Cargando…
Prediction of epidemic trends in COVID-19 with logistic model and machine learning technics
COVID-19 has now had a huge impact in the world, and more than 8 million people in more than 100 countries are infected. To contain its spread, a number of countries published control measures. However, it’s not known when the epidemic will end in global and various countries. Predicting the trend o...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Ltd.
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7328553/ https://www.ncbi.nlm.nih.gov/pubmed/32834611 http://dx.doi.org/10.1016/j.chaos.2020.110058 |
_version_ | 1783552745598353408 |
---|---|
author | Wang, Peipei Zheng, Xinqi Li, Jiayang Zhu, Bangren |
author_facet | Wang, Peipei Zheng, Xinqi Li, Jiayang Zhu, Bangren |
author_sort | Wang, Peipei |
collection | PubMed |
description | COVID-19 has now had a huge impact in the world, and more than 8 million people in more than 100 countries are infected. To contain its spread, a number of countries published control measures. However, it’s not known when the epidemic will end in global and various countries. Predicting the trend of COVID-19 is an extremely important challenge. We integrate the most updated COVID-19 epidemiological data before June 16, 2020 into the Logistic model to fit the cap of epidemic trend, and then feed the cap value into FbProphet model, a machine learning based time series prediction model to derive the epidemic curve and predict the trend of the epidemic. Three significant points are summarized from our modeling results for global, Brazil, Russia, India, Peru and Indonesia. Under mathematical estimation, the global outbreak will peak in late October, with an estimated 14.12 million people infected cumulatively. |
format | Online Article Text |
id | pubmed-7328553 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-73285532020-07-01 Prediction of epidemic trends in COVID-19 with logistic model and machine learning technics Wang, Peipei Zheng, Xinqi Li, Jiayang Zhu, Bangren Chaos Solitons Fractals Article COVID-19 has now had a huge impact in the world, and more than 8 million people in more than 100 countries are infected. To contain its spread, a number of countries published control measures. However, it’s not known when the epidemic will end in global and various countries. Predicting the trend of COVID-19 is an extremely important challenge. We integrate the most updated COVID-19 epidemiological data before June 16, 2020 into the Logistic model to fit the cap of epidemic trend, and then feed the cap value into FbProphet model, a machine learning based time series prediction model to derive the epidemic curve and predict the trend of the epidemic. Three significant points are summarized from our modeling results for global, Brazil, Russia, India, Peru and Indonesia. Under mathematical estimation, the global outbreak will peak in late October, with an estimated 14.12 million people infected cumulatively. Elsevier Ltd. 2020-10 2020-07-01 /pmc/articles/PMC7328553/ /pubmed/32834611 http://dx.doi.org/10.1016/j.chaos.2020.110058 Text en © 2020 Elsevier Ltd. 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 Wang, Peipei Zheng, Xinqi Li, Jiayang Zhu, Bangren Prediction of epidemic trends in COVID-19 with logistic model and machine learning technics |
title | Prediction of epidemic trends in COVID-19 with logistic model and machine learning technics |
title_full | Prediction of epidemic trends in COVID-19 with logistic model and machine learning technics |
title_fullStr | Prediction of epidemic trends in COVID-19 with logistic model and machine learning technics |
title_full_unstemmed | Prediction of epidemic trends in COVID-19 with logistic model and machine learning technics |
title_short | Prediction of epidemic trends in COVID-19 with logistic model and machine learning technics |
title_sort | prediction of epidemic trends in covid-19 with logistic model and machine learning technics |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7328553/ https://www.ncbi.nlm.nih.gov/pubmed/32834611 http://dx.doi.org/10.1016/j.chaos.2020.110058 |
work_keys_str_mv | AT wangpeipei predictionofepidemictrendsincovid19withlogisticmodelandmachinelearningtechnics AT zhengxinqi predictionofepidemictrendsincovid19withlogisticmodelandmachinelearningtechnics AT lijiayang predictionofepidemictrendsincovid19withlogisticmodelandmachinelearningtechnics AT zhubangren predictionofepidemictrendsincovid19withlogisticmodelandmachinelearningtechnics |