covvfit 0.3.1

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covvfit

Fitness estimates of SARS-CoV-2 variants from variant abundance data.

• Documentation: https://cbg-ethz.github.io/covvfit
• Source code: https://github.com/cbg-ethz/covvfit
• Bug reports: https://github.com/cbg-ethz/covvfit/issues

Installation and usage

Covvfit can be installed from the Python Package Index:

$ pip install covvfit

For an example how to analyze the data see this tutorial.

References

This method accompanies our manuscript:

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

@article{Dreifuss2025-Covvfit,
	author = {Dreifuss, David and Czy{\.z}, Pawe{\l} Piotr and Beerenwinkel, Niko},
	title = {Learning and forecasting selection dynamics of SARS-CoV-2 variants from wastewater sequencing data using Covvfit},
	elocation-id = {2025.03.25.25324639},
	year = {2025},
	doi = {10.1101/2025.03.25.25324639},
	publisher = {Cold Spring Harbor Laboratory Press},
	eprint = {https://www.medrxiv.org/content/early/2025/03/26/2025.03.25.25324639.full.pdf},
	journal = {medRxiv}
}

See Also

• V-pipe: a bioinformatics pipeline for viral sequencing data.
• cojac: command-line tools for the analysis of co-occurrence of mutations on amplicons.