İstatistiksel ve makine öğrenme yöntemleri ile COVID-19 salgın tahmini üzerine yapılan güncel çalışmaların incelemesi

Yıl 2022, Cilt: 11 Sayı: 3, 484 - 495, 18.07.2022

Umut Ahmet Çetin , Fatih Abut

https://doi.org/10.28948/ngumuh.1025095

Cited By: 1

Öz

COVID-19, ilk olarak Aralık 2019'da Çin'in Wuhan şehrinde ortaya çıkan bulaşıcı bir hastalıktır. O zaman beri COVID-19 dünya çapında 70 milyondan fazla insanı enfekte etmiştir ve 1 milyondan fazla ölüme neden olmuştur. Bu denli bulaşıcı ve ölümcül bir hastalıkla mücadele etmek için COVID-19 salgınını mümkün olduğunca doğru tahmin eden modellere ihtiyaç duyulmaktadır. Hükümetler tahmin modellerinin sonuçlarını kullanarak hastalıkla mücadele için bütçe ve tesis planlaması, ne kadar ilaç ve tıbbi ekipmanın üretileceğine veya ithal edileceği ve ne kadar tıbbi personele ihtiyaç duyulacağı hakkında daha iyi kararlar ve kontrol önlemleri alabilir. Sonuç olarak, çeşitli ülke ve kıtalarda COVID-19 salgınının zaman serileri veya denetimli tahmini için çeşitli regresyon ve sınıflandırma modelleri önerilmiştir. Bu makale, istatistiksel ve makine öğrenimi yöntemlerini kullanarak COVID-19 salgınını tahmin etmeye yönelik son çalışmalara genel bir bakış sunmayı amaçlamaktadır. Özellikle, her çalışma için, kullanılan veri kümesi özelliklerini, geliştirilen modellerin türünü, tahmin değişkenlerini, istatistiksel ve makine öğrenimi yöntemlerini, performans ölçümlerini ve son olarak ana sonucu ana hatlarıyla özetlenmiştir. Araştırma sonucu, makine öğrenme yöntemlerinin, COVID-19 salgını eğilimini tahmin etmek veya bir hastanın COVID-19 ile enfekte olup olmadığını tespit etmek gibi çeşitli ihtiyaçlar için tahminler yapmak için umut verici araçlar olduğunu ortaya koymaktadır.

Anahtar Kelimeler

Makine öğrenimi, sınıflandırma, regresyon, COVID-19, koronavirüs

A survey of recent studies on COVID-19 outbreak prediction using statistical and machine learning methods

Öz

COVID-19 is an infectious disease first discovered in Wuhan City, China, in December 2019. Ever since, COVID-19 has infected more than 70 million people and caused more than 1 million deaths worldwide. There is a need for models that predict the COVID-19 outbreak as accurately as possible to combat such an infectious and deadly disease. By using the results of the prediction models, governments can make better decisions and control measures about the disease, such as arranging budget and facility planning to combat the disease, deciding on how many medicines and medical equipment should be produced or imported, and how much medical staff is going to be needed. Consequently, various regression and classification models have been proposed for time series or supervised prediction of the COVID-19 outbreak in several countries and continents. This study aims to give an overview of recent studies on predicting the COVID-19 outbreak utilizing statistical and machine learning methods. Particularly, for each study, we outline the utilized ground-truth dataset characteristics, the type of the developed models, the predictor variables, the statistical and machine learning methods, the performance metrics, and finally, the major conclusion. The survey results reveal that machine learning methods are promising tools for making predictions for various needs, such as predicting whether a patient is infected with COVID-19 or not, predicting the trend of COVID-19 outbreaks, or predicting which age groups are most affected by COVID-19.

