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Statistical and network analysis of 1212 COVID-19 patients in Henan, China
BACKGROUND: COVID-19 is spreading quickly all over the world. Publicly released data for 1212 COVID-19 patients in Henan of China were analyzed in this paper. METHODS: Various statistical and network analysis methods were employed. RESULTS: We found that COVID-19 patients show gender (55% vs 45%) an...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Author(s). Published by Elsevier Ltd on behalf of International Society for Infectious Diseases.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7180361/ https://www.ncbi.nlm.nih.gov/pubmed/32339715 http://dx.doi.org/10.1016/j.ijid.2020.04.051 |
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author | Wang, Pei Lu, Jun-an Jin, Yanyu Zhu, Mengfan Wang, Lingling Chen, Shunjie |
author_facet | Wang, Pei Lu, Jun-an Jin, Yanyu Zhu, Mengfan Wang, Lingling Chen, Shunjie |
author_sort | Wang, Pei |
collection | PubMed |
description | BACKGROUND: COVID-19 is spreading quickly all over the world. Publicly released data for 1212 COVID-19 patients in Henan of China were analyzed in this paper. METHODS: Various statistical and network analysis methods were employed. RESULTS: We found that COVID-19 patients show gender (55% vs 45%) and age (81% aged between 21 and 60) preferences; possible causes were explored. The estimated average, mode and median incubation periods are 7.4, 4 and 7 days. Incubation periods of 92% of patients were no more than 14 days. The epidemic in Henan has undergone three stages and has shown high correlations with the numbers of patients recently returned from Wuhan. Network analysis revealed that 208 cases were clustering infected, and various People's Hospitals are the main force in treating COVID-19. CONCLUSIONS: The incubation period was statistically estimated, and the proposed state transition diagram can explore the epidemic stages of emerging infectious disease. We suggest that although the quarantine measures are gradually working, strong measures still might be needed for a period of time, since ∼7.45% of patients may have very long incubation periods. Migrant workers or college students are at high risk. State transition diagrams can help us to recognize the time-phased nature of the epidemic. Our investigations have implications for the prevention and control of COVID-19 in other regions of the world. |
format | Online Article Text |
id | pubmed-7180361 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | The Author(s). Published by Elsevier Ltd on behalf of International Society for Infectious Diseases. |
record_format | MEDLINE/PubMed |
spelling | pubmed-71803612020-04-24 Statistical and network analysis of 1212 COVID-19 patients in Henan, China Wang, Pei Lu, Jun-an Jin, Yanyu Zhu, Mengfan Wang, Lingling Chen, Shunjie Int J Infect Dis Article BACKGROUND: COVID-19 is spreading quickly all over the world. Publicly released data for 1212 COVID-19 patients in Henan of China were analyzed in this paper. METHODS: Various statistical and network analysis methods were employed. RESULTS: We found that COVID-19 patients show gender (55% vs 45%) and age (81% aged between 21 and 60) preferences; possible causes were explored. The estimated average, mode and median incubation periods are 7.4, 4 and 7 days. Incubation periods of 92% of patients were no more than 14 days. The epidemic in Henan has undergone three stages and has shown high correlations with the numbers of patients recently returned from Wuhan. Network analysis revealed that 208 cases were clustering infected, and various People's Hospitals are the main force in treating COVID-19. CONCLUSIONS: The incubation period was statistically estimated, and the proposed state transition diagram can explore the epidemic stages of emerging infectious disease. We suggest that although the quarantine measures are gradually working, strong measures still might be needed for a period of time, since ∼7.45% of patients may have very long incubation periods. Migrant workers or college students are at high risk. State transition diagrams can help us to recognize the time-phased nature of the epidemic. Our investigations have implications for the prevention and control of COVID-19 in other regions of the world. The Author(s). Published by Elsevier Ltd on behalf of International Society for Infectious Diseases. 2020-06 2020-04-24 /pmc/articles/PMC7180361/ /pubmed/32339715 http://dx.doi.org/10.1016/j.ijid.2020.04.051 Text en © 2020 The Author(s) 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, Pei Lu, Jun-an Jin, Yanyu Zhu, Mengfan Wang, Lingling Chen, Shunjie Statistical and network analysis of 1212 COVID-19 patients in Henan, China |
title | Statistical and network analysis of 1212 COVID-19 patients in Henan, China |
title_full | Statistical and network analysis of 1212 COVID-19 patients in Henan, China |
title_fullStr | Statistical and network analysis of 1212 COVID-19 patients in Henan, China |
title_full_unstemmed | Statistical and network analysis of 1212 COVID-19 patients in Henan, China |
title_short | Statistical and network analysis of 1212 COVID-19 patients in Henan, China |
title_sort | statistical and network analysis of 1212 covid-19 patients in henan, china |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7180361/ https://www.ncbi.nlm.nih.gov/pubmed/32339715 http://dx.doi.org/10.1016/j.ijid.2020.04.051 |
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