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A novel method to detect the early warning signal of COVID-19 transmission
BACKGROUND: Infectious illness outbreaks, particularly the corona-virus disease 2019 (COVID-19) pandemics in recent years, have wreaked havoc on human society, and the growing number of infected patients has put a strain on medical facilities. It’s necessary to forecast early warning signals of pote...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9289935/ https://www.ncbi.nlm.nih.gov/pubmed/35850664 http://dx.doi.org/10.1186/s12879-022-07603-z |
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author | Li, Mingzhang Ma, Shuo Liu, Zhengrong |
author_facet | Li, Mingzhang Ma, Shuo Liu, Zhengrong |
author_sort | Li, Mingzhang |
collection | PubMed |
description | BACKGROUND: Infectious illness outbreaks, particularly the corona-virus disease 2019 (COVID-19) pandemics in recent years, have wreaked havoc on human society, and the growing number of infected patients has put a strain on medical facilities. It’s necessary to forecast early warning signals of potential outbreaks of COVID-19, which would facilitate the health ministry to take some suitable control measures timely to prevent or slow the spread of COVID-19. However, since the intricacy of COVID-19 transmission, which connects biological and social systems, it is a difficult task to predict outbreaks of COVID-19 epidemics timely. RESULTS: In this work, we developed a new model-free approach, called, the landscape network entropy based on Auto-Reservoir Neural Network (ARNN-LNE), for quantitative analysis of COVID-19 propagation, by mining dynamic information from regional networks and short-term high-dimensional time-series data. Through this approach, we successfully identified the early warning signals in six nations or areas based on historical data of COVID-19 infections. CONCLUSION: Based on the newly published data on new COVID-19 disease, the ARNN-LNE method can give early warning signals for the outbreak of COVID-19. It’s worth noting that ARNN-LNE only relies on small samples data. Thus, it has great application potential for monitoring outbreaks of infectious diseases. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12879-022-07603-z. |
format | Online Article Text |
id | pubmed-9289935 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-92899352022-07-18 A novel method to detect the early warning signal of COVID-19 transmission Li, Mingzhang Ma, Shuo Liu, Zhengrong BMC Infect Dis Research BACKGROUND: Infectious illness outbreaks, particularly the corona-virus disease 2019 (COVID-19) pandemics in recent years, have wreaked havoc on human society, and the growing number of infected patients has put a strain on medical facilities. It’s necessary to forecast early warning signals of potential outbreaks of COVID-19, which would facilitate the health ministry to take some suitable control measures timely to prevent or slow the spread of COVID-19. However, since the intricacy of COVID-19 transmission, which connects biological and social systems, it is a difficult task to predict outbreaks of COVID-19 epidemics timely. RESULTS: In this work, we developed a new model-free approach, called, the landscape network entropy based on Auto-Reservoir Neural Network (ARNN-LNE), for quantitative analysis of COVID-19 propagation, by mining dynamic information from regional networks and short-term high-dimensional time-series data. Through this approach, we successfully identified the early warning signals in six nations or areas based on historical data of COVID-19 infections. CONCLUSION: Based on the newly published data on new COVID-19 disease, the ARNN-LNE method can give early warning signals for the outbreak of COVID-19. It’s worth noting that ARNN-LNE only relies on small samples data. Thus, it has great application potential for monitoring outbreaks of infectious diseases. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12879-022-07603-z. BioMed Central 2022-07-18 /pmc/articles/PMC9289935/ /pubmed/35850664 http://dx.doi.org/10.1186/s12879-022-07603-z Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Li, Mingzhang Ma, Shuo Liu, Zhengrong A novel method to detect the early warning signal of COVID-19 transmission |
title | A novel method to detect the early warning signal of COVID-19 transmission |
title_full | A novel method to detect the early warning signal of COVID-19 transmission |
title_fullStr | A novel method to detect the early warning signal of COVID-19 transmission |
title_full_unstemmed | A novel method to detect the early warning signal of COVID-19 transmission |
title_short | A novel method to detect the early warning signal of COVID-19 transmission |
title_sort | novel method to detect the early warning signal of covid-19 transmission |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9289935/ https://www.ncbi.nlm.nih.gov/pubmed/35850664 http://dx.doi.org/10.1186/s12879-022-07603-z |
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