During an epidemic outbreak, decision makers crucially need accurate and robust tools to monitor the pathogen propagation. The effective reproduction number, defined as the expected number of secondary infections stemming from one contaminated individual, is a state-of-the-art indicator quantifying the epidemic intensity. Numerous estimators have been developed to precisely track the reproduction number temporal evolution. Yet, COVID-19 pandemic surveillance raised unprecedented challenges due to the poor quality of worldwide reported infection counts. When monitoring the epidemic in different territories simultaneously, leveraging the spatial structure of data significantly enhances both the accuracy and robustness of reproduction number estimates. However, this requires a good estimate of the spatial structure. To tackle this major limitation, the present work proposes a joint estimator of the reproduction number and connectivity structure. The procedure is assessed through intensive numerical simulations on carefully designed synthetic data and illustrated on real COVID-19 spatiotemporal infection counts.

翻译：在流行病爆发期间，决策者迫切需要准确且稳健的工具来监测病原体传播。有效再生数——定义为单个感染者预期产生的二代感染数量——是量化疫情强度的先进指标。目前已开发出多种估计器来精确追踪再生数的时间演化。然而，由于全球报告的感染病例数据质量参差不齐，COVID-19大流行监测提出了前所未有的挑战。当同时监测不同区域的疫情时，利用数据的空间结构能显著提升再生数估计的准确性与稳健性。但这需要获得良好的空间结构估计。为克服这一主要局限，本研究提出了一种联合估计再生数与连接结构的方法。该程序通过精心设计的合成数据进行了大量数值模拟验证，并在真实的COVID-19时空感染病例数据上进行了实证分析。