Modeling and prediction of the covid-19 cases with deep assessment methodology and fractional calculus

Ertu Rul Karacuha, Nisa Ozge Onal, Esra Ergun, Vasil Tabatadze, Hasan Alka, Kamil Karacuha, Haci Omer Tontu, Nguyen Vinh Ngocnu

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17 Atıf (Scopus)

Özet

This study focuses on modeling, prediction, and analysis of confirmed, recovered, and death cases of COVID-19 by using Fractional Calculus in comparison with other models for eight countries including China, France, Italy, Spain, Turkey, the UK, and the US. First, the dataset is modeled using our previously proposed approach Deep Assessment Methodology, next, one step prediction of the future is made using two methods: Deep Assessment Methodology and Long Short-Term Memory. Later, a Gaussian prediction model is proposed to predict the short-Term (30 Days) future of the pandemic, and prediction performance is evaluated. The proposed Gaussian model is compared to a time-dependent susceptibleinfected-recovered (SIR) model. Lastly, an analysis of understanding the effect of history is made on memory vectors using wavelet-based denoising and correlation coefficients. Results prove that Deep Assessment Methodology successfully models the dataset with 0.6671%, 0.6957%, and 0.5756% average errors for confirmed, recovered, and death cases, respectively. We found that using the proposed Gaussian approach underestimates the trend of the pandemic and the fastest increase is observed in the US while the slowest is observed in China and Spain. Analysis of the past showed that, for all countries except Turkey, the current time instant is mainly dependent on the past two weeks where countries like Germany, Italy, and the UK have a shorter average incubation period when compared to the US and France.

Orijinal dilİngilizce
Sayfa (başlangıç-bitiş)164012-164034
Sayfa sayısı23
DergiIEEE Access
Hacim8
DOI'lar
Yayın durumuYayınlandı - 2020

Bibliyografik not

Publisher Copyright:
© 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

Finansman

This work was supported by the Vodafone Future Laboratory, Istanbul Technical University (ITU), under Grant ITUVF20180901P11.

FinansörlerFinansör numarası
Vodafone Future Laboratory
Istanbul Teknik ÜniversitesiITUVF20180901P11

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