Cargando…
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...
Autores principales: | , , , , |
---|---|
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 |
_version_ | 1783600922440499200 |
---|---|
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. |
format | Online Article Text |
id | pubmed-7591076 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT zhanchoujun modelingandpredictionofthe2019coronavirusdiseasespreadinginchinaincorporatinghumanmigrationdata AT tsechik modelingandpredictionofthe2019coronavirusdiseasespreadinginchinaincorporatinghumanmigrationdata AT fuyuxia modelingandpredictionofthe2019coronavirusdiseasespreadinginchinaincorporatinghumanmigrationdata AT laizhikang modelingandpredictionofthe2019coronavirusdiseasespreadinginchinaincorporatinghumanmigrationdata AT zhanghaijun modelingandpredictionofthe2019coronavirusdiseasespreadinginchinaincorporatinghumanmigrationdata |