [22] N. Zheng et al., “Predicting COVID-19 in China Using Hybrid AI Model,” IEEE Trans. Cybern., vol. 50, no. 7, pp. 2891–2904, 2020, doi: 10.1109/TCYB.2020.2990162.
  • [22] N. Zheng et al., “Predicting COVID-19 in China Using Hybrid AI Model,” IEEE Trans. Cybern., vol. 50, no. 7, pp. 2891–2904, 2020, doi: 10.1109/TCYB.2020.2990162.
  • [23] S. Chae, S. Kwon, and D. Lee, “Predicting infectious disease using deep learning and big data,” Int. J. Environ. Res. Public Health, vol. 15, no. 8, 2018, doi: 10.3390/ijerph15081596.
  • [23] S. Chae, S. Kwon, and D. Lee, “Predicting infectious disease using deep learning and big data,” Int. J. Environ. Res. Public Health, vol. 15, no. 8, 2018, doi: 10.3390/ijerph15081596.
  • [24] R. C. esa. Sato, “Disease management with ARIMA model in time series,” Einstein (Sao Paulo)., vol. 11, no. 1, pp. 128–131, 2013, doi: 10.1590/S1679-45082013000100024.
  • [24] R. C. esa. Sato, “Disease management with ARIMA model in time series,” Einstein (Sao Paulo)., vol. 11, no. 1, pp. 128–131, 2013, doi: 10.1590/S1679-45082013000100024.
  • [25] Z. He and H. Tao, “Epidemiology and ARIMA model of positive-rate of influenza viruses among children in Wuhan, China: A nine-year retrospective study,” Int. J. Infect. Dis., vol. 74, pp. 61–70, 2018, doi: 10.1016/j.ijid.2018.07.003.
  • [25] Z. He and H. Tao, “Epidemiology and ARIMA model of positive-rate of influenza viruses among children in Wuhan, China: A nine-year retrospective study,” Int. J. Infect. Dis., vol. 74, pp. 61–70, 2018, doi: 10.1016/j.ijid.2018.07.003.
  • [26] D. Benvenuto, M. Giovanetti, L. Vassallo, S. Angeletti, and M. Ciccozzi, “Application of the ARIMA model on the COVID-2019 epidemic dataset,” Data Br., vol. 29, p. 105340, 2020, doi: 10.1016/j.dib.2020.105340.
  • [26] D. Benvenuto, M. Giovanetti, L. Vassallo, S. Angeletti, and M. Ciccozzi, “Application of the ARIMA model on the COVID-2019 epidemic dataset,” Data Br., vol. 29, p. 105340, 2020, doi: 10.1016/j.dib.2020.105340.
  • [27] X. Li, C. Zhang, B. Zhang, and K. Liu, “A comparative time series analysis and modeling of aerosols in the contiguous United States and China,” Sci. Total Environ., vol. 690, pp. 799–811, 2019, doi: 10.1016/j.scitotenv.2019.07.072.
  • [27] X. Li, C. Zhang, B. Zhang, and K. Liu, “A comparative time series analysis and modeling of aerosols in the contiguous United States and China,” Sci. Total Environ., vol. 690, pp. 799–811, 2019, doi: 10.1016/j.scitotenv.2019.07.072.
  • [28] M. Wang et al., “A novel model for malaria prediction based on ensemble algorithms,” PLoS One, vol. 14, no. 12, pp. 1–15, 2019, doi: 10.1371/journal.pone.0226910.
  • [28] M. Wang et al., “A novel model for malaria prediction based on ensemble algorithms,” PLoS One, vol. 14, no. 12, pp. 1–15, 2019, doi: 10.1371/journal.pone.0226910.
  • [29] J. C. B. Gamboa, “Deep Learning for Time-Series Analysis,” 2017, [Online]. Available: http://arxiv.org/abs/1701.01887.
  • [29] J. C. B. Gamboa, “Deep Learning for Time-Series Analysis,” 2017, [Online]. Available: http://arxiv.org/abs/1701.01887.
  • [30] S. Karasu, A. Altan, Z. Sarac, and R. Hacioglu, “Prediction of Bitcoin prices with machine learning methods using time series data | Zaman serisi verilerini kullanarak makine öǧrenmesi yöntemleri ile bitcoin fiyat tahmini,” 26th IEEE Signal Process. Commun. Appl. Conf. SIU 2018, pp. 1–4, 2018, doi: 10.1109/SIU.2018.8404760.
  • [30] S. Karasu, A. Altan, Z. Sarac, and R. Hacioglu, “Prediction of Bitcoin prices with machine learning methods using time series data | Zaman serisi verilerini kullanarak makine öǧrenmesi yöntemleri ile bitcoin fiyat tahmini,” 26th IEEE Signal Process. Commun. Appl. Conf. SIU 2018, pp. 1–4, 2018, doi: 10.1109/SIU.2018.8404760.
  • [31] S. ÇINAROĞLU, “Sağlik HarcamasiniTahmi̇ni̇ndeMaki̇neÖğrenmesi̇ RegresyoYöntemleri̇ni̇n Karşilaştirilmasi,” Uludağ Univ. J. Fac. Eng., vol. 22, no. 2, pp. 179–200, 2017, doi: 10.17482/uumfd.338805.
  • [31] S. ÇINAROĞLU, “Sağlik HarcamasiniTahmi̇ni̇ndeMaki̇neÖğrenmesi̇ RegresyoYöntemleri̇ni̇n Karşilaştirilmasi,” Uludağ Univ. J. Fac. Eng., vol. 22, no. 2, pp. 179–200, 2017, doi: 10.17482/uumfd.338805.
  • [32] P. M. Swamidass, Encyclopedia of Production and Manufacturing Management, no. June 2000. 2000.
  • [32] P. M. Swamidass, Encyclopedia of Production and Manufacturing Management, no. June 2000. 2000.
  • [33] Y. Wang, C. Xu, Z. Wang, S. Zhang, Y. Zhu, and J. Yuan, “Time series modeling of pertussis incidence in China from 2004 to 2018 with a novel wavelet based SARIMA-NAR hybrid model,” PLoS One, vol. 13, no. 12, pp. 1–23, 2018, doi: 10.1371/journal.pone.0208404.
  • [33] Y. Wang, C. Xu, Z. Wang, S. Zhang, Y. Zhu, and J. Yuan, “Time series modeling of pertussis incidence in China from 2004 to 2018 with a novel wavelet based SARIMA-NAR hybrid model,” PLoS One, vol. 13, no. 12, pp. 1–23, 2018, doi: 10.1371/journal.pone.0208404.

