Investigation of Covid 19 Cases in Sivas Province with Time Series Analysis

Esra Akaydın Gültürk; Emek Güldoğan

doi:10.7197/cmj.1807704

EN TR

Investigation of Covid 19 Cases in Sivas Province with Time Series Analysis

Abstract

Objective: The objective of this study is to compare the prediction performance of classical statistical, machine learning, and deep learning-based modeling approaches by performing time series analysis of monthly case numbers in Sivas province during the COVID-19 pandemic. Thus, the aim is to determine the most suitable model that can more accurately predict the future course of the pandemic. Method: The study used confirmed COVID-19 case data from March 1, 2020, to August 1, 2022. The data were obtained from the records of the Turkish Ministry of Health and the Sivas Provincial Health Directorate. The stationarity of the series was assessed using Augmented Dickey–Fuller (ADF) and KPSS tests, and a logarithmic transformation was applied to reduce variance imbalance. The data were divided into a 70% training set, a 10% validation set, and a 20% test set. Classical statistical SARIMAX, machine learning-based Prophet, and deep learning-based LSTM models were used in the analyses, and performance comparisons were made using RMSE and SMAPE metrics. Findings: According to the results, the LSTM model showed the lowest error rate with RMSE=1.975 and SMAPE=109.9%. The log-transformed SARIMAX model (RMSE=2.093; SMAPE=114.3%) ranked second, while the Prophet model (RMSE=5.290; SMAPE=116.6%) ranked third. The error rates of models built with raw data were significantly higher. Conclusion: While logarithmic transformation improved the performance of classical models, the LSTM model provided the highest accuracy for complex and non-linear time series such as COVID-19. This result demonstrates that deep learning approaches offer a powerful alternative for epidemiological forecasting and can significantly contribute to decision-making in local health planning and resource management.

Keywords

Sivas İlinde Covid 19 Vakalarının Zaman Serileri Analizi İle İncelenmesi

Abstract

Amaç: Bu çalışmanın amacı, COVID-19 pandemisi süresince Sivas iline ait aylık vaka sayılarının zaman serisi analizini gerçekleştirerek, klasik istatistiksel, makine öğrenmesi ve derin öğrenme tabanlı modelleme yaklaşımlarının tahmin performanslarını karşılaştırmaktır. Böylece, salgının gelecekteki seyrini daha doğru öngörebilecek en uygun modelin belirlenmesi hedeflenmiştir. Yöntem: Çalışmada 1 Mart 2020 – 1 Ağustos 2022 tarihleri arasındaki doğrulanmış COVID-19 vaka verileri kullanılmıştır. Veriler, T.C. Sağlık Bakanlığı ve Sivas İl Sağlık Müdürlüğü kayıtlarından elde edilmiştir. Serinin durağanlığı Augmented Dickey Fuller (ADF) ve KPSS testleriyle değerlendirilmiş, varyans dengesizliğini azaltmak amacıyla logaritmik dönüşüm uygulanmıştır. Veriler %70 eğitim, %10 doğrulama ve %20 test kümesi olarak ayrılmıştır. Analizlerde klasik istatistiksel SARIMAX, makine öğrenmesi tabanlı Prophet ve derin öğrenme temelli LSTM modelleri kullanılmış, performans karşılaştırmaları RMSE ve SMAPE ölçütleriyle yapılmıştır. Bulgular: Sonuçlara göre, LSTM modeli RMSE=1.975 ve SMAPE=%109.9 ile en düşük hata oranını göstermiştir. Log-dönüştürülmüş SARIMAX modeli (RMSE=2.093; SMAPE=%114.3) ikinci sırada, Prophet modeli (RMSE=5.290; SMAPE=%116.6) ise üçüncü sırada yer almıştır. Ham verilerle kurulan modellerin hata oranları belirgin biçimde daha yüksek bulunmuştur. Sonuç: Logaritmik dönüşüm, klasik modellerin performansını artırmakla birlikte, COVID-19 gibi karmaşık ve doğrusal olmayan zaman serilerinde en yüksek doğruluğu LSTM modeli sağlamıştır. Bu sonuç, derin öğrenme yaklaşımlarının epidemiyolojik öngörülerde güçlü bir alternatif sunduğunu ve yerel sağlık planlaması ile kaynak yönetiminde karar vericilere önemli katkı sağlayabileceğini göstermektedir.

Keywords

References

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Details

Primary Language

English

Subjects

Health Informatics and Information Systems, Health Counselling, Health Systems, Health and Community Services, Implementation Science and Evaluation, Health Services and Systems (Other)

Journal Section

Research Article

Authors

Esra Akaydın Gültürk ^*
0000-0003-0978-3091
Türkiye

Emek Güldoğan
0000-0002-5436-8164
Türkiye

Publication Date

December 31, 2025

Submission Date

October 20, 2025

Acceptance Date

December 22, 2025

Published in Issue

Year 2025 Volume: 47 Number: 4

DOI

https://doi.org/10.7197/cmj.1807704

IZ

https://izlik.org/JA52GK77RS

Cite

RIS / Bibtex

APA

Akaydın Gültürk, E., & Güldoğan, E. (2025). Investigation of Covid 19 Cases in Sivas Province with Time Series Analysis. Cumhuriyet Medical Journal, 47(4), 32-39. https://doi.org/10.7197/cmj.1807704

AMA

1.Akaydın Gültürk E, Güldoğan E. Investigation of Covid 19 Cases in Sivas Province with Time Series Analysis. CMJ. 2025;47(4):32-39. doi:10.7197/cmj.1807704

Chicago

Akaydın Gültürk, Esra, and Emek Güldoğan. 2025. “Investigation of Covid 19 Cases in Sivas Province With Time Series Analysis”. Cumhuriyet Medical Journal 47 (4): 32-39. https://doi.org/10.7197/cmj.1807704.

EndNote

Akaydın Gültürk E, Güldoğan E (December 1, 2025) Investigation of Covid 19 Cases in Sivas Province with Time Series Analysis. Cumhuriyet Medical Journal 47 4 32–39.

IEEE

[1]E. Akaydın Gültürk and E. Güldoğan, “Investigation of Covid 19 Cases in Sivas Province with Time Series Analysis”, CMJ, vol. 47, no. 4, pp. 32–39, Dec. 2025, doi: 10.7197/cmj.1807704.

ISNAD

Akaydın Gültürk, Esra - Güldoğan, Emek. “Investigation of Covid 19 Cases in Sivas Province With Time Series Analysis”. Cumhuriyet Medical Journal 47/4 (December 1, 2025): 32-39. https://doi.org/10.7197/cmj.1807704.

JAMA

1.Akaydın Gültürk E, Güldoğan E. Investigation of Covid 19 Cases in Sivas Province with Time Series Analysis. CMJ. 2025;47:32–39.

MLA

Akaydın Gültürk, Esra, and Emek Güldoğan. “Investigation of Covid 19 Cases in Sivas Province With Time Series Analysis”. Cumhuriyet Medical Journal, vol. 47, no. 4, Dec. 2025, pp. 32-39, doi:10.7197/cmj.1807704.

Vancouver

1.Esra Akaydın Gültürk, Emek Güldoğan. Investigation of Covid 19 Cases in Sivas Province with Time Series Analysis. CMJ. 2025 Dec. 1;47(4):32-9. doi:10.7197/cmj.1807704