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

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Author(s)

  • Choujun Zhan
  • Chi K. Tse
  • Yuxia Fu
  • Zhikang Lai
  • Haijun Zhang

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Detail(s)

Original languageEnglish
Article numbere0241171
Journal / PublicationPLoS ONE
Volume15
Issue number10
Online published27 Oct 2020
Publication statusPublished - 2020

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Abstract

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 mobileapp 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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Citation Format(s)

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.

In: PLoS ONE, Vol. 15, No. 10 , e0241171, 2020.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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