Back

Real-time, multi-pathogen wastewater genomic surveillance with Freyja 2

Levy, J. I.; Gangavarapu, P.; Pilz, D. A.; Jesvaghane, M. A.; Steedman, A. L.; Zeller, M.; Gangavarapu, K.; McDonald, D.; Libuit, K. G.; Kapsak, C.; Young, E. L.; Agrawal, S.; Orschler, L.; Hölzer, M.; Stachler, E.; Park, D. J.; Hinrichs, A. S.; Laurent, L. C.; Knight, R.; Andersen, K. G.

2025-07-27 epidemiology
10.1101/2025.07.26.25332245 medRxiv
Show abstract

Case-based infectious disease surveillance is fundamental to public health, but is resource-intensive, logistically complex, and prone to sampling bias. Wastewater testing and sequencing have increasingly been used for population-scale monitoring of pathogen dynamics, including in low-resource settings. Broader adoption of wastewater genomic surveillance, however, is limited by a lack of flexibility across sequencing platforms and approaches, and adaptability to additional pathogens. Here, we describe "Freyja 2", an integrated bioinformatics tool enabling robust real-time inference of pathogen lineage prevalence and growth dynamics from wastewater and other complex samples. In Freyja 2, we develop new methods for estimating lineage prevalence and growth rates, and demonstrate robustness across common sequencing platforms and to low genomic coverage. By incorporating global pathogen data streams, we extend Freyja 2 to support multi-pathogen surveillance. We demonstrate tracking of multiple recent or ongoing public health emergencies, including COVID-19, mpox, and H5N1 influenza, revealing unreported diversity and lineage co-circulation.

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.