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Forecasting of COVID-19 Cases Under Different Precaution Strategies in Turkey

Yıl 2024, Cilt: 12 Sayı: 3, 1279 - 1295, 31.07.2024

Serdar Arslan

https://doi.org/10.29130/dubited.1234168

Öz

The coronavirus disease started at the end of 2019 and affected all the countries in the world. In Turkey, the vaccination process started at the beginning of 2021 but performed in slow progress. Thus, the Turkish Government tried to implement precautions to control this virus's spread. In this study, we evaluated and compared five different forecasting models, ARIMA, Prophet, NARNN, Stacked LSTM, and Bidirectional LSTM, in order to show the effect of these precaution strategies on virus spread using a real-world data set. According to the test results, ARIMA and Prophet were found to be the most accurate models for small data sets that are split regarding precautions. Moreover, test results showed that when data size grows, LSTM model performance increases. However, these models' performance decreased when we fed these models by using the entire data set without splitting.

Anahtar Kelimeler

COVID-19 Forecasting ARIMA Prophet NARNN Deep learning LSTM

Kaynakça

  • [1] A. Rismanbaf, “Potential Treatments for COVID-19; a Narrative Literature Review,” Archives of academic emergency medicine, vol.8(1), 2020.
  • [2] I. Rahimi, F. Chen, and A. H. Gandomi, “A review on COVID-19 forecasting models,” Neural Comput. Appl., vol. 35, pp.23671–23681, 2023.
  • [3] I. Nesteruk, “Statistics based predictions of coronavirus 2019-nCoV spreading in mainland China,” medRxiv, 2020.
  • [4] C. Anastassopoulou, L. Russo, A. Tsakris, and C. Siettos, “Data-based analysis, modelling and forecasting of the COVID-19 outbreak,” PLoS One, vol. 15, no. 3, pp. 1–21, 2020.
  • [5] G. Giordano et al., “Modelling the COVID-19 epidemic and implementation of population-wide interventions in Italy,” Nat. Med., vol. 26, no. 6, pp. 855–860, 2020.
  • [6] S. Moein et al., “Inefficiency of SIR models in forecasting COVID-19 epidemic: a case study of Isfahan,” Sci. Rep., vol. 11, no. 1, p. 4725, 2021.
  • [7] İ. Kırbaş, A. Sözen, A. D. Tuncer, and F. Ş. Kazancıoğlu, “Comparative analysis and forecasting of COVID-19 cases in various European countries with ARIMA, NARNN and LSTM approaches,” Chaos, Solitons and Fractals, vol. 138, Sep. 2020.
  • [8] T. Dehesh, H. A. Mardani-Fard, and P. Dehesh, “Forecasting of COVID-19 Confirmed Cases in Different Countries with ARIMA Models,” medRxiv ,2020.
  • [9] 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.
  • [10] M. Yousaf, S. Zahir, M. Riaz, S. M. Hussain, and K. Shah, “Statistical analysis of forecasting COVID-19 for upcoming month in Pakistan,” Chaos, Solitons & Fractals, vol. 138, p. 109926, 2020.
  • [11] O. D. Ilie, R. O. Cojocariu, A. Ciobica, S. I. Timofte, I. Mavroudis, and B. Doroftei, “Forecasting the spreading of COVID-19 across nine countries from Europe, Asia, and the American continents using the arima models,” Microorganisms, vol. 8, no. 8, pp. 1–19, 2020.
  • [12] Z. Ceylan, “Estimation of COVID-19 prevalence in Italy, Spain, and France,” Sci. Total Environ., vol. 729, Aug. 2020.
  • [13] Facebook, “Prophet Web Page.” https://facebook.github.io/prophet/ (accessed Feb. 15, 2023).
  • [14] B. M. Ndiaye, L. Tendeng, and D. Seck, “Analysis of the COVID-19 pandemic by SIR model and machine learning technics for forecasting,”, arXiv preprint arXiv:2004.01574, 2020.
  • [15] S. S. Helli, Ç. Demirci, O. Çoban, and A. Hamamci, “Short-Term Forecasting COVID-19 Cases in Turkey Using Long Short-Term Memory Network,” TIPTEKNO 2020 - Tip Teknol. Kongresi - 2020 Med. Technol. Congr. TIPTEKNO 2020, pp. 2–5, 2020.
  • [16] İ. Kırbaş, A. Sözen, A. D. Tuncer, and F. Ş. Kazancıoğlu, “Comparative analysis and forecasting of COVID-19 cases in various European countries with ARIMA, NARNN and LSTM approaches,” Chaos, Solitons and Fractals, vol. 138, 2020.
  • [17] S. Namasudra, S. Dhamodharavadhani, and R. Rathipriya, “Nonlinear Neural Network Based Forecasting Model for Predicting COVID-19 Cases,” Neural Process. Lett., vol.55, pp-171-191, 2023.
