Minimising biases in #viralsurveillance data from #wastewater: A study led by the groups of Niko Beerenwinkel and Tanja Stadler @ethz.ch as well as their collaborators at @eawag.bsky.social developed a mathematical tool to factor in 'shedding' >https://u.ethz.ch/zUQBP

We’re happy to announce our study on tracing influenza A from wastewater has been published in Water Research. We developed a novel tiling amplicon approach, enabling deep genomic coverage and our method is now part of Swiss-wide wastewater surveillance efforts. www.sciencedirect.com/science/arti...

www.nature.com/articles/s41... In our new study, we use mathematical modelling, simulations, and Swiss wastewater data to show that that key epidemiological metrics are not biased by differences in virus shedding. This highlights the robustness of wastewater monitoring for pathogen surveillance.

Congratulations to Xiang Ge Luo for the Outstanding Student Paper award at ISMB/ECCB 2025 in Liverpool (www.iscb.org/ismbeccb2025...) for her work on the Bayesian inference of fitness landscapes via tree-structured branching processes published in Bioinformatics at doi.org/10.1093/bioi...

We are excited to share our latest research published in Nature Genetics, where we could detect genetic heterogeneity within circulating tumor cell clusters that can seed metastases. To do so, we tailor-made a new phylogenetic algorithm "CTC-SCITE": www.nature.com/articles/s41...