Identifying the mechanisms by which diseases spread among populations is important for understanding and forecasting patterns of epidemics and pandemics. Estimating transmission coupling among populations is challenging because transmission events are difficult to observe in practice, and connectivity among populations is often obscured by local disease dynamics. We consider the common situation in which an epidemic is seeded in one population and later spreads to a second population. We present a method for estimating transmission coupling between the two populations, assuming they can be modelled as susceptible–infected–removed (SIR) systems. We show that the strength of coupling between the two populations can be estimated from the time taken for the disease to invade the second population. Confidence in the estimate is low if only a single invasion event has been observed, but is substantially improved if numerous independent invasion events are observed. Our analysis of this simplest, idealized scenario represents a first step toward developing and verifying methods for estimating epidemic coupling among populations in an ever-more-connected global human population.

中文翻译：

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估计从入侵时间到人群之间的流行病耦合

确定疾病在人群中传播的机制对于理解和预测流行病和大流行的模式非常重要。估计人群之间的传播耦合具有挑战性，因为传播事件在实践中很难观察到，而且人群之间的连通性往往被当地疾病动态所掩盖。我们考虑一种常见情况，即流行病在一个人群中传播，然后传播到第二个人群。我们提出了一种估计两个群体之间传播耦合的方法，假设它们可以建模为易感-感染-移除（SIR）系统。我们表明，两个群体之间的耦合强度可以根据疾病侵入第二个群体所需的时间来估计。如果仅观察到单个入侵事件，则估计的置信度较低，但如果观察到大量独立的入侵事件，则估计置信度会大大提高。我们对这种最简单、理想化场景的分析代表了开发和验证用于估计日益紧密的全球人口中人群之间流行病耦合的方法的第一步。