Anahtar Kelimeler

Machine learning, classification, regression, COVID-19, coronavirus

Kaynakça

• What is COVID-19, https://www.who.int/-emergencies /diseases/novel-coronavirus-2019/ques-tion-and-ans wers-hub/q-a-detail/coronavirus-disease-COVID-19, Accessed December 14, 2020.
• Worldometer COVID-19 Count, https://www.worldo-meters.¬info/¬coronavirus/?, Accessed December 14, 2020.
• WHO Director-General’s opening remarks at the media briefing on COVID-19 - March 11, 2020, https://www. who.¬int/¬¬ director-general/speeches /detail/ -who-director-general-s-opening-remarks-at-the-medi a-brie fing-on-COVID-19---11-march-2020, Accessed Dece mber 14, 2020.
• C. B. A. Satrio, W. Darmawan, B. U. Nadia and N. Hanafiah, Time series analysis and forecasting of coronavirus disease in Indonesia using ARIMA model and PROPHET, Procedia Computer Science, 179, 524-532, 2021. https://doi.org/10.1016/j.procs.2021.01.036
• A. Althnian, A. A. Elwafa, N. Aloboud, H. Alrasheed and H. Kurdi, Prediction of COVID-19 Individual Susceptibility using Demographic Data: A Case Study on Saudi Arabia, Procedia Computer Science, 177, 379-386, 2020. https://doi.org/-10.1016/j.procs.2020.1 0.051
• Z. Ceylan, Estimation of COVID-19 prevalence in Italy, Spain, and France, Science of The Total Environment, 729, 138817, 2020. https://doi.org/-10.10 16/j.¬scitotenv.2020.138817
• L. Fang, D. Wang and G. Pan, Analysis and Estimation of COVID-19 Spreading in Russia Based on ARIMA Model. SN Compr. Clin. Med. 2, 2521–2527, 2020. https://doi.org/10.1007/s42399-020-00555-y
• E. Fayyoumi, S. Idwan and H. AboShindi, Machine Learning and Statistical Modelling for Prediction of Novel COVID-19 Patients Case Study: Jordan. International Journal of Advanced Computer Science and Applications. 11(5), 122-126, 2020. https://doi.org. /10.14569/IJACSA.2020.0110518
• A. K. Gupta, V. Singh, P. Mathur and M. C. Travieso-Gonzalez, Prediction of COVID-19 pandemic measuring criteria using support vector machine, prophet and LinReg models in Indian scenario, Journal of Interdisciplinary Mathematics, 24(1), 89-108, 2020. doi:10.1080/09720502.2020.-1833458
• H. Önder, Short-term forecasts of the COVID-19 epidemic in Turkey: March 16–28, Black Sea Journal of Health Science, 3(2), 27-30, 2020.
• G. Pinter, I. Felde, A. Mosavi, P. Ghamisi and R. Gloaguen, COVID-19 Pandemic Prediction for Hungary; A Hybrid Machine Learning Approach. Mathematics, 8, 890, 2020.
• H. R. Pourghasemi, S. Pouyan, Z. F. N. Sadhasivam, B. Heidari, S. Babaei and J. P. Tiefenbacher, 2020. https://doi.org/10.1371/¬journal.pone.0236238
• R. Tamhane and S. Mulge, “Prediction of COVID-19 Outbreak using Machine Learning”. In International Research Journal of Engineering and Technology (IRJET), 7:5, 2020.
• Y. A. Gebretensae and D. Asmelash, Trend Analysis and Forecasting the Spread of COVID-19 Pandemic in Ethiopia Using Box–Jenkins Modeling Procedure. Int J Gen Med. 14, 1485-1498, 2021. https://doi.org/¬10.2147 ¬/¬IJGM.S306250
• M. Marzouk, N. Elshaboury, A. Abdel-Latif and S. Azab, Deep learning model for forecasting COVID-19 outbreak in Egypt, Process Safety and Environmental Protection, 153, 363-375, 2021. https://doi.org/10.10 16/j.psep.2021.07.034
• S. Z. Ahmed “Analysis and forecasting the outbreak of COVID-19 in Ethiopia using Machine learning”. European Journal of Computer Science and Information Technology, 8(4), 1-13, 2020.
• M. Djeddou, I. A. Hameed, A. Nejatian and I. Loukam, Predictive Modelling of COVID-19 New Cases in Algeria using An Extreme Learning Machines (ELM) medRxiv 2020.09.28.¬20203299. doi: https://doi.org /10.1101/2020.09.28.¬20203299
• A. I. Saba and A. H. Elsheikh, Forecasting the prevalence of COVID-19 outbreak in Egypt using nonlinear autoregressive artificial neural networks, Process Safety and Environmental Protection, 141, 1-8, 2020. https://doi.org/10.1016/j.psep.2020.05.029
• R. Takele, Stochastic modelling for predicting COVID-19 prevalence in East Africa Countries, Infectious Disease Modelling, 5, 598-607, 2020. https://doi.org /10.1016/j.idm.2020.08.005
