ArticleChaos, solitons, and fractals2020
Evolutionary modelling of the COVID-19 pandemic in fifteen most affected countries.
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 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it, 50 citations in OpenAlex.
- Application of Artificial Intelligence-Based Regression Methods in the Problem of COVID-19 Spread Prediction: A Systematic Review.International journal of environmental research and public health · 2021Pooled it
- Analysis of learning curves in predictive modeling using exponential curve fitting with an asymptotic approach.PloS one · 2024Article
- Government Bonds and COVID-19. An International Evaluation Under Different Market States.Evaluation review · 2023Article
- Is It Possible to Predict COVID-19? Stochastic System Dynamic Model of Infection Spread in Kazakhstan.Healthcare (Basel, Switzerland) · 2023Article
- Modeling the impact of combined use of COVID Alert SA app and vaccination to curb COVID-19 infections in South Africa.PloS one · 2023Article
- Multiple change point estimation of trends in Covid-19 infections and deaths in India as compared with WHO regions.Spatial statistics · 2022Article
- Bioengineering of Novel Non-Replicating mRNA (NRM) and Self-Amplifying mRNA (SAM) Vaccine Candidates Against SARS-CoV-2 Using Immunoinformatics Approach.Molecular biotechnology · 2022Article
- COVID-19 and Chikungunya: an optimal control model with consideration of social and environmental factors.Journal of ambient intelligence and humanized computing · 2022Article
- Mathematical modeling and impact analysis of the use of COVID Alert SA app.AIMS public health · 2022Article
- How COVID-19 Pandemic Worsens the Economic Situation of Women in South Africa.The European journal of development research · 2022Article
- Machine learning techniques to predict different levels of hospital care of CoVid-19.Applied intelligence (Dordrecht, Netherlands) · 2022Article
- A death, infection, and recovery (DIR) model to forecast the COVID-19 spread.Computer methods and programs in biomedicine update · 2022Article
- Computational construction of a glycoprotein multi-epitope subunit vaccine candidate for old and new South-African SARS-CoV-2 virus strains.Informatics in medicine unlocked · 2022Article
- Predicting the trend of indicators related to Covid-19 using the combined MLP-MC model.Chaos, solitons, and fractals · 2021Article
- COVID-19 prediction based on genome similarity of human SARS-CoV-2 and bat SARS-CoV-like coronavirus.Computers & industrial engineering · 2021Article
- COVID-19 prediction models: a systematic literature review.Osong public health and research perspectives · 2021Article
- A review on COVID-19 forecasting models.Neural computing & applications · 2021Review
- Human activity pattern implications for modeling SARS-CoV-2 transmission.Computer methods and programs in biomedicine · 2021Article
- Genome-wide identification and prediction of SARS-CoV-2 mutations show an abundance of variants: Integrated study of bioinformatics and deep neural learning.Informatics in medicine unlocked · 2021Article
- Applications of artificial intelligence in battling against covid-19: A literature review.Chaos, solitons, and fractals · 2021Review
Corrections and comments
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Authors and funding
3 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
COVID-19 or SARS-Cov-2, affecting 6 million people and more than 300,000 deaths, the global pandemic has engulfed more than 90% countries of the world. The virus started from a single organism and is escalating at a rate of 3% to 5% daily and seems to be a never ending process. Understanding the basic dynamics and presenting new predictions models for evaluating the potential effect of the virus is highly crucial. In present work, an evolutionary data analytics method called as Genetic programming (GP) is used to mathematically model the potential effect of coronavirus in 15 most affected countries of the world. Two datasets namely confirmed cases (CC) and death cases (DC) were taken into consideration to estimate, how transmission varied in these countries between January 2020 and May 2020. Further, a percentage rise in the number of daily cases is also shown till 8 June 2020 and it is expected that Brazil will have the maximum rise in CC and USA have the most DC. Also, prediction of number of new CC and DC cases for every one million people in each of these countries is presented. The proposed model predicted that the transmission of COVID-19 in China is declining since late March 2020; in Singapore, France, Italy, Germany and Spain the curve has stagnated; in case of Canada, South Africa, Iran and Turkey the number of cases are rising slowly; whereas for USA, UK, Brazil, Russia and Mexico the rate of increase is very high and control measures need to be taken to stop the chains of transmission. Apart from that, the proposed prediction models are simple mathematical equations and future predictions can be drawn from these general equations. From the experimental results and statistical validation, it can be said that the proposed models use simple linkage functions and provide highly reliable results for time series prediction of COVID-19 in these countries.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.