Time series analysis of the COVID-19 pandemic in Australia using genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InCollection{Salgotra:2021:ds4COVID-19,

• author = "Rohit Salgotra and Amir H. Gandomi",
• title = "Time series analysis of the COVID-19 pandemic in {Australia} using genetic programming",
• booktitle = "Data Science for COVID-19",
• publisher = "Academic Press",
• year = "2021",
• editor = "Utku Kose and Deepak Gupta and Victor Hugo C. {de Albuquerque} and Ashish Khanna",
• chapter = "21",
• pages = "399--411",
• month = may # " 21",
• keywords = "genetic algorithms, genetic programming, GEP, Corona virus, COVID-19, Pandemic, SARS-CoV-2",
• isbn13 = "978-0-12-824536-1",
• URL = "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8137504/",
• URL = "https://www.sciencedirect.com/science/article/pii/B9780128245361000368",
• DOI = "doi:10.1016/B978-0-12-824536-1.00036-8",
• size = "13 pages",
• abstract = "COVID-19 has emerged as a global pandemic over the past four months and has impacted more than 180 countries of the world. With a global increase rate of 3percent to 5percent daily cases, the virus seems to be a never ending process and WHO reports that the virus may stay here forever. So it becomes necessary to analyze the possible impact of the virus globally and present predictions on how it will behave in future. In this chapter, time series forecasting of COVID-19 with respect to Australia has been analysed, and prediction models have been derived by using genetic programming. Two prediction models have been proposed, one each for confirmed cases and death cases. The results are validated and importance of prediction variables are presented and discussed. From the numerical results, it can be said that the proposed gene expression programming models are highly reliable and can be considered as standard for time series prediction for COVID-19 in Australia.",
• notes = "PMCID: PMC8137504",

}

Genetic Programming entries for Rohit Salgotra A H Gandomi

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