By May 2021, over 160 million SARS-CoV-2 diagnoses have been reported worldwide. Yet, the true number of infections is unknown and believed to exceed the reported numbers by several fold. National testing policies, in particular, can strongly affect the proportion of undetected cases.
Here, we propose a novel method (GInPipe) that reconstructs SARS-CoV-2 incidence profiles within minutes, solely from publicly available, time-stamped viral genomes. We validated GInPipe against in silico generated outbreak data and elaborate phylodynamic analyses. We apply the method to reconstruct incidence histories from sequence data for Denmark, Scotland, Switzerland, and Victoria (Australia). GInPipe reconstructs the different pandemic waves robustly and remarkably accurate. We demonstrate how the method can be used to investigate the effects of changing testing policies on the probability to diagnose and report infected individuals. Specifically, we find that under-reporting was highest in mid 2020 in parts of Europe, coinciding with changes towards more liberal testing policies at times of low testing capacities.
Due to the increased use of real-time sequencing, it is envisaged that GInPipe can complement established surveillance tools to monitor the SARS-CoV-2 pandemic. We anticipate that the method is particularly useful in settings where diagnostic and reporting infrastructures are insufficient. In ‘post-pandemic’ times, when diagnostic efforts are decreased, GInPipe may facilitate the detection of hidden infection dynamics.