Modeling and Prediction of the Covid-19 Cases With Deep Assessment Methodology and Fractional Calculus
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 pr...
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9186689/ |
| _version_ | 1818739992835391488 |
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| author | Ertugrul Karacuha Nisa Ozge Onal Esra Ergun Vasil Tabatadze Hasan Alkas Kamil Karacuha Haci Omer Tontus Nguyen Vinh Ngoc Nu |
| author_facet | Ertugrul Karacuha Nisa Ozge Onal Esra Ergun Vasil Tabatadze Hasan Alkas Kamil Karacuha Haci Omer Tontus Nguyen Vinh Ngoc Nu |
| author_sort | Ertugrul Karacuha |
| collection | DOAJ |
| description | 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 susceptible-infected-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. |
| first_indexed | 2024-12-18T01:33:39Z |
| format | Article |
| id | doaj.art-1e6bc6957989404d9d4d5f8133ab2e1d |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-18T01:33:39Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-1e6bc6957989404d9d4d5f8133ab2e1d2022-12-21T21:25:31ZengIEEEIEEE Access2169-35362020-01-01816401216403410.1109/ACCESS.2020.30219529186689Modeling and Prediction of the Covid-19 Cases With Deep Assessment Methodology and Fractional CalculusErtugrul Karacuha0Nisa Ozge Onal1https://orcid.org/0000-0002-6229-7132Esra Ergun2https://orcid.org/0000-0001-5000-8543Vasil Tabatadze3https://orcid.org/0000-0003-4350-3196Hasan Alkas4https://orcid.org/0000-0002-9416-3884Kamil Karacuha5https://orcid.org/0000-0002-0609-5085Haci Omer Tontus6Nguyen Vinh Ngoc Nu7https://orcid.org/0000-0002-4818-561XInformatics Institute, Istanbul Technical University, Istanbul, TurkeyInformatics Institute, Istanbul Technical University, Istanbul, TurkeyInformatics Institute, Istanbul Technical University, Istanbul, TurkeyInformatics Institute, Istanbul Technical University, Istanbul, TurkeyFaculty of Society and Economics, Rhine-Waal University of Applied Science, Kleve, GermanyInformatics Institute, Istanbul Technical University, Istanbul, TurkeyFaculty of Science and Letters, Istanbul Technical University, Istanbul, TurkeyFaculty of Society and Economics, Rhine-Waal University of Applied Science, Kleve, GermanyThis 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 susceptible-infected-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.https://ieeexplore.ieee.org/document/9186689/COVID-19deep assessment methodology (DAM)fractional calculusleast squareslong short-term memorymodeling |
| spellingShingle | Ertugrul Karacuha Nisa Ozge Onal Esra Ergun Vasil Tabatadze Hasan Alkas Kamil Karacuha Haci Omer Tontus Nguyen Vinh Ngoc Nu Modeling and Prediction of the Covid-19 Cases With Deep Assessment Methodology and Fractional Calculus IEEE Access COVID-19 deep assessment methodology (DAM) fractional calculus least squares long short-term memory modeling |
| title | Modeling and Prediction of the Covid-19 Cases With Deep Assessment Methodology and Fractional Calculus |
| title_full | Modeling and Prediction of the Covid-19 Cases With Deep Assessment Methodology and Fractional Calculus |
| title_fullStr | Modeling and Prediction of the Covid-19 Cases With Deep Assessment Methodology and Fractional Calculus |
| title_full_unstemmed | Modeling and Prediction of the Covid-19 Cases With Deep Assessment Methodology and Fractional Calculus |
| title_short | Modeling and Prediction of the Covid-19 Cases With Deep Assessment Methodology and Fractional Calculus |
| title_sort | modeling and prediction of the covid 19 cases with deep assessment methodology and fractional calculus |
| topic | COVID-19 deep assessment methodology (DAM) fractional calculus least squares long short-term memory modeling |
| url | https://ieeexplore.ieee.org/document/9186689/ |
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