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Modeling the complete spatiotemporal spread of the COVID-19 epidemic in mainland China
OBJECTIVES: The novel coronavirus (COVID-19) epidemic is reaching its final phase in China. The epidemic data are available for a complete assessment of epidemiological parameters in all regions and time periods. METHODS: This study aims to present a spatiotemporal epidemic model based on spatially...
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.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8056483/ https://www.ncbi.nlm.nih.gov/pubmed/33862212 http://dx.doi.org/10.1016/j.ijid.2021.04.021 |
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author | Hu, Bisong Ning, Pan Qiu, Jingyu Tao, Vincent Devlin, Adam Thomas Chen, Haiying Wang, Jinfeng Lin, Hui |
author_facet | Hu, Bisong Ning, Pan Qiu, Jingyu Tao, Vincent Devlin, Adam Thomas Chen, Haiying Wang, Jinfeng Lin, Hui |
author_sort | Hu, Bisong |
collection | PubMed |
description | OBJECTIVES: The novel coronavirus (COVID-19) epidemic is reaching its final phase in China. The epidemic data are available for a complete assessment of epidemiological parameters in all regions and time periods. METHODS: This study aims to present a spatiotemporal epidemic model based on spatially stratified heterogeneity (SSH) to simulate the epidemic spread. A susceptible-exposed/latent-infected-removed (SEIR) model was constructed for each SSH-identified stratum (each administrative city) to estimate the spatiotemporal epidemiological parameters of the outbreak. RESULTS: We estimated that the mean latent and removed periods were 5.40 and 2.13 days, respectively. There was an average of 1.72 latent or infected persons per 10,000 Wuhan travelers to other locations until January 20th, 2020. The space-time basic reproduction number (R(0)) estimates indicate an initial value between 2 and 3.5 in most cities on this date. The mean period for R(0) estimates to decrease to 80%, and 50% of initial values in cities were an average of 14.73 and 19.62 days, respectively. CONCLUSIONS: Our model estimates the complete spatiotemporal epidemiological characteristics of the outbreak in a space-time domain. These findings will help enhance a comprehensive understanding of the outbreak and inform the strategies of prevention and control in other countries worldwide. |
format | Online Article Text |
id | pubmed-8056483 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | The Author(s). Published by Elsevier Ltd on behalf of International Society for Infectious Diseases. |
record_format | MEDLINE/PubMed |
spelling | pubmed-80564832021-04-20 Modeling the complete spatiotemporal spread of the COVID-19 epidemic in mainland China Hu, Bisong Ning, Pan Qiu, Jingyu Tao, Vincent Devlin, Adam Thomas Chen, Haiying Wang, Jinfeng Lin, Hui Int J Infect Dis Article OBJECTIVES: The novel coronavirus (COVID-19) epidemic is reaching its final phase in China. The epidemic data are available for a complete assessment of epidemiological parameters in all regions and time periods. METHODS: This study aims to present a spatiotemporal epidemic model based on spatially stratified heterogeneity (SSH) to simulate the epidemic spread. A susceptible-exposed/latent-infected-removed (SEIR) model was constructed for each SSH-identified stratum (each administrative city) to estimate the spatiotemporal epidemiological parameters of the outbreak. RESULTS: We estimated that the mean latent and removed periods were 5.40 and 2.13 days, respectively. There was an average of 1.72 latent or infected persons per 10,000 Wuhan travelers to other locations until January 20th, 2020. The space-time basic reproduction number (R(0)) estimates indicate an initial value between 2 and 3.5 in most cities on this date. The mean period for R(0) estimates to decrease to 80%, and 50% of initial values in cities were an average of 14.73 and 19.62 days, respectively. CONCLUSIONS: Our model estimates the complete spatiotemporal epidemiological characteristics of the outbreak in a space-time domain. These findings will help enhance a comprehensive understanding of the outbreak and inform the strategies of prevention and control in other countries worldwide. The Author(s). Published by Elsevier Ltd on behalf of International Society for Infectious Diseases. 2021-09 2021-04-20 /pmc/articles/PMC8056483/ /pubmed/33862212 http://dx.doi.org/10.1016/j.ijid.2021.04.021 Text en © 2021 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 Hu, Bisong Ning, Pan Qiu, Jingyu Tao, Vincent Devlin, Adam Thomas Chen, Haiying Wang, Jinfeng Lin, Hui Modeling the complete spatiotemporal spread of the COVID-19 epidemic in mainland China |
title | Modeling the complete spatiotemporal spread of the COVID-19 epidemic in mainland China |
title_full | Modeling the complete spatiotemporal spread of the COVID-19 epidemic in mainland China |
title_fullStr | Modeling the complete spatiotemporal spread of the COVID-19 epidemic in mainland China |
title_full_unstemmed | Modeling the complete spatiotemporal spread of the COVID-19 epidemic in mainland China |
title_short | Modeling the complete spatiotemporal spread of the COVID-19 epidemic in mainland China |
title_sort | modeling the complete spatiotemporal spread of the covid-19 epidemic in mainland china |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8056483/ https://www.ncbi.nlm.nih.gov/pubmed/33862212 http://dx.doi.org/10.1016/j.ijid.2021.04.021 |
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