Accurate estimates of the reproduction ratio are crucial for projecting the evolution of an infectious disease epidemic and for guiding the public health response. Here we prove that estimates of the reproduction ratio based on inference from surveillance data can be inaccurate if the population comprises spatially distinct communities, as the space–mobility interplay may hide the true evolution of the epidemic from surveillance data. Consequently, surveillance may underestimate the reproduction ratio over long periods, even mistaking growing epidemics as subsiding. To address this, we use the spectral properties of the matrix describing the spatial epidemic spread to reweight surveillance data. We propose a correction that removes the bias across all epidemic phases. We validate this correction against simulated epidemics and use COVID-19 as a case study. However, our results apply to any epidemic in which mobility is a driver of circulation. Our findings may help improve epidemic monitoring and surveillance and inform strategies for public health responses.

准确估计繁殖率对于预测传染病流行的演变和指导公共卫生应对措施至关重要。在这里，我们证明，如果人口由空间上不同的社区组成，那么基于监测数据推断的繁殖率估计可能不准确，因为空间与流动性的相互作用可能会从监测数据中隐藏流行病的真实演变。因此，长期监测可能会低估繁殖率，甚至误认为流行病正在消退。为了解决这个问题，我们使用描述空间流行病传播的矩阵的光谱特性来重新加权监测数据。我们提出了一项修正，以消除所有流行病阶段的偏差。我们针对模拟流行病验证了这一修正，并使用 COVID-19 作为案例研究。然而，我们的结果适用于任何流动性是流通驱动力的流行病。我们的研究结果可能有助于改善流行病监测和监测，并为公共卫生应对策略提供信息。