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

Year 2021, , 123 - 131, 30.06.2021

Yıldıran Yılmaz , Selim Buyrukoğlu

https://doi.org/10.17350/HJSE19030000222
Cited By: 6

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.

Keywords

Covid-19 Neural Network ARIMA Time Series Correlated Additive Model

References

  • [1] H. Lu, C. W. Stratton, and Y. W. Tang, “Outbreak of pneumonia of unknown etiology in Wuhan, China: The mystery and the miracle,” J. Med. Virol., vol. 92, no. 4, pp. 401–402, 2020, doi: 10.1002/jmv.25678.
  • [1] H. Lu, C. W. Stratton, and Y. W. Tang, “Outbreak of pneumonia of unknown etiology in Wuhan, China: The mystery and the miracle,” J. Med. Virol., vol. 92, no. 4, pp. 401–402, 2020, doi: 10.1002/jmv.25678.
  • [2] Worldometers.info, “COVID-19 CORONAVIRUS PANDEMIC,” 2020. https://www.worldometers.info/coronavirus/?utm_campaign=CSauthorbio? (accessed Aug. 10, 2020).
  • [2] Worldometers.info, “COVID-19 CORONAVIRUS PANDEMIC,” 2020. https://www.worldometers.info/coronavirus/?utm_campaign=CSauthorbio? (accessed Aug. 10, 2020).
  • [3] M. Rypdal and G. Sugihara, “Inter-outbreak stability reflects the size of the susceptible pool and forecasts magnitudes of seasonal epidemics,” Nat. Commun., vol. 10, no. 1, 2019, doi: 10.1038/s41467-019-10099-y.
  • [3] M. Rypdal and G. Sugihara, “Inter-outbreak stability reflects the size of the susceptible pool and forecasts magnitudes of seasonal epidemics,” Nat. Commun., vol. 10, no. 1, 2019, doi: 10.1038/s41467-019-10099-y.
  • [4] J. K. Davis et al., “A genetic algorithm for identifying spatially-varying environmental drivers in a malaria time series model,” Environ. Model. Softw., vol. 119, no. February, pp. 275–284, 2019, doi: 10.1016/j.envsoft.2019.06.010.
  • [4] J. K. Davis et al., “A genetic algorithm for identifying spatially-varying environmental drivers in a malaria time series model,” Environ. Model. Softw., vol. 119, no. February, pp. 275–284, 2019, doi: 10.1016/j.envsoft.2019.06.010.
  • [5] J. M. Scavuzzo et al., “Modeling Dengue vector population using remotely sensed data and machine learning,” Acta Trop., vol. 185, no. October 2017, pp. 167–175, 2018, doi: 10.1016/j.actatropica.2018.05.003.
  • [5] J. M. Scavuzzo et al., “Modeling Dengue vector population using remotely sensed data and machine learning,” Acta Trop., vol. 185, no. October 2017, pp. 167–175, 2018, doi: 10.1016/j.actatropica.2018.05.003.
  • [6] R. Vaishya, M. Javaid, I. H. Khan, and A. Haleem, “Artificial Intelligence (AI) applications for COVID-19 pandemic,” Diabetes Metab. Syndr. Clin. Res. Rev., vol. 14, no. 4, pp. 337–339, 2020, doi: 10.1016/j.dsx.2020.04.012.
  • [6] R. Vaishya, M. Javaid, I. H. Khan, and A. Haleem, “Artificial Intelligence (AI) applications for COVID-19 pandemic,” Diabetes Metab. Syndr. Clin. Res. Rev., vol. 14, no. 4, pp. 337–339, 2020, doi: 10.1016/j.dsx.2020.04.012.
  • [7] S. P. Kaur and V. Gupta, “COVID-19 Vaccine: A comprehensive status report,” Virus Res., vol. 288, no. August, p. 198114, 2020, doi: 10.1016/j.virusres.2020.198114.