  • [18] A. I. Saba and A. H. Elsheikh, “Forecasting the prevalence of COVID-19 outbreak in Egypt using nonlinear autoregressive artificial neural networks,” Process Saf. Environ. Prot., vol. 141, pp. 1–8, 2020.
  • [19] W. Jiang and H. D. Schotten, “Deep Learning for Fading Channel Prediction,” IEEE Open J. Commun. Soc., vol. 1, pp. 320–332, 2020.
  • [20] A. Tomar and N. Gupta, “Prediction for the spread of COVID-19 in India and effectiveness of preventive measures,” Sci. Total Environ., vol. 728, p. 138762, 2020.
  • [21] V. K. R. Chimmula and L. Zhang, “Time series forecasting of COVID-19 transmission in Canada using LSTM networks,” Chaos, Solitons and Fractals, vol. 135, Jun. 2020.
  • [22] R. Pal, A. A. Sekh, S. Kar, and D. K. Prasad, “Neural Network Based Country Wise Risk Prediction of COVID-19", Applied Sciences, vol.10(18), 2020.
  • [23] A. J. Aljaaf, T. M. Mohsin, D. Al-Jumeily, and M. Alloghani, “A fusion of data science and feed-forward neural network-based modelling of COVID-19 outbreak forecasting in IRAQ,” J. Biomed. Inform., vol. 118, no.04, p. 103766, 2021.
  • [24] A. Dairi, F. Harrou, A. Zeroual, M. M. Hittawe, and Y. Sun, “Comparative study of machine learning methods for COVID-19 transmission forecasting,” J. Biomed. Inform., vol. 118, no. 04, p. 103791, 2021.
  • [25] S. Dutta and S. Kumar Bandyopadhyay, “Machine learning approach for confirmation of COVID-19 cases: positive, negative, death and release,” Iberoam. J. Med., vol. 03, pp. 172–177, 2020.
  • [26] S. Arslan, “A hybrid forecasting model using LSTM and Prophet for energy consumption with decomposition of time series data,”, PeerJ Computer Science, vol.8, 2022.
  • [27] S. Arslan, “Gated recurrent unit network-based fuzzy time series forecasting model,” Afyon Kocatepe Univ. J. Sci. Eng., vol. 23, no. 3, pp. 677–692, 2023.
  • [28] S. Tiwari, P. Chanak, and S. K. Singh, “A Review of the Machine Learning Algorithms for Covid-19 Case Analysis,” IEEE Trans. Artif. Intell., vol. 4, no. 1, pp. 44–59, 2023.
  • [29] F. Kamalov, K. Rajab, A. K. Cherukuri, A. Elnagar, and M. Safaraliev, “Deep learning for Covid-19 forecasting: State-of-the-art review.,” Neurocomputing, vol. 511, pp. 142–154, 2022.
  • [30] A. S. Ahmar and E. B. del Val, “SutteARIMA: Short-term forecasting method, a case: Covid-19 and stock market in Spain,” Sci. Total Environ., vol. 729, p. 138883, 2020.
  • [31] M. A. A. Al-qaness, A. A. Ewees, H. Fan, and M. Abd El Aziz, “Optimization Method for Forecasting Confirmed Cases of COVID-19 in China,” J. Clin. Med., vol. 9, no. 3, 2020.
  • [32] R. Salgotra, M. Gandomi, and A. H. Gandomi, “Time Series Analysis and Forecast of the COVID-19 Pandemic in India using Genetic Programming,” Chaos, Solitons & Fractals, vol. 138, p. 109945, 2020.
  • [33] S. Dil, N. Dil, and Z. H. Maken, “COVID-19 Trends and Forecast in the Eastern Mediterranean Region With a Particular Focus on Pakistan.,” Cureus, vol. 12, no. 6, p. e8582, Jun. 2020.
  • [34] Ministry of Health Web Page, https://covid19.saglik.gov.tr/ (accessed May 23, 2021).
  • [35] W. H. Organization, https://www.who.int/emergencies/diseases/novel-coronavirus-2019 (accessed Feb. 15, 2021).
  • [36] S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Comput., vol. 9, no. 8, pp. 1735–1780, Nov. 1997.
  • [37] A. Graves, A. Mohamed, and G. E. Hinton, “Speech Recognition with Deep Recurrent Neural Networks,” 2013 IEEE international conference on acoustics, speech and signal processing (pp. 6645-6649), 2013.
  • [38] M. Schuster and K. K. Paliwal, “Bidirectional recurrent neural networks,” IEEE Trans. Signal Process., vol. 45, no. 11, pp. 2673–2681, 1997.
  • [39] Z. Cui and Y. Wang, “Deep bidirectional and unidirectional LSTM recurrent neural network for network-wide traffic speed prediction,” arXiv preprint arXiv:1801.02143, 2018.
  • [40] J. G. De Gooijer and R. J. Hyndman, “25 years of time series forecasting,” Int. J. Forecast., vol. 22, no. 3, pp. 443–473, 2006.
  • [41] N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A Simple Way to Prevent Neural Networks from Overfitting,” J. Mach. Learn. Res., vol. 15, no. 56, pp. 1929–1958, 2014.

