SARS-CoV-2: Cell phone and genome data allow targeted tracing of infection pathways
Scientists from the Jena start-up nanozoo GmbH and Jena University Hospital present analysis
To track the spread of pathogens such as SARS-CoV-2 in a more targeted manner, anonymized mobile phone data and other metadata (such as zip codes) can be combined with genome data. A systematic evaluation of such combined data pools has now been presented by scientists from the Jena-based start-up nanozoo GmbH and Jena University Hospital (UKJ) - both partners in the InfectoGnostics Research Campus Jena. The team has published the results of the study under the title "Leveraging mobility data to analyze persistent SARS-CoV-2 mutations and inform targeted genomic surveillance" in the open access journal eLife.
Nanopore genome sequencing by employees of the Institute of Infection Medicine and Hospital Hygiene at Jena University Hospital.
Michael Szabó/UKJ
The SARS-CoV-2 pandemic has shown the importance of early tracking of outbreaks and infection pathways. However, effective monitoring of the infection process requires an accurate and resource-saving evaluation of data that actually leads to reliable predictions. UKJ doctoral student Riccardo Spott, together with other InfectoGnostics researchers from the UKJ and nanozoo, has now been able to show that cell phone service data in combination with high-resolution metadata (such as zip codes and genome data) can actually improve the monitoring of infection incidence in the German state of Thuringia. In the event of disease outbreaks, such data analysis could enable more targeted testing and quarantine in future.
In their evaluation, the researchers made sure that the data was processed anonymously, as Dr. Christian Brandt, UKJ researcher and co-founder of nanozoo, explains: "We received the mobile phone data as aggregated data pools, combined it with sequence data and analyzed it for conspicuous distribution patterns. It's like looking at an ant trail from above: You can see the totality of the movements, but you can't pick out a single ant."
In the study, more than 6,500 SARS-CoV-2 alpha genomes (B.1.1.7) were sequenced in Thuringia over a period of nine months. The data was supplemented with isolation data of the patients and their zip codes. In addition, over 136,000 publicly available German alpha genomes and cell phone service data for Thuringia were included in the analysis. The team identified nine relevant mutation variants of the virus in Thuringia, which were divided into seven separate kinship groups (phylogenetic clusters) with different spread patterns in Thuringia.
Use of cell phone data for better sampling
By linking genome and mobile phone data in this way, the scientists were able to precisely trace the spread of the alpha lineage of the coronavirus in Thuringia. The risk of distortion of epidemiological data was also demonstrated on the basis of a specific mutation - a factor that can be used to optimize future evaluations. Furthermore, this approach also enables more targeted testing: with additional data from an Omikron subline (BQ.1.1), the InfectoGnostics partners were able to show that such analyses can also be used to actively control sampling during regional outbreaks of an infectious disease. A federal state like Thuringia could thus monitor future epidemics more efficiently.
Prof. Dr. Mathias Pletz, Director of the Institute of Infection Medicine and Hospital Hygiene at Jena University Hospital and member of the extended InfectoGnostics Board of Directors, explains: "The spread of viruses often follows the paths of human mobility. With anonymized mobile phone data and advanced molecular techniques, we are able to map and predict these paths." According to Pletz, this makes a significant contribution to predicting the spatial spread of a pathogen - which can vary greatly between regions - more accurately and to implementing more targeted testing and infection control measures.
Note: This article has been translated using a computer system without human intervention. LUMITOS offers these automatic translations to present a wider range of current news. Since this article has been translated with automatic translation, it is possible that it contains errors in vocabulary, syntax or grammar. The original article in German can be found here.
Original publication
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, Volume 13, 2025-1-15
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