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

Year 2024, , 1279 - 1295, 31.07.2024

Serdar Arslan

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

Abstract

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.

Keywords

COVID-19, Forecasting, ARIMA, Prophet, NARNN, Deep learning, LSTM

Türkiye’de COVID-19 Vakalarının Farklı Önlemler Altında Tahminlemesi

Abstract

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.

Keywords

COVID-19, Tahminleme, ARIMA, Prophet, NARNN, Derin öğrenme, LSTM

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