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Estimate the Trend of COVID-19 Outbreak in China: a Statistical and Inferential Analysis on Provincial-level Data
The ongoing COVID-19 epidemic spreads with strong transmission power in every part of China. Analyses of the trend is highly need when the Chinese government makes plans and policies on epidemic control. This paper provides an estimation process on the trend of COVID-19 outbreak using the provincial...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Published by Elsevier B.V.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8197403/ https://www.ncbi.nlm.nih.gov/pubmed/34149970 http://dx.doi.org/10.1016/j.procs.2021.04.092 |
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author | Li, Kun Zhang, Yangyang Wang, Chao |
author_facet | Li, Kun Zhang, Yangyang Wang, Chao |
author_sort | Li, Kun |
collection | PubMed |
description | The ongoing COVID-19 epidemic spreads with strong transmission power in every part of China. Analyses of the trend is highly need when the Chinese government makes plans and policies on epidemic control. This paper provides an estimation process on the trend of COVID-19 outbreak using the provincial-level data of the confirmed cases. On the basis of the previous studies, we introduce an effective and practical method to compute accurate basic reproduction numbers (R(0)s) in each province-level division of China. The statistical results show a non-stop downward trend of the R(0)s in China, and confirm that China has made significant progress on the epidemic control by lowering the provincial R(0)s from 10 or above to 3.21 or less. In the inferential analysis, we introduce an effective AR(n) model for the trend forecasting. The inferential results imply that the nationwide epidemic risk will fall to a safe level by the end of April in China, which matches the actual situation. The results provide more accurate method and information about COVID-19. |
format | Online Article Text |
id | pubmed-8197403 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Published by Elsevier B.V. |
record_format | MEDLINE/PubMed |
spelling | pubmed-81974032021-06-15 Estimate the Trend of COVID-19 Outbreak in China: a Statistical and Inferential Analysis on Provincial-level Data Li, Kun Zhang, Yangyang Wang, Chao Procedia Comput Sci Article The ongoing COVID-19 epidemic spreads with strong transmission power in every part of China. Analyses of the trend is highly need when the Chinese government makes plans and policies on epidemic control. This paper provides an estimation process on the trend of COVID-19 outbreak using the provincial-level data of the confirmed cases. On the basis of the previous studies, we introduce an effective and practical method to compute accurate basic reproduction numbers (R(0)s) in each province-level division of China. The statistical results show a non-stop downward trend of the R(0)s in China, and confirm that China has made significant progress on the epidemic control by lowering the provincial R(0)s from 10 or above to 3.21 or less. In the inferential analysis, we introduce an effective AR(n) model for the trend forecasting. The inferential results imply that the nationwide epidemic risk will fall to a safe level by the end of April in China, which matches the actual situation. The results provide more accurate method and information about COVID-19. Published by Elsevier B.V. 2021 2021-06-12 /pmc/articles/PMC8197403/ /pubmed/34149970 http://dx.doi.org/10.1016/j.procs.2021.04.092 Text en © 2021 Published by Elsevier B.V. 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 Li, Kun Zhang, Yangyang Wang, Chao Estimate the Trend of COVID-19 Outbreak in China: a Statistical and Inferential Analysis on Provincial-level Data |
title | Estimate the Trend of COVID-19 Outbreak in China: a Statistical and Inferential Analysis on Provincial-level Data |
title_full | Estimate the Trend of COVID-19 Outbreak in China: a Statistical and Inferential Analysis on Provincial-level Data |
title_fullStr | Estimate the Trend of COVID-19 Outbreak in China: a Statistical and Inferential Analysis on Provincial-level Data |
title_full_unstemmed | Estimate the Trend of COVID-19 Outbreak in China: a Statistical and Inferential Analysis on Provincial-level Data |
title_short | Estimate the Trend of COVID-19 Outbreak in China: a Statistical and Inferential Analysis on Provincial-level Data |
title_sort | estimate the trend of covid-19 outbreak in china: a statistical and inferential analysis on provincial-level data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8197403/ https://www.ncbi.nlm.nih.gov/pubmed/34149970 http://dx.doi.org/10.1016/j.procs.2021.04.092 |
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