tetano
Editor, Senior Moderator
Elife
. 2025 Jan 15:13:RP94045.
doi: 10.7554/eLife.94045. Leveraging mobility data to analyze persistent SARS-CoV-2 mutations and inform targeted genomic surveillance
Riccardo Spott[SUP] 1 [/SUP], Mathias W Pletz[SUP] 1 2 [/SUP], Carolin Fleischmann-Struzek[SUP] 1 2 [/SUP], Aurelia Kimmig[SUP] 1 [/SUP], Christiane Hadlich[SUP] 3 [/SUP], Matthias Hauert[SUP] 3 [/SUP], Mara Lohde[SUP] 1 [/SUP], Mateusz Jundzill[SUP] 1 [/SUP], Mike Marquet[SUP] 1 [/SUP], Petra Dickmann[SUP] 4 [/SUP], Ruben Schüchner[SUP] 5 [/SUP], Martin Hölzer[SUP] 6 [/SUP], Denise Kühnert[SUP] 7 8 [/SUP], Christian Brandt[SUP] 1 9 [/SUP]
Affiliations
Given the rapid cross-country spread of SARS-CoV-2 and the resulting difficulty in tracking lineage spread, 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 pandemic in the federal state of Thuringia, Germany. We sequenced over 6500 SARS-CoV-2 Alpha genomes (B.1.1.7) across 7 months within Thuringia while collecting patients' isolation dates and postal codes. Our dataset is complemented by over 66,000 publicly available German Alpha genomes and mobile service data for Thuringia. We identified the existence and spread of nine persistent mutation variants within the Alpha lineage, seven of which formed separate phylogenetic clusters with different spreading patterns in Thuringia. The remaining two are subclusters. 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 was successfully implemented as a proof-of-concept for a mobility-guided sampling strategy in response to the surveillance of Omicron sublineage BQ.1.1. The combination of mobile service data and SARS-CoV-2 surveillance by genome sequencing is a valuable tool for more targeted and responsive surveillance.
Keywords: SARS-CoV-2; WGS; cluster tracking; epidemiology; global health; mobility data; nanopore sequencing; viruses.
. 2025 Jan 15:13:RP94045.
doi: 10.7554/eLife.94045. Leveraging mobility data to analyze persistent SARS-CoV-2 mutations and inform targeted genomic surveillance
Riccardo Spott[SUP] 1 [/SUP], Mathias W Pletz[SUP] 1 2 [/SUP], Carolin Fleischmann-Struzek[SUP] 1 2 [/SUP], Aurelia Kimmig[SUP] 1 [/SUP], Christiane Hadlich[SUP] 3 [/SUP], Matthias Hauert[SUP] 3 [/SUP], Mara Lohde[SUP] 1 [/SUP], Mateusz Jundzill[SUP] 1 [/SUP], Mike Marquet[SUP] 1 [/SUP], Petra Dickmann[SUP] 4 [/SUP], Ruben Schüchner[SUP] 5 [/SUP], Martin Hölzer[SUP] 6 [/SUP], Denise Kühnert[SUP] 7 8 [/SUP], Christian Brandt[SUP] 1 9 [/SUP]
Affiliations
- PMID: 39812649
- PMCID: PMC11735024
- DOI: 10.7554/eLife.94045
Given the rapid cross-country spread of SARS-CoV-2 and the resulting difficulty in tracking lineage spread, 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 pandemic in the federal state of Thuringia, Germany. We sequenced over 6500 SARS-CoV-2 Alpha genomes (B.1.1.7) across 7 months within Thuringia while collecting patients' isolation dates and postal codes. Our dataset is complemented by over 66,000 publicly available German Alpha genomes and mobile service data for Thuringia. We identified the existence and spread of nine persistent mutation variants within the Alpha lineage, seven of which formed separate phylogenetic clusters with different spreading patterns in Thuringia. The remaining two are subclusters. 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 was successfully implemented as a proof-of-concept for a mobility-guided sampling strategy in response to the surveillance of Omicron sublineage BQ.1.1. The combination of mobile service data and SARS-CoV-2 surveillance by genome sequencing is a valuable tool for more targeted and responsive surveillance.
Keywords: SARS-CoV-2; WGS; cluster tracking; epidemiology; global health; mobility data; nanopore sequencing; viruses.