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.

中文翻译：

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描述流行病全球传播的潜在动力。

在过去的几十年中，利用全球航空运输数据构建的全球人群种群流行病模拟一直是研究流行病如何从起源向世界其他地区传播的主要工具（例如大流行性流感，SARS和埃博拉病毒）。但是，尚不清楚疾病的流行病学和航空运输网络的结构如何决定全球不同人群的流行病到达。在这里，我们通过开发和验证仅需要来自随机过程的基本分析的分析框架来填补这一知识空白。我们将该框架追溯应用于2009年的流感大流行和2014年的埃博拉疫情，以显示可以以非常低的计算成本从本地和全球传播的公共数据实时可靠地估算关键的流行参数。