ABSTRACT
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.
ABSTRACT
In May 2021, over 160 million SARS-CoV-2 infections have been reported worldwide. Yet, the true amount of infections is unknown and believed to exceed the reported numbers by several fold, depending on national testing policies that can strongly affect the proportion of undetected cases. To overcome this testing bias and better assess SARS-CoV-2 transmission dynamics, we propose a genome-based computational pipeline, GInPipe, to reconstruct the SARS-CoV-2 incidence dynamics through time. After validating GInPipe against in silico generated outbreak data, as well as more complex phylodynamic analyses, we use the pipeline to reconstruct incidence histories in Denmark, Scotland, Switzerland, and Victoria (Australia) solely from viral sequence data. The proposed method robustly reconstructs the different pandemic waves in the investigated countries and regions, does not require phylodynamic reconstruction, and can be directly applied to publicly deposited SARS-CoV-2 sequencing data sets. We observe differences in the relative magnitude of reconstructed versus reported incidences during times with sparse availability of diagnostic tests. Using the reconstructed incidence dynamics, we assess how testing policies may have affected the probability to diagnose and report infected individuals. We find that under-reporting was highest in mid 2020 in all analysed countries, coinciding with liberal testing policies at times of low test 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 and evaluate testing policies. The method executes within minutes on very large data sets and is freely available as a fully automated pipeline from https://github.com/KleistLab/GInPipe.