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Modeling and prediction of the 2019 coronavirus disease spreading in China incorporating human migration data

This study integrates the daily intercity migration data with the classic Susceptible-Exposed-Infected-Removed (SEIR) model to construct a new model suitable for describing the dynamics of epidemic spreading of Coronavirus Disease 2019 (COVID-19) in China. Daily intercity migration data for 367 citi...

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Autores principales: Zhan, Choujun, Tse, Chi K., Fu, Yuxia, Lai, Zhikang, Zhang, Haijun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7591076/
https://www.ncbi.nlm.nih.gov/pubmed/33108386
http://dx.doi.org/10.1371/journal.pone.0241171
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author Zhan, Choujun
Tse, Chi K.
Fu, Yuxia
Lai, Zhikang
Zhang, Haijun
author_facet Zhan, Choujun
Tse, Chi K.
Fu, Yuxia
Lai, Zhikang
Zhang, Haijun
author_sort Zhan, Choujun
collection PubMed
description This study integrates the daily intercity migration data with the classic Susceptible-Exposed-Infected-Removed (SEIR) model to construct a new model suitable for describing the dynamics of epidemic spreading of Coronavirus Disease 2019 (COVID-19) in China. Daily intercity migration data for 367 cities in China were collected from Baidu Migration, a mobile-app based human migration tracking data system. Early outbreak data of infected, recovered and death cases from official source (from January 24 to February 16, 2020) were used for model fitting. The set of model parameters obtained from best data fitting using a constrained nonlinear optimisation procedure was used for estimation of the dynamics of epidemic spreading in the following months. The work was completed on February 19, 2020. Our results showed that the number of infections in most cities in China would peak between mid February to early March 2020, with about 0.8%, less than 0.1% and less than 0.01% of the population eventually infected in Wuhan, Hubei Province and the rest of China, respectively. Moreover, for most cities outside and within Hubei Province (except Wuhan), the total number of infected individuals is expected to be less than 300 and 4000, respectively.
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spelling pubmed-75910762020-10-30 Modeling and prediction of the 2019 coronavirus disease spreading in China incorporating human migration data Zhan, Choujun Tse, Chi K. Fu, Yuxia Lai, Zhikang Zhang, Haijun PLoS One Research Article This study integrates the daily intercity migration data with the classic Susceptible-Exposed-Infected-Removed (SEIR) model to construct a new model suitable for describing the dynamics of epidemic spreading of Coronavirus Disease 2019 (COVID-19) in China. Daily intercity migration data for 367 cities in China were collected from Baidu Migration, a mobile-app based human migration tracking data system. Early outbreak data of infected, recovered and death cases from official source (from January 24 to February 16, 2020) were used for model fitting. The set of model parameters obtained from best data fitting using a constrained nonlinear optimisation procedure was used for estimation of the dynamics of epidemic spreading in the following months. The work was completed on February 19, 2020. Our results showed that the number of infections in most cities in China would peak between mid February to early March 2020, with about 0.8%, less than 0.1% and less than 0.01% of the population eventually infected in Wuhan, Hubei Province and the rest of China, respectively. Moreover, for most cities outside and within Hubei Province (except Wuhan), the total number of infected individuals is expected to be less than 300 and 4000, respectively. Public Library of Science 2020-10-27 /pmc/articles/PMC7591076/ /pubmed/33108386 http://dx.doi.org/10.1371/journal.pone.0241171 Text en © 2020 Zhan et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Zhan, Choujun
Tse, Chi K.
Fu, Yuxia
Lai, Zhikang
Zhang, Haijun
Modeling and prediction of the 2019 coronavirus disease spreading in China incorporating human migration data
title Modeling and prediction of the 2019 coronavirus disease spreading in China incorporating human migration data
title_full Modeling and prediction of the 2019 coronavirus disease spreading in China incorporating human migration data
title_fullStr Modeling and prediction of the 2019 coronavirus disease spreading in China incorporating human migration data
title_full_unstemmed Modeling and prediction of the 2019 coronavirus disease spreading in China incorporating human migration data
title_short Modeling and prediction of the 2019 coronavirus disease spreading in China incorporating human migration data
title_sort modeling and prediction of the 2019 coronavirus disease spreading in china incorporating human migration data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7591076/
https://www.ncbi.nlm.nih.gov/pubmed/33108386
http://dx.doi.org/10.1371/journal.pone.0241171
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