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Forecasting The Impact of Vaccination on Daily Cases in Turkey for Covid-19

Year 2023, Volume: 11 Issue: 1, 19 - 26, 30.01.2023
https://doi.org/10.21541/apjess.1137177

Abstract

This study, it is aimed to investigate the effect of the vaccine on the cases in the fight against Covid-19, which threatens the whole world. The number of Covid-19 cases, which were tried to be reduced with various precautions worldwide and in Turkey, has become a new hope with the start of vaccination. The increase in the effect of the vaccination, which started in January 2021, brought the need to examine the vaccination rate in three groups as slow, medium, and fast. In this study, different scenarios were tried in the number of vaccinations applied in Turkey, and the daily number of cases until December 2021 was forecasted by Artificial Neural Networks (ANN). The effect of restrictions and vaccination on the number of Covid-19 cases was investigated. Different training algorithms were used, and the best success rate was found with the comparison method. Accurate forecasting of cases will let policymakers take precautions on time. Moreover, the effect of vaccination on cases should be investigated.

References

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  • [5] M. Alazab et all, “COVID-19 prediction and detection using deep learning”. International Journal of Computer Information Systems and Industrial Management Applications, 2020, ch. 12(1), pp. 168-181.
  • [6] N. Hasan, “A methodological approach for predicting COVID-19 epidemic using EEMD-ANN hybrid model”. Internet of things, 2020, ch. 11(100228), pp. 1-10.
  • [7] S Ardabili et all, “Coronavirus Disease (COVID-19) Global Prediction Using Hybrid Artificial Intelligence Method of ANN Trained with Grey Wolf Optimizer”, 2020 IEEE 3rd International Conference and Workshop in Óbuda on Electrical and Power Engineering, 19 November 2020 Budapest, Hungary, Accessed on: July. 05, 2021.
  • [8] N.S. Özen, S. Saraç and M. Koyuncu, “COVID-19 Vakalarının Makine Öğrenmesi Algoritmaları ile Tahmini: Amerika Birleşik Devletleri Örneği”. Avrupa Bilim ve Teknoloji Dergisi, 2021, ch. 22, pp. 134-139.
  • [9] H. Ankaralı, S. Ankaralı “Forecasting of the Number of Intensive Care Beds and Hospital Capacity for COVID-19 Outbreak in Turkey Until the End of April”. Türk Yoğun Bakım Dergisi, 2020, ch. 18(1), pp. 22-30.
  • [10] F. Rustam et all, “COVID-19 Future Forecasting Using Supervised Machine Learning Models”. IEEE Access, 2020, ch. 8(1), pp. 101489-101499.
  • [11] A.K. Sahai et all, “ARIMA modelling & forecasting of COVID-19 in top five affected countries”. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 2020, ch. 14(5), pp.1419-1427.
  • [12] IHME COVID-19 health service utilization forecasting team. “Forecasting COVID-19 impact on hospital bed-days, ICU-days, ventilator-days and deaths by US state in the next 4 months”. MedRxiv The Preprint Server for Health Sciences, 2020, Accessed on: Feb. 12, 2021.
  • [13] T. Turan , G. Turan and U. Köse , "Uyarlamalı Ağ Tabanlı Bulanık Mantık Çıkarım Sistemi ve Yapay Sinir Ağları ile Türkiye’deki COVID-19 Vefat Sayısının Tahmin Edilmesi", Bilişim Teknolojileri Dergisi, 2022, ch. 15(2), pp. 97-105.
  • [14] O. Sevli, V. G. Başer, “COVID-19 Salgınına Yönelik Zaman Serisi Verileri ile Prophet Model Kullanarak Makine Öğrenmesi Temelli Vaka Tahminlemesi”, Avrupa Bilim ve Teknoloji Dergisi, 2020, ch. (19), pp. 827-835.
  • [15] S. Namasudra et all, “Nonlinear Neural Network Based Forecasting Model for Predicting COVID-19 Cases”, Neural Process Lett, 2021, https://doi.org/10.1007/s11063-021-10495-w
  • [16] Y. Eroğlu, “Forecasting Models For Covid-19 Cases Of Turkey Using Artificial Neural Networks And Deep Learning”, Journal of Industrial Engineering, 2020, ch.. 31(3), pp. 354-372.
  • [17] D. Fanelli, F. Piazza, “Analysis And Forecast of COVID-19 Spreading In China, Italy And France”, Chaos, Solitons & Fractals, 2020, ch. 134, pp. 1-5.
  • [18] N. Delgrange et all, “Neural networks for prediction of ultrafiltration transmembrane pressure–application to drinking water production”. Journal of membrane science, 1998, ch. 150(1), pp. 111- 123.
  • [19] E. Cengiz et all “Pedestrian and Vehicles Detection with ResNet in Aerial Images”. In 4th International Symposium on Innovative Approaches in Engineering and Natural Sciences, Samsun, Türkiye, 22 December 2019.
  • [20] M.M. Kelek et al, “RLBP Metodu ile Mamografi Görüntülerinin İncelenmesi ve Sınıflandırılması”. Afyon Kocatepe Üniversitesi Uluslararası Mühendislik Teknolojileri ve Uygulamalı Bilimler Dergisi, 2021, ch. 4(2), pp. 59-64.
  • [21] Ö. Civalek, “The Analysis of Time Dependent Deformation in R.C. Members By Artificial Neural Network”. Pamukkale Üniversitesi Mühendislik Dergisi, 1997, ch. 3(2), pp. 331-335.
  • [22] R. Ata, “Akhisar Bölgesi için Ortalama Rüzgar Hızlarına Bağlı Rüzgar Esme Sürelerinin Yapay Sinir Ağları ile Tahmini”. Pamukkale Üniversitesi Mühendislik Dergisi, 2014, ch. 20(5), pp. 162-165.
  • [23] S. J. Taylor and B. Letham, “Forecasting at scale,” Am. Stat., 2018, ch. 72(1), pp. 37–45.
  • [24] H. Altun, K.M. Curtis, “Exploiting the statistical characteristic of the speech signals for an improved neural learning in a MLP neural network”. Neural Networks for Signal Processing VIII. Proceedings of the 1998 IEEE Signal Processing Society Workshop, Cambridge, UK, 2 September 1998.
  • [25] E. Yıldız, N. Çetinkaya “Elektrik Güç Sistemlerindeki Kaçak Kullanımların Tahmini”, Journal of Investigations on Engineering and Technology, 2022, ch. 5(1), pp. 1-10.
  • [26] Ministry of Health of the Republic of Türkiye. “Website for Covid-19”. Accessed on: January 13, 2021 https://covid19.saglik.gov.tr/.
Year 2023, Volume: 11 Issue: 1, 19 - 26, 30.01.2023
https://doi.org/10.21541/apjess.1137177