Türkiye’de COVID-19 Vakalarının Farklı Önlemler Altında Tahminlemesi

Yıl 2024, Cilt: 12 Sayı: 3, 1279 - 1295, 31.07.2024

Serdar Arslan

https://doi.org/10.29130/dubited.1234168

Öz

Korona virüs salgını 2019 sonunda başladı ve tüm dünyayı etkisi altına aldı. Türkiyede aşılama süreci 2021 senesini başlarında başlatıldı ama çok yavaş ilerledi. Bu yüzden, bu süreçte Türk Hükümeti virüs yayılımını engellemek için çeşitli önlemler aldı. Bu çalışmada, bu önlemlerin virüs yayılımına olan etkisini anlamak için beş farklı tahminleme modeli (ARIMA, Prophet, NARNN, Yığıt LSTM ve çiftyönlü LSTM) gerçek dünya verileri ile kullanıldı ve karşılaştırıldı. Test sonuçları önlemlere göre parçalanan veri setinde küçük olanlar için ARIMA ve Prophet’in diğer modellere göre iyi sonuçlar verdiğini gösterdi. Veri setinin büyüklüğü arttıkça derin öğrenme yöntemlerinin daha iyi sonuçlar ortaya koyduğu gözlemlendi. Fakat, önlemlere göre ayırmadan tüm veri setini tek bir seferde kullandığımızda bu modellerin performanslarının düştüğü gözlemlendi.

