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An accurate and timely estimate of the reproduction ratio R of an infectiousdisease epidemic is crucial to make projections on its evolution and set up theappropriate public health response. Estimates of R routinely come fromstatistical inference on timelines of cases or their proxies like symptomaticcases, hospitalizatons, deaths. Here, however, we prove that these estimates ofR may not be accurate if the population is made up of spatially distinctcommunities, as the interplay between space and mobility may hide the trueepidemic evolution from surveillance data. This means that surveillance mayunderestimate R over long periods, to the point of mistaking a growing epidemicfor a subsiding one, misinforming public health response. To overcome this, wepropose a correction to be applied to surveillance data that removes this biasand ensures an accurate estimate of R across all epidemic phases. We useCOVID-19 as case study; our results, however, apply to any epidemic wheremobility is a driver of circulation, including major challenges of the nextdecades: respiratory infections (influenza, SARS-CoV-2, emerging pathogens),vector-borne diseases (arboviruses). Our findings will help set up publichealth response to these threats, by improving epidemic monitoring andsurveillance.

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