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

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

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 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.

Suggested Citation

  • Choujun Zhan & Chi K Tse & Yuxia Fu & Zhikang Lai & Haijun Zhang, 2020. "Modeling and prediction of the 2019 coronavirus disease spreading in China incorporating human migration data," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-17, October.
    • Handle: RePEc:plo:pone00:0241171
    DOI: 10.1371/journal.pone.0241171
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    1. Shengjie Lai & Nick W. Ruktanonchai & Liangcai Zhou & Olivia Prosper & Wei Luo & Jessica R. Floyd & Amy Wesolowski & Mauricio Santillana & Chi Zhang & Xiangjun Du & Hongjie Yu & Andrew J. Tatem, 2020. "Effect of non-pharmaceutical interventions to contain COVID-19 in China," Nature, Nature, vol. 585(7825), pages 410-413, September.
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    1. Essam A. Rashed & Akimasa Hirata, 2021. "One-Year Lesson: Machine Learning Prediction of COVID-19 Positive Cases with Meteorological Data and Mobility Estimate in Japan," IJERPH, MDPI, vol. 18(11), pages 1-16, May.
    2. Zoungrana, Tibi Didier & Yerbanga, Antoine & Ouoba, Youmanli, 2022. "Socio-economic and environmental factors in the global spread of COVID-19 outbreak," Research in Economics, Elsevier, vol. 76(4), pages 325-344.
    3. Yun Qiu & Xi Chen & Wei Shi, 2020. "Impacts of social and economic factors on the transmission of coronavirus disease 2019 (COVID-19) in China," Journal of Population Economics, Springer;European Society for Population Economics, vol. 33(4), pages 1127-1172, October.
    4. Ghosh, Mousam & Ghosh, Swarnankur & Ghosh, Suman & Panda, Goutam Kumar & Saha, Pradip Kumar, 2021. "Dynamic model of infected population due to spreading of pandemic COVID-19 considering both intra and inter zone mobilization factors with rate of detection," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    5. Kebin Deng & Zhong Ding & Xu Liu, 2023. "Clan loyalty and COVID‐19 diffusion: Evidence from China," Health Economics, John Wiley & Sons, Ltd., vol. 32(4), pages 910-938, April.
    6. Yanting Zheng & Jinyuan Huang & Qiuyue Yin, 2021. "What Are the Reasons for the Different COVID-19 Situations in Different Cities of China? A Study from the Perspective of Population Migration," IJERPH, MDPI, vol. 18(6), pages 1-16, March.
    7. Klaus F. Zimmermann & Gokhan Karabulut & Mehmet Huseyin Bilgin & Asli Cansin Doker, 2020. "Inter‐country distancing, globalisation and the coronavirus pandemic," The World Economy, Wiley Blackwell, vol. 43(6), pages 1484-1498, June.
    8. Arshad, Sadia & Siddique, Imran & Nawaz, Fariha & Shaheen, Aqila & Khurshid, Hina, 2023. "Dynamics of a fractional order mathematical model for COVID-19 epidemic transmission," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    9. Sui Zhang & Minghao Wang & Zhao Yang & Baolei Zhang, 2021. "A Novel Predictor for Micro-Scale COVID-19 Risk Modeling: An Empirical Study from a Spatiotemporal Perspective," IJERPH, MDPI, vol. 18(24), pages 1-16, December.

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