  • [7] S. P. Kaur and V. Gupta, “COVID-19 Vaccine: A comprehensive status report,” Virus Res., vol. 288, no. August, p. 198114, 2020, doi: 10.1016/j.virusres.2020.198114.
  • [8] D. Kang, H. Choi, J. H. Kim, and J. Choi, “Spatial epidemic dynamics of the COVID-19 outbreak in China,” Int. J. Infect. Dis., vol. 94, no. January, pp. 96–102, 2020, doi: 10.1016/j.ijid.2020.03.076.
  • [8] D. Kang, H. Choi, J. H. Kim, and J. Choi, “Spatial epidemic dynamics of the COVID-19 outbreak in China,” Int. J. Infect. Dis., vol. 94, no. January, pp. 96–102, 2020, doi: 10.1016/j.ijid.2020.03.076.
  • [9] M. A. A. Al-Qaness, A. A. Ewees, H. Fan, and M. A. El Aziz, “Optimization method for forecasting confirmed cases of COVID-19 in China,” Appl. Sci., vol. 9, no. 3, 2020, doi: 10.3390/JCM9030674.
  • [9] M. A. A. Al-Qaness, A. A. Ewees, H. Fan, and M. A. El Aziz, “Optimization method for forecasting confirmed cases of COVID-19 in China,” Appl. Sci., vol. 9, no. 3, 2020, doi: 10.3390/JCM9030674.
  • [10] A. J. Kucharski et al., “Early dynamics of transmission and control of COVID-19: a mathematical modelling study,” Lancet Infect. Dis., vol. 20, no. 5, pp. 553–558, 2020, doi: 10.1016/S1473-3099(20)30144-4.
  • [10] A. J. Kucharski et al., “Early dynamics of transmission and control of COVID-19: a mathematical modelling study,” Lancet Infect. Dis., vol. 20, no. 5, pp. 553–558, 2020, doi: 10.1016/S1473-3099(20)30144-4.
  • [11] P. Lauret, F. Heymes, S. Forestier, L. Aprin, A. Pey, and M. Perrin, “Forecasting powder dispersion in a complex environment using Artificial Neural Networks,” Process Saf. Environ. Prot., vol. 110, pp. 71–76, 2017, doi: 10.1016/j.psep.2017.02.003.
  • [11] P. Lauret, F. Heymes, S. Forestier, L. Aprin, A. Pey, and M. Perrin, “Forecasting powder dispersion in a complex environment using Artificial Neural Networks,” Process Saf. Environ. Prot., vol. 110, pp. 71–76, 2017, doi: 10.1016/j.psep.2017.02.003.
  • [12] A. Narin, C. Kaya, and Z. Pamuk, “Department of Biomedical Engineering, Zonguldak Bulent Ecevit University, 67100, Zonguldak, Turkey.,” arXiv Prepr. arXiv2003.10849., 2020.
  • [12] A. Narin, C. Kaya, and Z. Pamuk, “Department of Biomedical Engineering, Zonguldak Bulent Ecevit University, 67100, Zonguldak, Turkey.,” arXiv Prepr. arXiv2003.10849., 2020.
  • [13] H. A. Babikir et al., “Noise prediction of axial piston pump based on different valve materials using a modified artificial neural network model,” Alexandria Eng. J., vol. 58, no. 3, pp. 1077–1087, 2019, doi: 10.1016/j.aej.2019.09.010.
  • [13] H. A. Babikir et al., “Noise prediction of axial piston pump based on different valve materials using a modified artificial neural network model,” Alexandria Eng. J., vol. 58, no. 3, pp. 1077–1087, 2019, doi: 10.1016/j.aej.2019.09.010.
  • [14] M. A. Elaziz, A. H. Elsheikh, and S. W. Sharshir, “Improved prediction of oscillatory heat transfer coefficient for a thermoacoustic heat exchanger using modified adaptive neuro-fuzzy inference system,” Int. J. Refrig., vol. 102, pp. 47–54, 2019, doi: 10.1016/j.ijrefrig.2019.03.009.
