4.8 Article

Characterizing the dynamics underlying global spread of epidemics

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NATURE COMMUNICATIONS
卷 9, 期 -, 页码 -

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NATURE PUBLISHING GROUP
DOI: 10.1038/s41467-017-02344-z

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资金

  1. National Institute of General Medical Sciences Models of Infectious Disease Agent Study (MIDAS) Informatics Services Group [1U24GM110707]
  2. Harvard Center for Communicable Disease Dynamics from the National Institute of General Medical Sciences MIDAS Initiative [U54GM088558]
  3. Area of Excellence Scheme of the Hong Kong University Grants Committee [AoE/M-12/06]
  4. Research Grants Council Collaborative Research Fund [CityU8/CRF/12G]
  5. Health and Medical Research Fund from the Government of the Hong Kong Special Administrative Region [HKS-15-E03, HKS-17-E13]
  6. NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES [U24GM110707, U54GM088558] Funding Source: NIH RePORTER

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Over the past few decades, global metapopulation epidemic simulations built with worldwide air-transportation data have been the main tool for studying how epidemics spread from the origin to other parts of the world (e.g., for pandemic influenza, SARS, and Ebola). However, it remains unclear how disease epidemiology and the air-transportation network structure determine epidemic arrivals for different populations around the globe. Here, we fill this knowledge gap by developing and validating an analytical framework that requires only basic analytics from stochastic processes. We apply this framework retrospectively to the 2009 influenza pandemic and 2014 Ebola epidemic to show that key epidemic parameters could be robustly estimated in real-time from public data on local and global spread at very low computational cost. Our framework not only elucidates the dynamics underlying global spread of epidemics but also advances our capability in nowcasting and forecasting epidemics.

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