COVID-19'un Yayılım Modelini Tahmin Etmek İçin Hibrit GA-ConvLSTM Modeli: Şanghay İşbirliği Örgütü İçin Bir Vaka Çalışması

Year 2025, Volume: 7 Issue: 1, 74 - 89, 30.06.2025

Anıl Utku

https://doi.org/10.59940/jismar.1604942

Abstract

COVID-19 dünyanın hemen her yerine çok hızlı bir şekilde yayılarak birçok insanın ciddi semptomlar yaşamasına ve hayatını kaybetmesine neden olmuştur. Bu çalışmada, sağlık sistemleri üzerindeki yükü hafifletmek ve salgının dağılımını tahmin etmek için planlar yapılabilmesi amacıyla derin öğrenme yöntemleriyle COVID-19'un yayılım örüntüsünü belirlemek amaçlandı. Bu amaçla CNN ve LSTM modelleri kullanılarak geliştirilen hibrit derin öğrenme modelinin hiper parametreleri genetik algoritma ile optimize edilerek daha başarılı bir tahmin performansı elde edilmiştir. GA-ConvLSTM modeli, SCO üyesi ülkelerde salgının yayılımını belirlemek için XGBoost, SVM, CNN, MLP, LSTM ve ConvLSTM ile test edilmiştir. Çalışmada, WHO tarafından sunulan 2020/01/03 ile 2022/05/31 tarihleri arasındaki günlük COVID-19 vaka ve ölüm verileri kullanılmıştır. Deneyler, GA-ConvLSTM'nin tüm ülkeler için vaka tahmininde 0,9’un üzerinde R2 değerine sahip olduğunu göstermiştir. Deneyler, GA-ConvLSTM'nin ölüm tahmininde ülkelerin çoğunluğu için 0,9’un üzerinde R2'ye sahip olduğunu göstermiştir. Ayrıca, COVID-19'un SCO ülkeleri arasındaki yayılım örüntüsü, 5 ve 14 günlük kuluçka dönemleri kullanılarak oluşturulan akor diyagramlarıyla belirlenmiştir.

Keywords

Derin öğrenme , Sangay İşbirliği Örgütü , CNN , LSTM , Genetik algoritma , COVID-19

Hybrid GA-ConvLSTM Model for Predicting the Transmission Pattern of COVID-19: A Case Study for Shanghai Cooperation Organisation

Abstract

COVID-19 has spread very quickly to almost every part of the world, causing many people to experience severe symptoms and lose their lives. In this study, it is aimed to determine the transmission pattern of COVID-19 with deep learning methods so that plans can be made to alleviate the burden on healthcare systems and predict the distribution of the epidemic. For this purpose, the hyper-parameters of the hybrid deep learning model developed using CNN and LSTM models were optimized with genetic algorithm, and a more successful prediction performance was achieved. The GA-ConvLSTM model was tested with XGBoost, SVM, CNN, MLP, LSTM, and ConvLSTM to determine the spread of the epidemic in the member countries of SCO. The study used daily COVID-19 case and death data between 2020/01/03 and 2022/05/31 presented by WHO. Experiments showed that GA-ConvLSTM has over 0.9 R2 in case prediction for all countries. Experiments showed that GA-ConvLSTM has above 0.9 R2 for the majority of countries when it comes to death prediction. In addition, the transmission pattern of COVID-19 among the SCO countries was determined with the chord diagrams created using 5 and 14 days’ incubation periods.

Keywords

Deep learning , Shanghai Cooperation Organization , CNN , LSTM , Genetic algorithm , COVID-19

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