Leveraging mobility data to analyze persistent SARS-CoV-2 mutations and inform targeted genomic surveillance
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...
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eLife Sciences Publications Ltd
2025-01-01
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| Online Access: | https://elifesciences.org/articles/94045 |
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| author | Riccardo Spott Mathias W Pletz Carolin Fleischmann-Struzek Aurelia Kimmig Christiane Hadlich Matthias Hauert Mara Lohde Mateusz Jundzill Mike Marquet Petra Dickmann Ruben Schüchner Martin Hölzer Denise Kühnert Christian Brandt |
| author_facet | Riccardo Spott Mathias W Pletz Carolin Fleischmann-Struzek Aurelia Kimmig Christiane Hadlich Matthias Hauert Mara Lohde Mateusz Jundzill Mike Marquet Petra Dickmann Ruben Schüchner Martin Hölzer Denise Kühnert Christian Brandt |
| author_sort | Riccardo Spott |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-5521fa98bf7247b39d40d2c4ecffadb3 |
| institution | Kabale University |
| issn | 2050-084X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | eLife Sciences Publications Ltd |
| record_format | Article |
| series | eLife |
| spelling | doaj-art-5521fa98bf7247b39d40d2c4ecffadb32025-01-15T14:52:49ZengeLife Sciences Publications LtdeLife2050-084X2025-01-011310.7554/eLife.94045Leveraging mobility data to analyze persistent SARS-CoV-2 mutations and inform targeted genomic surveillanceRiccardo Spott0https://orcid.org/0000-0002-2103-167XMathias W Pletz1Carolin Fleischmann-Struzek2Aurelia Kimmig3Christiane Hadlich4Matthias Hauert5Mara Lohde6Mateusz Jundzill7Mike Marquet8Petra Dickmann9Ruben Schüchner10Martin Hölzer11Denise Kühnert12https://orcid.org/0000-0002-5657-018XChristian Brandt13Institute for Infectious Diseases and Infection Control, Jena University Hospital, Jena, GermanyInstitute for Infectious Diseases and Infection Control, Jena University Hospital, Jena, Germany; Center for Sepsis Control and Care, Jena University Hospital/Friedrich Schiller University Jena, Jena, GermanyInstitute for Infectious Diseases and Infection Control, Jena University Hospital, Jena, Germany; Center for Sepsis Control and Care, Jena University Hospital/Friedrich Schiller University Jena, Jena, GermanyInstitute for Infectious Diseases and Infection Control, Jena University Hospital, Jena, GermanySMA Development GmbH - epicinsights Agentur für Künstliche Intelligenz und Big Data Analytics, Jena, GermanySMA Development GmbH - epicinsights Agentur für Künstliche Intelligenz und Big Data Analytics, Jena, GermanyInstitute for Infectious Diseases and Infection Control, Jena University Hospital, Jena, GermanyInstitute for Infectious Diseases and Infection Control, Jena University Hospital, Jena, GermanyInstitute for Infectious Diseases and Infection Control, Jena University Hospital, Jena, GermanyDepartment of Anaesthesiology and Intensive Care, Jena University Hospital, Jena, GermanyThuringian State Authority for Consumer Protection, Department Health Protection, Bad Langensalza, GermanyMethodology and Research Infrastructure, Genome Competence Center (MF1), Robert Koch Institute, Berlin, GermanyCentre for Artificial Intelligence in Public Health Research, Robert Koch Institute, Berlin, Germany; Transmission, Infection, Diversification and Evolution Group, Max Planck Institute for Geoanthropology, Jena, GermanyInstitute for Infectious Diseases and Infection Control, Jena University Hospital, Jena, Germany; Center for Applied Research, InfectoGnostics Research Campus Jena, Jena, GermanyGiven 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.https://elifesciences.org/articles/94045nanopore sequencingSARS-CoV-2WGSmobility datacluster tracking |
| spellingShingle | Riccardo Spott Mathias W Pletz Carolin Fleischmann-Struzek Aurelia Kimmig Christiane Hadlich Matthias Hauert Mara Lohde Mateusz Jundzill Mike Marquet Petra Dickmann Ruben Schüchner Martin Hölzer Denise Kühnert Christian Brandt Leveraging mobility data to analyze persistent SARS-CoV-2 mutations and inform targeted genomic surveillance eLife nanopore sequencing SARS-CoV-2 WGS mobility data cluster tracking |
| title | Leveraging mobility data to analyze persistent SARS-CoV-2 mutations and inform targeted genomic surveillance |
| title_full | Leveraging mobility data to analyze persistent SARS-CoV-2 mutations and inform targeted genomic surveillance |
| title_fullStr | Leveraging mobility data to analyze persistent SARS-CoV-2 mutations and inform targeted genomic surveillance |
| title_full_unstemmed | Leveraging mobility data to analyze persistent SARS-CoV-2 mutations and inform targeted genomic surveillance |
| title_short | Leveraging mobility data to analyze persistent SARS-CoV-2 mutations and inform targeted genomic surveillance |
| title_sort | leveraging mobility data to analyze persistent sars cov 2 mutations and inform targeted genomic surveillance |
| topic | nanopore sequencing SARS-CoV-2 WGS mobility data cluster tracking |
| url | https://elifesciences.org/articles/94045 |
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