ArticleChaos, solitons, and fractals2020
Time series prediction of COVID-19 by mutation rate analysis using recurrent neural network-based LSTM model.
Article in Chaos, solitons, and fractals, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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46 citing papers in PubMed, 125 citations in OpenAlex.
- Global infectious disease early warning models: An updated review and lessons from the COVID-19 pandemic.Infectious Disease Modelling · 2025Review
- Paying attention to the SARS-CoV-2 dialect : a deep neural network approach to predicting novel protein mutations.Communications biology · 2025Article
- Analysis of SARS-CoV-2 genome evolutionary patterns.Microbiology spectrum · 2024Article
- COVID-19-Related Age Profiles for SARS-CoV-2 Variants in England and Wales and States of the USA (2020 to 2022): Impact on All-Cause Mortality.Infectious disease reports · 2023Article
- Early warning of emerging infectious diseases based on multimodal data.Biosafety and health · 2023Review
- SERS Signature of SARS-CoV-2 in Saliva and Nasopharyngeal Swabs: Towards Perspective COVID-19 Point-of-Care Diagnostics.International journal of molecular sciences · 2023Article
- Explainable artificial intelligence model for identifying COVID-19 gene biomarkers.Computers in biology and medicine · 2023Article
- Article
- Identification and classification of coronavirus genomic signals based on linear predictive coding and machine learning methods.Biomedical signal processing and control · 2023Article
- Monkeypox genome mutation analysis using a timeseries model based on long short-term memory.PloS one · 2023Article
- Outbreak prediction of COVID-19 using Recurrent neural network with Gated Recurrent Units.Materials today. Proceedings · 2023Article
- Phylogenomic and Structural Analysis of the Monkeypox Virus Shows Evolution towards Increased Stability.Viruses · 2022Article
- Prediction of SONeural processing letters · 2022Article
- The efficacy of deep learning based LSTM model in forecasting the outbreak of contagious diseases.Infectious Disease Modelling · 2022Article
- SARS-CoV-2 variant with mutations in N gene affecting detection by widely used PCR primers.Journal of medical virology · 2022Article
- CNN-LSTM deep learning based forecasting model for COVID-19 infection cases in Nigeria, South Africa and Botswana.Health and technology · 2022Article
- The Evaluation on the Credit Risk of Enterprises with the CNN-LSTM-ATT Model.Computational intelligence and neuroscience · 2022Article
- Rapid testing for coronavirus disease 2019 (COVID-19).MRS communications · 2022Review
- Merits and Limitations of Mathematical Modeling and Computational Simulations in Mitigation of COVID-19 Pandemic: A Comprehensive Review.Archives of computational methods in engineering : state of the art reviews · 2022Review
- A Review on Measures to Rejuvenate Immune System: Natural Mode of Protection Against Coronavirus Infection.Frontiers in immunology · 2022Review
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
SARS-CoV-2, a novel coronavirus mostly known as COVID-19 has created a global pandemic. The world is now immobilized by this infectious RNA virus. As of June 15, already more than 7.9 million people have been infected and 432k people died. This RNA virus has the ability to do the mutation in the human body. Accurate determination of mutation rates is essential to comprehend the evolution of this virus and to determine the risk of emergent infectious disease. This study explores the mutation rate of the whole genomic sequence gathered from the patient's dataset of different countries. The collected dataset is processed to determine the nucleotide mutation and codon mutation separately. Furthermore, based on the size of the dataset, the determined mutation rate is categorized for four different regions: China, Australia, the United States, and the rest of the World. It has been found that a huge amount of Thymine (T) and Adenine (A) are mutated to other nucleotides for all regions, but codons are not frequently mutating like nucleotides. A recurrent neural network-based Long Short Term Memory (LSTM) model has been applied to predict the future mutation rate of this virus. The LSTM model gives Root Mean Square Error (RMSE) of 0.06 in testing and 0.04 in training, which is an optimized value. Using this train and testing process, the nucleotide mutation rate of 400
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