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E-ISSN: 2667-6893
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Forecasting Model to Predict the Spreading of the COVID-19 Outbreak in Turkey

Türkiye'de COVID-19 Salgınının Yayılımını Öngörmek İçin Tahmin Modeli

How to cite: Bereketoğlu C, Özcan N, Kıran TR, Yola ML. Forecasting model to predict the spreading of the covid-19 outbreak in turkey. Akıllı Sistemler ve Uygulamaları Dergisi (Journal of Intelligent Systems with Applications) 2021; 4(2): 95-102. DOI: 10.54856/jiswa.202112165

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Title: Forecasting Model to Predict the Spreading of the COVID-19 Outbreak in Turkey

Abstract: This study aimed to forecast the future of the COVID-19 outbreak parameters such as spreading, case fatality, and case recovery values based on the publicly available epidemiological data for Turkey. We first performed different forecasting methods including Facebook's Prophet, ARIMA and Decision Tree. Based on the metrics of MAPE and MAE, Facebook's Prophet has the most effective forecasting model. Then, using Facebook's Prophet, we generated a forecast model for the evolution of the outbreak in Turkey fifteen-days-ahead. Based on the reported confirmed cases, the simulations suggest that the total number of infected people could reach 4328083 (with lower and upper bounds of 3854261 and 4888611, respectively) by April 23, 2021. Simulation forecast shows that death toll could reach 35656 with lower and upper bounds of 34806 and 36246, respectively. Besides, our findings suggest that although more than 86.38% growth in recovered cases might be possible, the future active cases will also significantly increase compared to the current active cases. This time series analysis indicates an increase trend of the COVID-19 outbreak in Turkey in the near future. Altogether, the present study highlights the importance of an efficient data-driven forecast model analysis for the simulation of the pandemic transmission and hence for further implementation of essential interventions for COVID-19 outbreak.

Keywords: COVID-19; forecasting, Facebook’s Prophet, Turkish population, intervention

Başlık: Türkiye'de COVID-19 Salgınının Yayılımını Öngörmek İçin Tahmin Modeli

Özet: Bu çalışma, Türkiye için kamuya açık epidemiyolojik verilere dayanarak COVID-19 salgını yayılımı, ölüm oranı ve iyileşme verileri gibi parametrelerinin geleceğini tahmin etmeyi amaçlamıştır. İlk olarak Facebook's Prophet, ARIMA ve Decision Tree gibi farklı tahmin yöntemlerini uyguladık. MAPE ve MAE ölçümlerine dayalı olarak, Facebook's Prophet en etkili tahmin modeli çıkmıştır. Daha sonra, Facebook's Prophet kullanarak, salgının Türkiye'deki gelişimi için on beş gün öncesinden bir tahmin modeli oluşturduk. Bildirilen doğrulanmış vakalara dayanarak, simülasyonlar, enfekte olmuş kişilerin toplam sayısının 23 Nisan 2021'e kadar 4328083'e (sırasıyla 3854261 ve 4888611 alt ve üst sınırlar ile) ulaşabileceğini göstermektedir. Simülasyon tahmini, ölü sayısının sırasıyla 34806 ve 36246 alt ve üst sınırlar olmak üzere 35656'ya ulaşabileceğini göstermektedir. Ayrıca bulgularımız, iyileşen vaka oranın %86.38'i aşabileceğini, ancak gelecekteki aktif vakaların mevcut aktif vakalara göre önemli ölçüde artabileceğini de göstermektedir. Bu zaman serisi analizi, yakın gelecekte Türkiye'deki COVID-19 salgınının artış eğiliminde olduğunu göstermektedir. Genel olarak bu çalışma, pandemik yayılmanın simülasyonu ve dolayısıyla COVID-19 salgını için temel müdahalelerin daha ileri düzeyde uygulanması için verimli bir veriye dayalı tahmin modeli analizinin önemini vurgulamaktadır.

Anahtar kelimeler: COVID-19; tahmin; Facebook's prophet; Türk popülasyonu; müdahale