  • [14] M. A. Elaziz, A. H. Elsheikh, and S. W. Sharshir, “Improved prediction of oscillatory heat transfer coefficient for a thermoacoustic heat exchanger using modified adaptive neuro-fuzzy inference system,” Int. J. Refrig., vol. 102, pp. 47–54, 2019, doi: 10.1016/j.ijrefrig.2019.03.009.
  • [15] A. H. Elsheikh, S. W. Sharshir, M. Abd Elaziz, A. E. Kabeel, W. Guilan, and Z. Haiou, “Modeling of solar energy systems using artificial neural network: A comprehensive review,” Sol. Energy, vol. 180, no. January, pp. 622–639, 2019, doi: 10.1016/j.solener.2019.01.037.
  • [15] A. H. Elsheikh, S. W. Sharshir, M. Abd Elaziz, A. E. Kabeel, W. Guilan, and Z. Haiou, “Modeling of solar energy systems using artificial neural network: A comprehensive review,” Sol. Energy, vol. 180, no. January, pp. 622–639, 2019, doi: 10.1016/j.solener.2019.01.037.
  • [16] T. A. Shehabeldeen et al., “A Novel Method for Predicting Tensile Strength of Friction Stir Welded AA6061 Aluminium Alloy Joints Based on Hybrid Random Vector Functional Link and Henry Gas Solubility Optimization,” IEEE Access, vol. 8, pp. 79896–79907, 2020, doi: 10.1109/ACCESS.2020.2990137.
  • [16] T. A. Shehabeldeen et al., “A Novel Method for Predicting Tensile Strength of Friction Stir Welded AA6061 Aluminium Alloy Joints Based on Hybrid Random Vector Functional Link and Henry Gas Solubility Optimization,” IEEE Access, vol. 8, pp. 79896–79907, 2020, doi: 10.1109/ACCESS.2020.2990137.
  • [17] N. Hasan, “A Methodological Approach for Predicting COVID-19 Epidemic Using EEMD-ANN Hybrid Model,” Internet of Things, vol. 11, p. 100228, 2020, doi: 10.1016/j.iot.2020.100228.
  • [17] N. Hasan, “A Methodological Approach for Predicting COVID-19 Epidemic Using EEMD-ANN Hybrid Model,” Internet of Things, vol. 11, p. 100228, 2020, doi: 10.1016/j.iot.2020.100228.
  • [18] T. Chakraborty and I. Ghosh, “Real-time forecasts and risk assessment of novel coronavirus (COVID-19) cases: A data-driven analysis,” Chaos, Solitons and Fractals, vol. 135, 2020, doi: 10.1016/j.chaos.2020.109850.
  • [18] T. Chakraborty and I. Ghosh, “Real-time forecasts and risk assessment of novel coronavirus (COVID-19) cases: A data-driven analysis,” Chaos, Solitons and Fractals, vol. 135, 2020, doi: 10.1016/j.chaos.2020.109850.
  • [19] G. Pinter, I. Felde, A. Mosavi, P. Ghamisi, and R. Gloaguen, “COVID-19 Pandemic Prediction for Hungary ; a Hybrid Machine Learning Approach,” 2020.
  • [19] G. Pinter, I. Felde, A. Mosavi, P. Ghamisi, and R. Gloaguen, “COVID-19 Pandemic Prediction for Hungary ; a Hybrid Machine Learning Approach,” 2020.
  • [20] S. Singh, K. S. Parmar, J. Kumar, and S. J. S. Makkhan, “Development of new hybrid model of discrete wavelet decomposition and autoregressive integrated moving average (ARIMA) models in application to one month forecast the casualties cases of COVID-19,” Chaos, Solitons and Fractals, vol. 135, pp. 1–8, 2020, doi: 10.1016/j.chaos.2020.109866.
  • [20] S. Singh, K. S. Parmar, J. Kumar, and S. J. S. Makkhan, “Development of new hybrid model of discrete wavelet decomposition and autoregressive integrated moving average (ARIMA) models in application to one month forecast the casualties cases of COVID-19,” Chaos, Solitons and Fractals, vol. 135, pp. 1–8, 2020, doi: 10.1016/j.chaos.2020.109866.