Abstract

References

  • [1] World Health Organization (WHO). “Coronavirus disease 2019 (COVID-19) Situation Report – 62” Accessed on: June 25, 2021. https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200322-sitrep-62-covid-19.pdf?sfvrsn=755c76cd_2
  • [2] NTV TV Channel. “World Health Organization declared international emergency for Corona virus” Accessed on: January. 13, 2022. https://www.ntv.com.tr/galeri/dunya/corona-virusu-icin-dunya-saglik-orgutu-uluslararasi-acil-durum-ilan-etti-sars-be,3O3Hubz-NUOgO3ymqTqtBg
  • [3] S.J. Daniel “Education and the COVID-19 pandemic”. Prospects, 2020, ch. 49(1), pp. 91–96.
  • [4] E. Cengiz, C. Yılmaz and H. Kahraman. “Classification of Human and Vehicles with The Deep Learning Based on Transfer Learning Method”. Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 2021, ch. 9(3), pp. 215-225.
  • [5] M. Alazab et all, “COVID-19 prediction and detection using deep learning”. International Journal of Computer Information Systems and Industrial Management Applications, 2020, ch. 12(1), pp. 168-181.
  • [6] N. Hasan, “A methodological approach for predicting COVID-19 epidemic using EEMD-ANN hybrid model”. Internet of things, 2020, ch. 11(100228), pp. 1-10.
  • [7] S Ardabili et all, “Coronavirus Disease (COVID-19) Global Prediction Using Hybrid Artificial Intelligence Method of ANN Trained with Grey Wolf Optimizer”, 2020 IEEE 3rd International Conference and Workshop in Óbuda on Electrical and Power Engineering, 19 November 2020 Budapest, Hungary, Accessed on: July. 05, 2021.
  • [8] N.S. Özen, S. Saraç and M. Koyuncu, “COVID-19 Vakalarının Makine Öğrenmesi Algoritmaları ile Tahmini: Amerika Birleşik Devletleri Örneği”. Avrupa Bilim ve Teknoloji Dergisi, 2021, ch. 22, pp. 134-139.
  • [9] H. Ankaralı, S. Ankaralı “Forecasting of the Number of Intensive Care Beds and Hospital Capacity for COVID-19 Outbreak in Turkey Until the End of April”. Türk Yoğun Bakım Dergisi, 2020, ch. 18(1), pp. 22-30.
  • [10] F. Rustam et all, “COVID-19 Future Forecasting Using Supervised Machine Learning Models”. IEEE Access, 2020, ch. 8(1), pp. 101489-101499.
  • [11] A.K. Sahai et all, “ARIMA modelling & forecasting of COVID-19 in top five affected countries”. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 2020, ch. 14(5), pp.1419-1427.
  • [12] IHME COVID-19 health service utilization forecasting team. “Forecasting COVID-19 impact on hospital bed-days, ICU-days, ventilator-days and deaths by US state in the next 4 months”. MedRxiv The Preprint Server for Health Sciences, 2020, Accessed on: Feb. 12, 2021.
  • [13] T. Turan , G. Turan and U. Köse , "Uyarlamalı Ağ Tabanlı Bulanık Mantık Çıkarım Sistemi ve Yapay Sinir Ağları ile Türkiye’deki COVID-19 Vefat Sayısının Tahmin Edilmesi", Bilişim Teknolojileri Dergisi, 2022, ch. 15(2), pp. 97-105.
  • [14] O. Sevli, V. G. Başer, “COVID-19 Salgınına Yönelik Zaman Serisi Verileri ile Prophet Model Kullanarak Makine Öğrenmesi Temelli Vaka Tahminlemesi”, Avrupa Bilim ve Teknoloji Dergisi, 2020, ch. (19), pp. 827-835.
