Related Experiment Video
Updated: May 15, 2026
09:26
Quantification and Whole Genome Characterization of SARS-CoV-2 RNA in Wastewater and Air Samples
Published on: June 30, 2023
Learning and forecasting selection dynamics of SARS-CoV-2 variants from wastewater sequencing data using Covvfit
David Dreifuss1, Paweł Czyż2, Niko Beerenwinkel1
1Department of Biosystems Science and Engineering, ETH Zurich, CH-4056, Basel, Switzerland; SIB Swiss Institute of Bioinformatics, CH-1015, Lausanne, Switzerland.
Water Research
|May 13, 2026
Summary
Covvfit, a new model using wastewater data, accurately estimates the fitness advantages of SARS-CoV-2 variants. This method efficiently predicts variant dynamics up to 90 days, aiding pandemic response.
Area of Science:
- Epidemiology
- Genomics
- Environmental Science
Background:
- The COVID-19 pandemic is characterized by the emergence of SARS-CoV-2 variants with selective advantages.
- Estimating variant fitness typically relies on extensive genomic sequencing of clinical samples.
- Wastewater surveillance offers a complementary approach for monitoring pathogen evolution.
Purpose of the Study:
- To introduce Covvfit, a statistical model and software package for estimating variant fitness advantages.
- To utilize wastewater sequencing data for reconstructing variant competition dynamics.
- To validate wastewater-based fitness estimates against clinical data and assess predictive capabilities.
Main Methods:
- Development of the Covvfit statistical model for variant fitness estimation.
- Analysis of over 5000 wastewater sequencing samples collected between 2021 and 2025.
- Comparison of fitness advantage estimates derived from wastewater data with clinical data.
Main Results:
- Wastewater-based estimates of SARS-CoV-2 variant fitness advantages are efficient and accurate.
- Covvfit successfully reconstructed variant competition dynamics across pandemic waves.
- The model accurately predicts future variant dynamics up to 90 days post-detection.
Conclusions:
- Wastewater surveillance provides a reliable and efficient method for estimating variant fitness.
- Covvfit enhances pandemic monitoring by enabling accurate prediction of variant spread.
- This approach supports public health strategies by forecasting evolutionary trajectories of SARS-CoV-2.
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