  • Zhao S, Chen H. Modeling the epidemic dynamics and control of COVID-19 outbreak in China. Quantitative Biology 2020; 2020: 1-9.
  • Ibarra-Vega D. Lockdown, one, two, none, or smart. Modeling containing covid-19 infection. A conceptual model. Science of The Total Environment 2020; 730: 138917.
  • Liu Y, Gayle AA, Wilder-Smith A, Rocklov J. The reproductive number of COVID-19 is higher compared to SARS coronavirus. Journal of Travel Medicine 2020; 27(2): taaa021.
  • Song P, Karako T. COVID-19: Real-time dissemination of scientific information to fight a public health emergency of international concern. Bioscience Trends 2020; 14(1): 1-2.
  • Patel A, Jernigan DB, 2019-nCoV CDC Response Team. Initial public health response and interim clinical guidance for the 2019 novel coronavirus outbreak—United States, December 31, 2019–February 4, 2020. Morbidity and Mortality Weekly Report 2020; 69(5): 140-146.
  • Chen N, Zhou M, Dong X, Qu J, Gong F, Han Y, Qiu Y, Wang J, Liu Y, Wei Y, Xia J, Yu T, Zhang X, Zhang L. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: A descriptive study. The Lancet 2020; 395(10223): 507-513.
  • Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, Zhang L, Fan G, Xu J, Gu X, Cheng Z, Yu T, Xia J, Wei Y, Wu W, Xie X, Yin W, Li H, Liu M, Xiao Y, Gao H, Guo L, Xie J, Wang G, Jiang R, Gao Z, Jin Q, Wang J, Cao B. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. The Lancet 2020; 395(10223): 497-506.
  • Tu YF, Chien CS, Yarmishyn AA, Lin YY, Luo YH, Lin YT, Lai WY, Yang DM, Chou SJ, Yang YP, Wang ML, Chiou SH. A review of SARS-CoV-2 and the ongoing clinical trials. International Journal of Molecular Sciences. 2020; 21(7): 2657.
  • Zhu W, Li X, Wu Y, Xu C, Li L, Yang J, Fang S. Community quarantine strategy against coronavirus disease 2019 in Anhui: An evaluation based on trauma center patients. International Journal of Infectious Diseases 2020; 96: 417-421.
  • Tobias A. Evaluation of the lockdowns for the SARS-CoV-2 epidemic in Italy and Spain after one month follow up. Science of The Total Environment 2020; 725: 138539.
  • Newton PN, Bond KC, 53 signatories from 20 countries. COVID-19 and risks to the supply and quality of tests, drugs, and vaccines. The Lancet Global Health 2020; 8(6): e754-e755.
  • Lazzerini M, Barbi E, Apicella A, Marchetti F, Cardinale F, Trobia G. Delayed access or provision of care in Italy resulting from fear of COVID-19. The Lancet Child & Adolescent Health 2020; 4(5): e10-e11.
  • Xu J, Cheng Y, Yuan X, Li WV, Zhang L. Trends and prediction in daily incidence of novel coronavirus infection in China, Hubei Province and Wuhan City: An application of Farr's law. American Journal of Translational Research 2020; 12(4): 1355-1361.
  • Wu JT, Leung K, Leung GM. Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study. The Lancet 2020; 395(10225): 689-697.
  • Chakraborty T, Ghosh I. Real-time forecasts and risk assessment of novel coronavirus (COVID-19) cases: A data-driven analysis. Chaos, Solitons & Fractals 2020; 135: 109850.
  • Dong E, Du H, Gardner L. An interactive web-based dashboard to track COVID-19 in real time. The Lancet Infectious Diseases 2020; 20(5): 533-534.
  • Taylor SJ, Letham B. Forecasting at scale. The American Statistician 2018; 72: 37-45.
  • Liu H, Cocea M. Induction of classification rules by gini-index based rule generation. Information Sciences 2018; 436: 227-246.
  • Box GE, Jenkins GM, Reinsel GC, Ljung GM. Time Series Analysis: Forecasting and Control. John Wiley & Sons, 2015.
  • Shadab A, Said S, Ahmad S. Box–Jenkins multiplicative ARIMA modeling for prediction of solar radiation: A case study. International Journal of Energy and Water Resources 2019; 3: 305-318.
  • Roda WC, Varughese MB, Han D, Li MY. Why is it difficult to accurately predict the COVID-19 epidemic? Infectious Disease Modelling 2020; 5: 271-281.
  • Norman J, Bar-Yam Y, Taleb NN. Systemic Risk of Pandemic via Novel Pathogens—Coronavirus: A Note. New England Complex Systems Institute (January 26, 2020), 2020.
  • Petropoulos F, Makridakis S. Forecasting the novel coronavirus COVID-19. PloS one 2020; 15: e0231236.
  • Wu X, Nethery RC, Sabath MB, Braun D, Dominici F. Air pollution and COVID-19 mortality in the United States: Strengths and limitations of an ecological regression analysis. Science Advances 2020; 6(45): eabd4049.
  • Martelletti L, Martelletti P. Air pollution and the novel Covid-19 disease: A putative disease risk factor. SN Comprehensive Clinical Medicine 2020; 2: 383-387.