近日，法国索邦大学的Eugenio Valdano及其研究团队取得一项新进展。经过不懈努力，他们发现从流行病监测中估计的基本传染数在空间结构化人口中可能存在偏差。相关研究成果已于2024年4月25日在国际知名学术期刊《自然—物理学》上发表。

该研究团队证实，如果人口由空间上不同的社区组成，基于监测数据推断的基本传染数估计可能是不准确的，因为空间流动性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，监测可能低估了长期的基本传染数，甚至可能将逐渐加剧的流行病错误判断为正在减弱。为了解决这个问题，研究人员利用描述空间流行病传播矩阵的频谱特性，对监测数据进行重新加权。

他们进一步提出了一种修正方法，成功消除了流行病各阶段的偏差。他们针对模拟流行病验证了这一修正，并以COVID-19作为案例研究。然而，该结果适用于任何流行病，其中流动性是循环的驱动因素。这项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。

据悉，准确估计基本传染数对于预测传染病流行的演变和指导公共卫生对策至关重要。

附：英文原文

Title: Estimates of the reproduction ratio from epidemic surveillance may be biased in spatially structured populations

Author: Birello, Piero, Re Fiorentin, Michele, Wang, Boxuan, Colizza, Vittoria, Valdano, Eugenio

Issue&Volume: 2024-04-25

Abstract: 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.

DOI: 10.1038/s41567-024-02471-7

Source: https://www.nature.com/articles/s41567-024-02471-7