Anahtar Kelimeler

COVID-19 Tahminleme ARIMA Prophet NARNN Derin öğrenme LSTM

Kaynakça

  • [1] A. Rismanbaf, “Potential Treatments for COVID-19; a Narrative Literature Review,” Archives of academic emergency medicine, vol.8(1), 2020.
  • [2] I. Rahimi, F. Chen, and A. H. Gandomi, “A review on COVID-19 forecasting models,” Neural Comput. Appl., vol. 35, pp.23671–23681, 2023.
  • [3] I. Nesteruk, “Statistics based predictions of coronavirus 2019-nCoV spreading in mainland China,” medRxiv, 2020.
  • [4] C. Anastassopoulou, L. Russo, A. Tsakris, and C. Siettos, “Data-based analysis, modelling and forecasting of the COVID-19 outbreak,” PLoS One, vol. 15, no. 3, pp. 1–21, 2020.
  • [5] G. Giordano et al., “Modelling the COVID-19 epidemic and implementation of population-wide interventions in Italy,” Nat. Med., vol. 26, no. 6, pp. 855–860, 2020.
  • [6] S. Moein et al., “Inefficiency of SIR models in forecasting COVID-19 epidemic: a case study of Isfahan,” Sci. Rep., vol. 11, no. 1, p. 4725, 2021.
  • [7] İ. Kırbaş, A. Sözen, A. D. Tuncer, and F. Ş. Kazancıoğlu, “Comparative analysis and forecasting of COVID-19 cases in various European countries with ARIMA, NARNN and LSTM approaches,” Chaos, Solitons and Fractals, vol. 138, Sep. 2020.
  • [8] T. Dehesh, H. A. Mardani-Fard, and P. Dehesh, “Forecasting of COVID-19 Confirmed Cases in Different Countries with ARIMA Models,” medRxiv ,2020.
  • [9] 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.
  • [10] M. Yousaf, S. Zahir, M. Riaz, S. M. Hussain, and K. Shah, “Statistical analysis of forecasting COVID-19 for upcoming month in Pakistan,” Chaos, Solitons & Fractals, vol. 138, p. 109926, 2020.
  • [11] O. D. Ilie, R. O. Cojocariu, A. Ciobica, S. I. Timofte, I. Mavroudis, and B. Doroftei, “Forecasting the spreading of COVID-19 across nine countries from Europe, Asia, and the American continents using the arima models,” Microorganisms, vol. 8, no. 8, pp. 1–19, 2020.
  • [12] Z. Ceylan, “Estimation of COVID-19 prevalence in Italy, Spain, and France,” Sci. Total Environ., vol. 729, Aug. 2020.
  • [13] Facebook, “Prophet Web Page.” https://facebook.github.io/prophet/ (accessed Feb. 15, 2023).
  • [14] B. M. Ndiaye, L. Tendeng, and D. Seck, “Analysis of the COVID-19 pandemic by SIR model and machine learning technics for forecasting,”, arXiv preprint arXiv:2004.01574, 2020.
  • [15] S. S. Helli, Ç. Demirci, O. Çoban, and A. Hamamci, “Short-Term Forecasting COVID-19 Cases in Turkey Using Long Short-Term Memory Network,” TIPTEKNO 2020 - Tip Teknol. Kongresi - 2020 Med. Technol. Congr. TIPTEKNO 2020, pp. 2–5, 2020.
  • [16] İ. Kırbaş, A. Sözen, A. D. Tuncer, and F. Ş. Kazancıoğlu, “Comparative analysis and forecasting of COVID-19 cases in various European countries with ARIMA, NARNN and LSTM approaches,” Chaos, Solitons and Fractals, vol. 138, 2020.
  • [17] S. Namasudra, S. Dhamodharavadhani, and R. Rathipriya, “Nonlinear Neural Network Based Forecasting Model for Predicting COVID-19 Cases,” Neural Process. Lett., vol.55, pp-171-191, 2023.
  • [18] A. I. Saba and A. H. Elsheikh, “Forecasting the prevalence of COVID-19 outbreak in Egypt using nonlinear autoregressive artificial neural networks,” Process Saf. Environ. Prot., vol. 141, pp. 1–8, 2020.
  • [19] W. Jiang and H. D. Schotten, “Deep Learning for Fading Channel Prediction,” IEEE Open J. Commun. Soc., vol. 1, pp. 320–332, 2020.
  • [20] A. Tomar and N. Gupta, “Prediction for the spread of COVID-19 in India and effectiveness of preventive measures,” Sci. Total Environ., vol. 728, p. 138762, 2020.
  • [21] V. K. R. Chimmula and L. Zhang, “Time series forecasting of COVID-19 transmission in Canada using LSTM networks,” Chaos, Solitons and Fractals, vol. 135, Jun. 2020.
  • [22] R. Pal, A. A. Sekh, S. Kar, and D. K. Prasad, “Neural Network Based Country Wise Risk Prediction of COVID-19", Applied Sciences, vol.10(18), 2020.
  • [23] A. J. Aljaaf, T. M. Mohsin, D. Al-Jumeily, and M. Alloghani, “A fusion of data science and feed-forward neural network-based modelling of COVID-19 outbreak forecasting in IRAQ,” J. Biomed. Inform., vol. 118, no.04, p. 103766, 2021.
  • [24] A. Dairi, F. Harrou, A. Zeroual, M. M. Hittawe, and Y. Sun, “Comparative study of machine learning methods for COVID-19 transmission forecasting,” J. Biomed. Inform., vol. 118, no. 04, p. 103791, 2021.
  • [25] S. Dutta and S. Kumar Bandyopadhyay, “Machine learning approach for confirmation of COVID-19 cases: positive, negative, death and release,” Iberoam. J. Med., vol. 03, pp. 172–177, 2020.
  • [26] S. Arslan, “A hybrid forecasting model using LSTM and Prophet for energy consumption with decomposition of time series data,”, PeerJ Computer Science, vol.8, 2022.
  • [27] S. Arslan, “Gated recurrent unit network-based fuzzy time series forecasting model,” Afyon Kocatepe Univ. J. Sci. Eng., vol. 23, no. 3, pp. 677–692, 2023.
  • [28] S. Tiwari, P. Chanak, and S. K. Singh, “A Review of the Machine Learning Algorithms for Covid-19 Case Analysis,” IEEE Trans. Artif. Intell., vol. 4, no. 1, pp. 44–59, 2023.
  • [29] F. Kamalov, K. Rajab, A. K. Cherukuri, A. Elnagar, and M. Safaraliev, “Deep learning for Covid-19 forecasting: State-of-the-art review.,” Neurocomputing, vol. 511, pp. 142–154, 2022.
  • [30] A. S. Ahmar and E. B. del Val, “SutteARIMA: Short-term forecasting method, a case: Covid-19 and stock market in Spain,” Sci. Total Environ., vol. 729, p. 138883, 2020.
  • [31] M. A. A. Al-qaness, A. A. Ewees, H. Fan, and M. Abd El Aziz, “Optimization Method for Forecasting Confirmed Cases of COVID-19 in China,” J. Clin. Med., vol. 9, no. 3, 2020.
  • [32] R. Salgotra, M. Gandomi, and A. H. Gandomi, “Time Series Analysis and Forecast of the COVID-19 Pandemic in India using Genetic Programming,” Chaos, Solitons & Fractals, vol. 138, p. 109945, 2020.
  • [33] S. Dil, N. Dil, and Z. H. Maken, “COVID-19 Trends and Forecast in the Eastern Mediterranean Region With a Particular Focus on Pakistan.,” Cureus, vol. 12, no. 6, p. e8582, Jun. 2020.
  • [34] Ministry of Health Web Page, https://covid19.saglik.gov.tr/ (accessed May 23, 2021).
  • [35] W. H. Organization, https://www.who.int/emergencies/diseases/novel-coronavirus-2019 (accessed Feb. 15, 2021).
  • [36] S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Comput., vol. 9, no. 8, pp. 1735–1780, Nov. 1997.
  • [37] A. Graves, A. Mohamed, and G. E. Hinton, “Speech Recognition with Deep Recurrent Neural Networks,” 2013 IEEE international conference on acoustics, speech and signal processing (pp. 6645-6649), 2013.
  • [38] M. Schuster and K. K. Paliwal, “Bidirectional recurrent neural networks,” IEEE Trans. Signal Process., vol. 45, no. 11, pp. 2673–2681, 1997.
  • [39] Z. Cui and Y. Wang, “Deep bidirectional and unidirectional LSTM recurrent neural network for network-wide traffic speed prediction,” arXiv preprint arXiv:1801.02143, 2018.
  • [40] J. G. De Gooijer and R. J. Hyndman, “25 years of time series forecasting,” Int. J. Forecast., vol. 22, no. 3, pp. 443–473, 2006.
  • [41] N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A Simple Way to Prevent Neural Networks from Overfitting,” J. Mach. Learn. Res., vol. 15, no. 56, pp. 1929–1958, 2014.
Toplam 41 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Serdar Arslan 0000-0003-3115-0741

