Science in Real-Time – Genomic Epidemiology During the SARS-CoV-2 Pandemic

Abstract

Modern sequencing technologies have enabled rapid decoding of the SARS-CoV-2 virus genome. These data allowed the monitoring of the virus evolution, which is crucial in understanding epidemiological dynamics and anticipating future spread. In response to the challenges of the pandemic, this thesis focuses on addressing the practical needs of public health through scientific advances in molecular epidemiology. Within Switzerland, the work contributed to the Swiss SARS-CoV-2 Sequencing Consortium (S3C) to sequence SARS-CoV-2 samples in Switzerland and used the sequences to quantify the spread of variants of concern. On an international scale, the thesis contributed two widely accessible web services, CoV-Spectrum and LAPIS, for the interactive exploration of globally-sourced sequencing data. The primary approach of the thesis involves the application of computer science, specifically data engineering techniques, to design efficient algorithms and software for processing genomic sequencing data. Given the extraordinary volume of viral sequences produced within a brief three-year period, these efficient methods were urgently needed to generate real-time insights of public health relevance. Although the infrastructure for genomic epidemiology has significantly advanced during the pandemic, it is not without shortcomings. Specifically, data-sharing infrastructure and international collaborations could be further strengthened. Drawing on the experiences from the projects of this thesis, six recommendations have been proposed for future improvement.