Riccardo Spott (Jena / DE), Mathias W. Pletz (Jena / DE), Carolin Fleischmann-Struzek (Jena / DE), Aurelia Kimmig (Jena / DE), Christiane Hadlich (Jena / DE), Mathias Hauert (Jena / DE), Mara Lohde (Jena / DE), Mateusz Jundzill (Jena / DE), Mike Marquet (Jena / DE), Petra Dickmann (Jena / DE), Ruben Schüchner (Bad Langensalza / DE), Martin Hölzer (Berlin / DE), Denise Kühnert (Berlin / DE; Jena / DE), Christian Brandt (Jena / DE)
Question
Efficient surveillance strategies are essential to keep track of the rapid developments during a pandemic. We investigated the potential of combining mobile service data and fine-granular metadata (such as postal codes and genomic data) to advance integrated genomic surveillance of the SARS-CoV-2 pandemic in the federal state of Thuringia, Germany.
Methods
We received anonymized and aggregated mobile service data from T-systems, containing circa 200 Mio data points from 2020 to 2021. Additionally, we sequenced over 6,500 SARS-CoV-2 Alpha genomes (B.1.1.7) across seven months within Thuringia while collecting patients' isolation dates and postal codes. Our dataset is complemented by over 66,000 publicly available German Alpha genomes. The dataset was screened for mutational clusters, and by combining the samples' time and location data with the mobile service data, the spread of identified clusters in Thuringia was analyzed.
Results
We could track the existence and spread of nine persistent mutation variants within the Alpha lineage. Seven formed separate phylogenetic clusters with different spreading patterns in Thuringia, of which two contained an additional sub-cluster, totaling nine Thuringian clusters. Our data suggests that mobile service data can indicate these clusters' spread and highlight a potential sampling bias, especially of low-prevalence variants. Thereby, mobile service data can be used either retrospectively to assess surveillance coverage and efficiency from already collected data or to actively guide part of a surveillance sampling process to districts where these variants are expected to emerge. The latter concept proved successful as we introduced a mobility-guided sampling strategy to track the low-prevalence Omicron sublineage BQ.1.1.
Conclusions
The combination of mobile service data and SARS-CoV-2 surveillance by genome sequencing is a valuable tool for more targeted and responsive surveillance.