ARIMA Modeli Kullanılarak Dünyadaki Maymun Çiçeği Vakalarının Tahmini

Dünyada Covid-19 salgını henüz bitmemişken maymun çiçeği salgını başladı. Maymun çiçeği virüsü 4 ayda 59'dan fazla ülkeye yayıldı. Bu yayılmayı etkin bir şekilde kontrol etmek için bilgisayar destekli tahmin modellerine ihtiyaç vardır. Zaman serisi modellerinin salgının etkisinin tahmin edilmesinde ve gerekli önlemlerin alınmasında etkili olduğu daha önceki salgınlarda görülmüştür. Bu çalışmada, dünyadaki maymun çiçeği vaka sayısını başarılı bir şekilde tahmin etmek için farklı Otomatik Regresif Entegre Hareketli Ortalama (ARIMA) modelleri geliştirilmiştir. Çalışmada 07 Mayıs-12 Temmuz 2022 tarihleri arasında teyit edilen günlük maymun çiçeği vakaları verileri kullanılmıştır. ARIMA modellerinin eğitiminde 07 Mayıs 2022-02 Temmuz verileri kullanılmıştır. Modellerin tahmin performansları 03 Temmuz-12 Temmuz 2022 verileri ile test edilmiştir. Test sonuçlarına göre en düşük RMSE=483, MAE=410 ve MAPE=4.82 değerine sahip ARIMA(2,2,1) modeli en başarılı model olarak belirlendi. Belirlenen ARIMA modelinin yaklaşık %5 civarında bir hata değeri ile gerçek değerlerle iyi bir uyum içinde olduğu tespit edilmiştir. İleriki 7 gün için maymun çiçeği vakalarının sayısı ARIMA(2,2,1) modeli kullanılarak tahmin edildi. Model, 19 Temmuz 2022 için maymun çiçeği vakalarının sayısını 15056 olarak tahmin ederken, gerçek vaka sayısının 15032 olması modelin başarısını kanıtlamaktadır. Bu çalışma ARIMA yöntemini kullanarak maymun çiçeği vakalarının sayısını tahmin eden ilk çalışmadır ve sonuçlar ARIMA modelinin maymun çiçeği vaka sayısını tahmin etmek için uygun bir yöntem olduğunu göstermektedir.

Anahtar Kelimeler:

Maymun çiçeği, Zaman serisi, ARIMA, Tahmin.

Forecasting of Monkeypox Cases in the World Using the ARIMA Model

While the Covid-19 epidemic in the world was not over yet, the monkeypox epidemic started. The monkeypox virus spread to more than 59 countries in 4 months. Computer-aided forecasting models are needed to effectively control this spread. It has been seen in previous outbreaks that time-series models are effective in estimating the impact of the epidemic and taking necessary precautions. In this study, different Automatic Regressive Integrated Moving Average (ARIMA) models were developed to successfully forecast the number of monkeypox cases in the World. Daily confirmed monkeypox cases data from 07 May-12 July 2022 were used in the study. 07 May 2022-02 July data were used in the training of ARIMA models. The prediction performances of the models were tested with the data of 03 July-12 July 2022. According to the test results, the ARIMA(2,2,1) model with the lowest RMSE=483, MAE=410, and MAPE=4.82 was determined as the most successful model. It has been determined that the determined ARIMA model is in good agreement with the real values with an average error value of around 5%. The number of monkeypox cases for the next 7-day was forecasted using ARIMA(2,2,1) model. While the model predicts the number of monkeypox cases to be 15056 for 19 July 2022, the actual number of cases is 15032 proves the model's success. This is the first study to estimate the number of monkeypox cases using the ARIMA method, and the results show that the ARIMA model is a convenient method for estimating the number of monkeypox cases.

Keywords:

Monkeypox, Time-series, ARIMA, Forecasting.,

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