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

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.Spatial dynamics can obscure epidemic trends from surveillance data, biasing reproduction ratio estimates over long periods. A spectral correction reweights incidence data to remove this bias, thus improving monitoring to inform response strategies.