  • [21] A. Altan and S. Karasu, “Recognition of COVID-19 disease from X-ray images by hybrid model consisting of 2D curvelet transform, chaotic salp swarm algorithm and deep learning technique,” Chaos, Solitons and Fractals, vol. 140, 2020, doi: 10.1016/j.chaos.2020.110071.
  • [21] A. Altan and S. Karasu, “Recognition of COVID-19 disease from X-ray images by hybrid model consisting of 2D curvelet transform, chaotic salp swarm algorithm and deep learning technique,” Chaos, Solitons and Fractals, vol. 140, 2020, doi: 10.1016/j.chaos.2020.110071.
  • [22] N. Zheng et al., “Predicting COVID-19 in China Using Hybrid AI Model,” IEEE Trans. Cybern., vol. 50, no. 7, pp. 2891–2904, 2020, doi: 10.1109/TCYB.2020.2990162.
  • [22] N. Zheng et al., “Predicting COVID-19 in China Using Hybrid AI Model,” IEEE Trans. Cybern., vol. 50, no. 7, pp. 2891–2904, 2020, doi: 10.1109/TCYB.2020.2990162.
  • [23] S. Chae, S. Kwon, and D. Lee, “Predicting infectious disease using deep learning and big data,” Int. J. Environ. Res. Public Health, vol. 15, no. 8, 2018, doi: 10.3390/ijerph15081596.
  • [23] S. Chae, S. Kwon, and D. Lee, “Predicting infectious disease using deep learning and big data,” Int. J. Environ. Res. Public Health, vol. 15, no. 8, 2018, doi: 10.3390/ijerph15081596.
  • [24] R. C. esa. Sato, “Disease management with ARIMA model in time series,” Einstein (Sao Paulo)., vol. 11, no. 1, pp. 128–131, 2013, doi: 10.1590/S1679-45082013000100024.
  • [24] R. C. esa. Sato, “Disease management with ARIMA model in time series,” Einstein (Sao Paulo)., vol. 11, no. 1, pp. 128–131, 2013, doi: 10.1590/S1679-45082013000100024.
  • [25] Z. He and H. Tao, “Epidemiology and ARIMA model of positive-rate of influenza viruses among children in Wuhan, China: A nine-year retrospective study,” Int. J. Infect. Dis., vol. 74, pp. 61–70, 2018, doi: 10.1016/j.ijid.2018.07.003.
  • [25] Z. He and H. Tao, “Epidemiology and ARIMA model of positive-rate of influenza viruses among children in Wuhan, China: A nine-year retrospective study,” Int. J. Infect. Dis., vol. 74, pp. 61–70, 2018, doi: 10.1016/j.ijid.2018.07.003.
  • [26] D. Benvenuto, M. Giovanetti, L. Vassallo, S. Angeletti, and M. Ciccozzi, “Application of the ARIMA model on the COVID-2019 epidemic dataset,” Data Br., vol. 29, p. 105340, 2020, doi: 10.1016/j.dib.2020.105340.
  • [26] D. Benvenuto, M. Giovanetti, L. Vassallo, S. Angeletti, and M. Ciccozzi, “Application of the ARIMA model on the COVID-2019 epidemic dataset,” Data Br., vol. 29, p. 105340, 2020, doi: 10.1016/j.dib.2020.105340.
  • [27] X. Li, C. Zhang, B. Zhang, and K. Liu, “A comparative time series analysis and modeling of aerosols in the contiguous United States and China,” Sci. Total Environ., vol. 690, pp. 799–811, 2019, doi: 10.1016/j.scitotenv.2019.07.072.
  • [27] X. Li, C. Zhang, B. Zhang, and K. Liu, “A comparative time series analysis and modeling of aerosols in the contiguous United States and China,” Sci. Total Environ., vol. 690, pp. 799–811, 2019, doi: 10.1016/j.scitotenv.2019.07.072.
