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Going by the numbers : Learning and modeling COVID-19 disease dynamics
The COrona VIrus Disease (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) has resulted in a challenging number of infections and deaths worldwide. In order to combat the pandemic, several countries worldwide enforced mitigation measures in the forms of lo...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Ltd.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7369612/ https://www.ncbi.nlm.nih.gov/pubmed/32834585 http://dx.doi.org/10.1016/j.chaos.2020.110140 |
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author | Basu, Sayantani Campbell, Roy H. |
author_facet | Basu, Sayantani Campbell, Roy H. |
author_sort | Basu, Sayantani |
collection | PubMed |
description | The COrona VIrus Disease (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) has resulted in a challenging number of infections and deaths worldwide. In order to combat the pandemic, several countries worldwide enforced mitigation measures in the forms of lockdowns, social distancing, and disinfection measures. In an effort to understand the dynamics of this disease, we propose a Long Short-Term Memory (LSTM) based model. We train our model on more than four months of cumulative COVID-19 cases and deaths. Our model can be adjusted based on the parameters in order to provide predictions as needed. We provide results at both the country and county levels. We also perform a quantitative comparison of mitigation measures in various counties in the United States based on the rate of difference of a short and long window parameter of the proposed LSTM model. The analyses provided by our model can provide valuable insights based on the trends in the rate of infections and deaths. This can also be of help for countries and counties deciding on mitigation and reopening strategies. We believe that the results obtained from the proposed method will contribute to societal benefits for a current global concern. |
format | Online Article Text |
id | pubmed-7369612 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-73696122020-07-20 Going by the numbers : Learning and modeling COVID-19 disease dynamics Basu, Sayantani Campbell, Roy H. Chaos Solitons Fractals Article The COrona VIrus Disease (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) has resulted in a challenging number of infections and deaths worldwide. In order to combat the pandemic, several countries worldwide enforced mitigation measures in the forms of lockdowns, social distancing, and disinfection measures. In an effort to understand the dynamics of this disease, we propose a Long Short-Term Memory (LSTM) based model. We train our model on more than four months of cumulative COVID-19 cases and deaths. Our model can be adjusted based on the parameters in order to provide predictions as needed. We provide results at both the country and county levels. We also perform a quantitative comparison of mitigation measures in various counties in the United States based on the rate of difference of a short and long window parameter of the proposed LSTM model. The analyses provided by our model can provide valuable insights based on the trends in the rate of infections and deaths. This can also be of help for countries and counties deciding on mitigation and reopening strategies. We believe that the results obtained from the proposed method will contribute to societal benefits for a current global concern. Elsevier Ltd. 2020-09 2020-07-20 /pmc/articles/PMC7369612/ /pubmed/32834585 http://dx.doi.org/10.1016/j.chaos.2020.110140 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 Basu, Sayantani Campbell, Roy H. Going by the numbers : Learning and modeling COVID-19 disease dynamics |
title | Going by the numbers : Learning and modeling COVID-19 disease dynamics |
title_full | Going by the numbers : Learning and modeling COVID-19 disease dynamics |
title_fullStr | Going by the numbers : Learning and modeling COVID-19 disease dynamics |
title_full_unstemmed | Going by the numbers : Learning and modeling COVID-19 disease dynamics |
title_short | Going by the numbers : Learning and modeling COVID-19 disease dynamics |
title_sort | going by the numbers : learning and modeling covid-19 disease dynamics |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7369612/ https://www.ncbi.nlm.nih.gov/pubmed/32834585 http://dx.doi.org/10.1016/j.chaos.2020.110140 |
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