Cargando…
Time series analysis of the COVID-19 pandemic in Australia using genetic programming
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 3% to 5% 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 t...
Autores principales: | , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8137504/ http://dx.doi.org/10.1016/B978-0-12-824536-1.00036-8 |
_version_ | 1783695640650317824 |
---|---|
author | Salgotra, Rohit Gandomi, Amir H. |
author_facet | Salgotra, Rohit Gandomi, Amir H. |
author_sort | Salgotra, Rohit |
collection | PubMed |
description | 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 3% to 5% 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 analyzed, 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. |
format | Online Article Text |
id | pubmed-8137504 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
record_format | MEDLINE/PubMed |
spelling | pubmed-81375042021-05-21 Time series analysis of the COVID-19 pandemic in Australia using genetic programming Salgotra, Rohit Gandomi, Amir H. Data Science for COVID-19 Article 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 3% to 5% 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 analyzed, 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. 2021 2021-05-21 /pmc/articles/PMC8137504/ http://dx.doi.org/10.1016/B978-0-12-824536-1.00036-8 Text en Copyright © 2021 Elsevier Inc. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Salgotra, Rohit Gandomi, Amir H. Time series analysis of the COVID-19 pandemic in Australia using genetic programming |
title | Time series analysis of the COVID-19 pandemic in Australia using genetic programming |
title_full | Time series analysis of the COVID-19 pandemic in Australia using genetic programming |
title_fullStr | Time series analysis of the COVID-19 pandemic in Australia using genetic programming |
title_full_unstemmed | Time series analysis of the COVID-19 pandemic in Australia using genetic programming |
title_short | Time series analysis of the COVID-19 pandemic in Australia using genetic programming |
title_sort | time series analysis of the covid-19 pandemic in australia using genetic programming |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8137504/ http://dx.doi.org/10.1016/B978-0-12-824536-1.00036-8 |
work_keys_str_mv | AT salgotrarohit timeseriesanalysisofthecovid19pandemicinaustraliausinggeneticprogramming AT gandomiamirh timeseriesanalysisofthecovid19pandemicinaustraliausinggeneticprogramming |