  • [15] S. Namasudra et all, “Nonlinear Neural Network Based Forecasting Model for Predicting COVID-19 Cases”, Neural Process Lett, 2021, https://doi.org/10.1007/s11063-021-10495-w
  • [16] Y. Eroğlu, “Forecasting Models For Covid-19 Cases Of Turkey Using Artificial Neural Networks And Deep Learning”, Journal of Industrial Engineering, 2020, ch.. 31(3), pp. 354-372.
  • [17] D. Fanelli, F. Piazza, “Analysis And Forecast of COVID-19 Spreading In China, Italy And France”, Chaos, Solitons & Fractals, 2020, ch. 134, pp. 1-5.
  • [18] N. Delgrange et all, “Neural networks for prediction of ultrafiltration transmembrane pressure–application to drinking water production”. Journal of membrane science, 1998, ch. 150(1), pp. 111- 123.
  • [19] E. Cengiz et all “Pedestrian and Vehicles Detection with ResNet in Aerial Images”. In 4th International Symposium on Innovative Approaches in Engineering and Natural Sciences, Samsun, Türkiye, 22 December 2019.
  • [20] M.M. Kelek et al, “RLBP Metodu ile Mamografi Görüntülerinin İncelenmesi ve Sınıflandırılması”. Afyon Kocatepe Üniversitesi Uluslararası Mühendislik Teknolojileri ve Uygulamalı Bilimler Dergisi, 2021, ch. 4(2), pp. 59-64.
  • [21] Ö. Civalek, “The Analysis of Time Dependent Deformation in R.C. Members By Artificial Neural Network”. Pamukkale Üniversitesi Mühendislik Dergisi, 1997, ch. 3(2), pp. 331-335.
  • [22] R. Ata, “Akhisar Bölgesi için Ortalama Rüzgar Hızlarına Bağlı Rüzgar Esme Sürelerinin Yapay Sinir Ağları ile Tahmini”. Pamukkale Üniversitesi Mühendislik Dergisi, 2014, ch. 20(5), pp. 162-165.
  • [23] S. J. Taylor and B. Letham, “Forecasting at scale,” Am. Stat., 2018, ch. 72(1), pp. 37–45.
  • [24] H. Altun, K.M. Curtis, “Exploiting the statistical characteristic of the speech signals for an improved neural learning in a MLP neural network”. Neural Networks for Signal Processing VIII. Proceedings of the 1998 IEEE Signal Processing Society Workshop, Cambridge, UK, 2 September 1998.
  • [25] E. Yıldız, N. Çetinkaya “Elektrik Güç Sistemlerindeki Kaçak Kullanımların Tahmini”, Journal of Investigations on Engineering and Technology, 2022, ch. 5(1), pp. 1-10.
  • [26] Ministry of Health of the Republic of Türkiye. “Website for Covid-19”. Accessed on: January 13, 2021 https://covid19.saglik.gov.tr/.
There are 26 citations in total.

Details

Primary Language English
Subjects Artificial Intelligence
Journal Section Research Articles
Authors

Enes Yıldız 0000-0001-9116-4980

Muhammed Mustafa Kelek 0000-0002-9915-4776

Fatih Onur Hocaoğlu 0000-0002-3640-7676

Yüksel Oğuz 0000-0002-5233-151X

Publication Date January 30, 2023
Submission Date July 24, 2022
Published in Issue Year 2023 Volume: 11 Issue: 1

Cite

IEEE E. Yıldız, M. M. Kelek, F. O. Hocaoğlu, and Y. Oğuz, “Forecasting The Impact of Vaccination on Daily Cases in Turkey for Covid-19”, APJESS, vol. 11, no. 1, pp. 19–26, 2023, doi: 10.21541/apjess.1137177.

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