ArticleApplied soft computing2020
Modelling and forecasting of COVID-19 spread using wavelet-coupled random vector functional link networks.
Article in Applied soft computing, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed.
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- Explainable artificial intelligence model for identifying COVID-19 gene biomarkers.Computers in biology and medicine · 2023Article
- Fruit-CoV: An efficient vision-based framework for speedy detection and diagnosis of SARS-CoV-2 infections through recorded cough sounds.Expert systems with applications · 2023Article
- A swarm-optimizer-assisted simulation and prediction model for emerging infectious diseases based on SEIR.Complex & intelligent systems · 2023Article
- Using discrete wavelet transform for optimizing COVID-19 new cases and deaths prediction worldwide with deep neural networks.PloS one · 2023Article
- Fuzzy-SIRD model: Forecasting COVID-19 death tolls considering governments intervention.Artificial intelligence in medicine · 2022Article
- Horizon scanning process to foresight emerging issues in Arabsphere's water vision.Scientific reports · 2022Article
- A data-driven hybrid ensemble AI model for COVID-19 infection forecast using multiple neural networks and reinforced learning.Computers in biology and medicine · 2022Article
- Artificial intelligence for forecasting and diagnosing COVID-19 pandemic: A focused review.Artificial intelligence in medicine · 2022Review
- Prediction and analysis of COVID-19 daily new cases and cumulative cases: times series forecasting and machine learning models.BMC infectious diseases · 2022Article
- Interval type-2 fuzzy computational model for real time Kalman filtering and forecasting of the dynamic spreading behavior of novel Coronavirus 2019.ISA transactions · 2022Article
- COVID-19 Diagnosis: A Review of Rapid Antigen, RT-PCR and Artificial Intelligence Methods.Bioengineering (Basel, Switzerland) · 2022Review
- COVID-19 Symptoms app analysis to foresee healthcare impacts: Evidence from Northern Ireland.Applied soft computing · 2022Article
- Identification of phytocompounds from Houttuynia cordata Thunb. as potential inhibitors for SARS-CoV-2 replication proteins through GC-MS/LC-MS characterization, molecular docking and molecular dynamics simulation.Molecular diversity · 2022Article
- DNA Vaccines Expressing the Envelope and Membrane Proteins Provide Partial Protection Against SARS-CoV-2 in Mice.Frontiers in immunology · 2022Article
- A novel interval type-2 fuzzy Kalman filtering and tracking of experimental data.Evolving systems · 2022Article
- A comparison of Covid-19 cases and deaths in Turkey and in other countries.Network modeling and analysis in health informatics and bioinformatics · 2022Article
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2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Researchers around the world are applying various prediction models for COVID-19 to make informed decisions and impose appropriate control measures. Because of a high degree of uncertainty and lack of necessary data, the traditional models showed low accuracy over the long term forecast. Although the literature contains several attempts to address this issue, there is a need to improve the essential prediction capability of existing models. Therefore, this study focuses on modelling and forecasting of COVID-19 spread in the top 5 worst-hit countries as per the reports on 10th July 2020. They are Brazil, India, Peru, Russia and the USA. For this purpose, the popular and powerful random vector functional link (RVFL) network is hybridized with 1-D discrete wavelet transform and a wavelet-coupled RVFL (WCRVFL) network is proposed. The prediction performance of the proposed model is compared with the state-of-the-art support vector regression (SVR) model and the conventional RVFL model. A 60 day ahead daily forecasting is also shown for the proposed model. Experimental results indicate the potential of the WCRVFL model for COVID-19 spread forecasting.
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