• J. Luo, Z. Zhang, Y. Fu, F. Rao, Time series prediction of COVID-19 transmission in America using LSTM and XGBoost algorithms, Results in Physics, 27, 104462, 2021. https://doi.org/10.1016/j.rinp.2021.1044 62
• V. dos S. Santana et al., A Machine Learning Models for COVID-19 Detection in Brazil Based on Symptoms JMIR Preprints 25/01/2021:27293. doi: 10.2196/prep rints.27293
• M. Jojoa and B. Garcia-Zapirain, Forecasting COVID 19 Confirmed Cases Using Machine Learning: the Case of America. Preprints 2020, 2020090228. doi: 10.20944/preprints202009.0228.v1
• V. H. Moreau, Forecast predictions for the COVID-19 pandemic in Brazil by statistical modeling using the Weibull distribution for daily new cases and deaths. Braz J Microbiol, 51, 1109–1115, 2020. https://doi.org /10.1007/s42770-020-00331-z
• R. G. da Silva, M. H. D. M. Ribeiro, V. C. Mariani, L. dos S. Coelho, Forecasting Brazilian and American COVID-19 cases based on artificial intelligence coupled with climatic exogenous variables, Chaos, Solitons & Fractals, 139, 110027, 2020. https://doi .org/10.1016/¬j.chaos.2020.110027
• F. S. H. de Souza, N. S. Hojo-Souza, E. B. dos Santos, C. M. da Silva and D. L. Guidoni, Predicting the disease outcome in COVID-19 positive patients through machine learning: a retrospective cohort study with Brazilian data. medRxiv 2020.06.26.20140764. doi: https://doi.org/10.1101/2020.06.26.20140764
• S. Wollenstein-Betech, C. G. Cassandras, I. C. Paschalidis, Personalized Predictive Models for Symptomatic COVID-19 Patients Using Basic Preconditions: Hospitalizations, Mortality, and the Need for an ICU or Ventilator. medRxiv 2020.05.03.20089813. doi: https://doi.org/10.1101/¬2020.¬05.03.20089813
• N. Ayoobi et al., Time series forecasting of new cases and new deaths rate for COVID-19 using deep learning methods, Results in Physics, 27, 104495, 2021. https://doi.org/10.1016/-j.rinp.2021.104495
• Bala, Sagar, COVID-19 Outbreak Prediction Analysis using Machine Learning. International Journal for Research in Applied Science and Engineering Technology, 9, 1-7, 2021. doi:10.22214/¬ijraset.2021.3 2690
• A. Hassan, A. Qasem, W. Abdalla and O. Elhassan. Visualization, Prediction of COVID-19 Future Outbreak by Using Machine Learning. International Journal of Information Technology and Computer Science. 13, 16-32, 2021. doi: 10.5815/ijitcs.2021.03.0 2
• C. Yu, S. Chang, T. Chang, J. Wu, Y. Lin, H. Chien and R. A. Chen, COVID-19 Pandemic Artificial Intelligence–Based System with Deep Learning Forecasting and Automatic Statistical Data Acquisition: Development and Implementation Study J Med Internet Res, 23(5), e27806, 2021. https://www .jmir.org/2021/5/e27806 doi:10.2196/278 06
• S. F. Ardabili, A. Mosavi, P. Ghamisi, F. Ferdinand, A. R. Varkonyi-Koczy, U. Reuter, T. Rabczuk and P. M. Atkinson, “COVID-19 Outbreak Prediction with Machine Learning”. medRxiv 2020.04.17.20070094. doi: https://doi.org/-10.1101/2020.04.17.20070094
• K. B. Prakash, S. S. Imambi, M. Ismail, T. P. Kumar, Y. N. Pawan, “Analysis, Prediction and Evaluation of COVID-19 Datasets using Machine Learning Algorithms” in International Journal of Emerging Trends in Engineering Research, 8(5), 2020. doi: https://doi.org/10.30534/ijeter/2020/¬117852020
• N. S. Punn, S. K. Sonbhadra and S. Agarwal. “COVID-19 Epidemic Analysis using Machine Learning. and Deep Learning Algorithms”. medRxiv 2020.04.08. 20057679. doi: https://doi.org/¬10.1101/2020 .04.08 .20057679
• F. Rustam, A. A. Reshi, A. Mehmood, S. Ullah, B.-W. On, W. Aslam and G. S. Choi, “COVID-19 Future Forecasting Using Supervised Machine Learning Models”. in IEEE Access, 8, 101489-101499, 2020. doi: 10.1109/ACCESS.-2020.2997311
• M. Şahin, Forecasting COVID-19 cases based on mobility. MANAS Journal of Engineering, 8(2), 144-150, 2020. doi: 10.51354/mjen.769763
• S. Tuli, S. Tuli, R. Tuli, and S. S. Gill, “Predicting the Growth and Trend of COVID-19 Pandemic Using Machine Learning and Cloud Computing”. Internet of Things, 11, 100222, 2020. https://doi.org/10.1016/j.iot .2020.100222