Yayımlanma Tarihi 31 Temmuz 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 12 Sayı: 3

Kaynak Göster

APA Arslan, S. (2024). Forecasting of COVID-19 Cases Under Different Precaution Strategies in Turkey. Düzce Üniversitesi Bilim Ve Teknoloji Dergisi, 12(3), 1279-1295. https://doi.org/10.29130/dubited.1234168
AMA Arslan S. Forecasting of COVID-19 Cases Under Different Precaution Strategies in Turkey. DÜBİTED. Temmuz 2024;12(3):1279-1295. doi:10.29130/dubited.1234168
Chicago Arslan, Serdar. “Forecasting of COVID-19 Cases Under Different Precaution Strategies in Turkey”. Düzce Üniversitesi Bilim Ve Teknoloji Dergisi 12, sy. 3 (Temmuz 2024): 1279-95. https://doi.org/10.29130/dubited.1234168.
EndNote Arslan S (01 Temmuz 2024) Forecasting of COVID-19 Cases Under Different Precaution Strategies in Turkey. Düzce Üniversitesi Bilim ve Teknoloji Dergisi 12 3 1279–1295.
IEEE S. Arslan, “Forecasting of COVID-19 Cases Under Different Precaution Strategies in Turkey”, DÜBİTED, c. 12, sy. 3, ss. 1279–1295, 2024, doi: 10.29130/dubited.1234168.
ISNAD Arslan, Serdar. “Forecasting of COVID-19 Cases Under Different Precaution Strategies in Turkey”. Düzce Üniversitesi Bilim ve Teknoloji Dergisi 12/3 (Temmuz 2024), 1279-1295. https://doi.org/10.29130/dubited.1234168.
JAMA Arslan S. Forecasting of COVID-19 Cases Under Different Precaution Strategies in Turkey. DÜBİTED. 2024;12:1279–1295.
MLA Arslan, Serdar. “Forecasting of COVID-19 Cases Under Different Precaution Strategies in Turkey”. Düzce Üniversitesi Bilim Ve Teknoloji Dergisi, c. 12, sy. 3, 2024, ss. 1279-95, doi:10.29130/dubited.1234168.
Vancouver Arslan S. Forecasting of COVID-19 Cases Under Different Precaution Strategies in Turkey. DÜBİTED. 2024;12(3):1279-95.
 Kapak Resmi İndir