Covvfit: Variant fitness estimates from wastewater data
Covvfit is a framework for estimating relative growth advantages of different variants from deconvolved wastewater samples. It consists of command line tools, which can be included in the existing workflows and a Python package, which can be used to quickly develop custom solutions and extensions.
Getting started
Covvfit can be installed from the Python Package Index:
For an example how to analyze the data using the provided command line tool, see this tutorial.
For more detailed installation instructions, including troubleshooting, see the installation guide.
FAQ
How do I run Covvfit on my data?
We recommend to start using Covvfit as a command line tool, with the tutorial available here.
What data does Covvfit use?
Covvfit uses deconvolved wastewater data, accepting relative abundances of different variants measured at different locations and times. Tools such as LolliPop or Freyja can be used to deconvolve wastewater data.
Note, however, that the deconvolution procedure should not smooth abundance results. For more information on this topic, see here.
Can Covvfit predict emergence of new variants?
No, Covvfit explicitly assumes that no new variants emerge in the considered timeframe, so its predictions are unlikely to hold on longer timescales. The underlying model also cannot take into account changes in the transmission dynamics or immune response, so that it cannot predict the effects of vaccination programs or lockdowns.
How can I contact the developers?
In case you find a bug, want to ask about integrating Covvfit into your pipeline, or have any other feedback, we would love to hear it via our issue tracker! In this manner, other users can also benefit from your insights.
Is there a manuscript associated with the tool?
Yes! The reference for this tool is:
David Dreifuss, Paweł Piotr Czyż, Niko Beerenwinkel. Learning and forecasting selection dynamics of SARS-CoV-2 variants from wastewater sequencing data using Covvfit. medRxiv 2025.03.25.25324639; doi: https://doi.org/10.1101/2025.03.25.25324639