Reproduction Number and Spatial Connectivity Structure Estimation via Graph Sparsity-Promoting Penalized Functional

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.