WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, 7 Sassoon Road, Hong Kong Special Administrative Region, 999077, Pokfulam, China.
Nat Commun. 2018 Jan 15;9(1):218. doi: 10.1038/s41467-017-02344-z.
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.
在过去的几十年中，使用全球航空运输数据构建的全球复合种群传染病模拟一直是研究传染病如何从起源地传播到世界其他地区（例如，大流行性流感、SARS 和埃博拉）的主要工具。然而，目前尚不清楚疾病流行病学和航空运输网络结构如何确定全球不同人群的传染病到达情况。在这里，我们通过开发和验证一个仅需要随机过程基本分析的分析框架来填补这一知识空白。我们将该框架回溯应用于 2009 年流感大流行和 2014 年埃博拉疫情，结果表明，通过对本地和全球传播的公共数据进行基本的实时分析，可以以非常低的计算成本稳健地估计关键传染病参数。我们的框架不仅阐明了传染病全球传播的潜在动态，而且提高了我们对传染病进行实时预测的能力。
