Modeling and Prediction of the Covid-19 Cases With Deep Assessment Methodology and Fractional Calculus


Karaçuha E., Onal N. Ö., Ergün E., Tabatadze V., Alkas H., Karaçuha K., ...Daha Fazla

IEEE ACCESS, cilt.8, ss.164012-164034, 2020 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 8
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1109/access.2020.3021952
  • Dergi Adı: IEEE ACCESS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC, Directory of Open Access Journals
  • Sayfa Sayıları: ss.164012-164034
  • Anahtar Kelimeler: Predictive models, Biological system modeling, Fractional calculus, Data models, Analytical models, Differential equations, COVID-19, deep assessment methodology (DAM), fractional calculus, least squares, long short-term memory, modeling, prediction of pandemics, SIR model, STATE-SPACE, DYNAMICS
  • İstanbul Teknik Üniversitesi Adresli: Evet

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 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.