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Changing transmission dynamics of COVID-19 in China: a nationwide population-based piecewise mathematical modelling study
Jiawen Hou; Jie Hong; Boyun Ji; Bowen Dong; Yue Chen; Michael P Ward; Wei Tu; Zhen Jin; Jian Hu; Qing Su; Wenge Wang; Zheng Zhao; Shuang Xiao; Jiaqi Huang; Wei Lin; Zhijie Zhang.
Afiliação
  • Jiawen Hou; Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China; Centre for Computational Systems Biology and Rese
  • Jie Hong; Department of Epidemiology and Health Statistics, School of Public Health, Fudan University, Shanghai 200032, China; Key Laboratory of Public Health Safety, Min
  • Boyun Ji; School of Mathematical Sciences and Shanghai Center for Mathematical Sciences, Fudan University, Shanghai 200433, China
  • Bowen Dong; School of Mathematical Sciences and Shanghai Center for Mathematical Sciences, Fudan University, Shanghai 200433, China
  • Yue Chen; Department of Epidemiology and Community Medicine, Faculty of Medicine, University of Ottawa, 451 Smyth Rd, Ottawa, Ontario, Canada
  • Michael P Ward; Faculty of Veterinary Science, The University of Sydney NSW, Sydney, Australia
  • Wei Tu; Department of Geology and Geography, Georgia Southern University, Statesboro, GA 30460, USA
  • Zhen Jin; Complex Systems Research Center, Shanxi University, Taiyuan, Shan'xi 030006
  • Jian Hu; Department of Epidemiology and Health Statistics, School of Public Health, Fudan University, Shanghai 200032, China; Key Laboratory of Public Health Safety, Min
  • Qing Su; Department of Epidemiology and Health Statistics, School of Public Health, Fudan University, Shanghai 200032, China; Key Laboratory of Public Health Safety, Min
  • Wenge Wang; Department of Epidemiology and Health Statistics, School of Public Health, Fudan University, Shanghai 200032, China; Key Laboratory of Public Health Safety, Min
  • Zheng Zhao; Department of Epidemiology and Health Statistics, School of Public Health, Fudan University, Shanghai 200032, China; Key Laboratory of Public Health Safety, Min
  • Shuang Xiao; Department of Epidemiology and Health Statistics, School of Public Health, Fudan University, Shanghai 200032, China; Key Laboratory of Public Health Safety, Min
  • Jiaqi Huang; Department of Epidemiology and Health Statistics, School of Public Health, Fudan University, Shanghai 200032, China; Key Laboratory of Public Health Safety, Min
  • Wei Lin; School of Mathematical Sciences and Shanghai Center for Mathematical Sciences, Fudan University, Shanghai 200433, China
  • Zhijie Zhang; Department of Epidemiology and Health Statistics, School of Public Health, Fudan University, Shanghai 200032, China; Key Laboratory of Public Health Safety, Min
Preprint em Inglês | medRxiv | ID: ppmedrxiv-20045757
ABSTRACT
BackgroundThe first case of COVID-19 atypical pneumonia was reported in Wuhan, China on December 1, 2019. Since then, at least 33 other countries have been affected and there is a possibility of a global outbreak. A tremendous amount of effort has been made to understand its transmission dynamics; however, the temporal and spatial transmission heterogeneity and changing epidemiology have been mostly ignored. The epidemic mechanism of COVID-19 remains largely unclear. MethodsEpidemiological data on COVID-19 in China and daily population movement data from Wuhan to other cities were obtained and analyzed. To describe the transmission dynamics of COVID-19 at different spatio-temporal scales, we used a three-stage continuous-time Susceptible-Exposed-Infectious-Recovered (SEIR) meta-population model based on the characteristics and transmission dynamics of each stage 1) local epidemic from December 1, 2019 to January 9, 2020; 2) long-distance spread due to the Spring Festival travel rush from January 10 to 22, 2020; and 3) intra-provincial transmission from January 23, 2020 when travel restrictions were imposed. Together with the basic reproduction number (R0) for mathematical modelling, we also considered the variation in infectivity and introduced the controlled reproduction number (Rc) by assuming that exposed individuals to be infectious; we then simulated the future spread of COVID across Wuhan and all the provinces in mainland China. In addition, we built a novel source tracing algorithm to infer the initial exposed number of individuals in Wuhan on January 10, 2020, to estimate the number of infections early during this epidemic. FindingsThe spatial patterns of disease spread were heterogeneous. The estimated controlled reproduction number (Rc) in the neighboring provinces of Hubei province were relatively large, and the nationwide reproduction number - except for Hubei - ranged from 0.98 to 2.74 with an average of 1.79 (95% CI 1.77-1.80). Infectivity was significantly greater for exposed than infectious individuals, and exposed individuals were predicted to have become the major source of infection after January 23. For the epidemic process, most provinces reached their epidemic peak before February 10, 2020. It is expected that the maximum number of infections will be approached by the end of March. The final infectious size is estimated to be about 58,000 for Wuhan, 20,800 for the rest of Hubei province, and 17,000 for the other provinces in mainland China. Moreover, the estimated number of the exposed individuals is much greater than the officially reported number of infectious individuals in Wuhan on January 10, 2020. InterpretationThe transmission dynamics of COVID-19 have been changing over time and were heterogeneous across regions. There was a substantial underestimation of the number of exposed individuals in Wuhan early in the epidemic, and the Spring Festival travel rush played an important role in enhancing and accelerating the spread of COVID-19. However, Chinas unprecedented large-scale travel restrictions quickly reduced Rc. The next challenge for the control of COVID-19 will be the second great population movement brought by removing these travel restrictions.
Licença
cc_by_nc_nd
Texto completo: Disponível Coleções: Preprints Base de dados: medRxiv Tipo de estudo: Experimental_studies / Estudo observacional / Estudo prognóstico Idioma: Inglês Ano de publicação: 2020 Tipo de documento: Preprint
Texto completo: Disponível Coleções: Preprints Base de dados: medRxiv Tipo de estudo: Experimental_studies / Estudo observacional / Estudo prognóstico Idioma: Inglês Ano de publicação: 2020 Tipo de documento: Preprint
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