  • [28] M. Wang et al., “A novel model for malaria prediction based on ensemble algorithms,” PLoS One, vol. 14, no. 12, pp. 1–15, 2019, doi: 10.1371/journal.pone.0226910.
  • [28] M. Wang et al., “A novel model for malaria prediction based on ensemble algorithms,” PLoS One, vol. 14, no. 12, pp. 1–15, 2019, doi: 10.1371/journal.pone.0226910.
  • [29] J. C. B. Gamboa, “Deep Learning for Time-Series Analysis,” 2017, [Online]. Available: http://arxiv.org/abs/1701.01887.
  • [29] J. C. B. Gamboa, “Deep Learning for Time-Series Analysis,” 2017, [Online]. Available: http://arxiv.org/abs/1701.01887.
  • [30] S. Karasu, A. Altan, Z. Sarac, and R. Hacioglu, “Prediction of Bitcoin prices with machine learning methods using time series data | Zaman serisi verilerini kullanarak makine öǧrenmesi yöntemleri ile bitcoin fiyat tahmini,” 26th IEEE Signal Process. Commun. Appl. Conf. SIU 2018, pp. 1–4, 2018, doi: 10.1109/SIU.2018.8404760.
  • [30] S. Karasu, A. Altan, Z. Sarac, and R. Hacioglu, “Prediction of Bitcoin prices with machine learning methods using time series data | Zaman serisi verilerini kullanarak makine öǧrenmesi yöntemleri ile bitcoin fiyat tahmini,” 26th IEEE Signal Process. Commun. Appl. Conf. SIU 2018, pp. 1–4, 2018, doi: 10.1109/SIU.2018.8404760.
  • [31] S. ÇINAROĞLU, “Sağlik HarcamasiniTahmi̇ni̇ndeMaki̇neÖğrenmesi̇ RegresyoYöntemleri̇ni̇n Karşilaştirilmasi,” Uludağ Univ. J. Fac. Eng., vol. 22, no. 2, pp. 179–200, 2017, doi: 10.17482/uumfd.338805.
  • [31] S. ÇINAROĞLU, “Sağlik HarcamasiniTahmi̇ni̇ndeMaki̇neÖğrenmesi̇ RegresyoYöntemleri̇ni̇n Karşilaştirilmasi,” Uludağ Univ. J. Fac. Eng., vol. 22, no. 2, pp. 179–200, 2017, doi: 10.17482/uumfd.338805.
  • [32] P. M. Swamidass, Encyclopedia of Production and Manufacturing Management, no. June 2000. 2000.
  • [32] P. M. Swamidass, Encyclopedia of Production and Manufacturing Management, no. June 2000. 2000.
  • [33] Y. Wang, C. Xu, Z. Wang, S. Zhang, Y. Zhu, and J. Yuan, “Time series modeling of pertussis incidence in China from 2004 to 2018 with a novel wavelet based SARIMA-NAR hybrid model,” PLoS One, vol. 13, no. 12, pp. 1–23, 2018, doi: 10.1371/journal.pone.0208404.
  • [33] Y. Wang, C. Xu, Z. Wang, S. Zhang, Y. Zhu, and J. Yuan, “Time series modeling of pertussis incidence in China from 2004 to 2018 with a novel wavelet based SARIMA-NAR hybrid model,” PLoS One, vol. 13, no. 12, pp. 1–23, 2018, doi: 10.1371/journal.pone.0208404.
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Details

Primary Language English
Subjects Engineering
Journal Section Research Article
Authors

Yıldıran Yılmaz 0000-0002-5337-6090

Selim Buyrukoğlu 0000-0001-7844-3168

Publication Date June 30, 2021
Submission Date January 15, 2021
Published in Issue Year 2021

Cite

Vancouver Yılmaz Y, Buyrukoğlu S. Hybrid Machine Learning Model Coupled with School Closure For Forecasting COVID-19 Cases in the Most Affected Countries. Hittite J Sci Eng. 2021;8(2):123-31.

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