SARS-CoV-2 forecasts for Washington and other US states

Here we show the change in frequency of SARS-CoV-2 variants over time in Washington and other US states. We use this change in frequency to estimate the relative growth advantage or evolutionary fitness of different variants. We apply a Multinomial Logistic Regression (MLR) model to estimate frequencies and growth advantages using daily sequence counts. We apply this model independently across different US states and partition SARS-CoV-2 variants by Nextstrain clades and separately by Pango lineages.

The Seattle Flu Alliance adapted Nextstrain’s workflow for countries to produce variant forecasts for US states. Further details on data preparation and analysis can be found in the forecasts-ncov-states GitHub repo (forked from Nextstrain’s forecasts-ncov repo), while further details on the MLR model implementation can be found in the evofr GitHub repo.

Enabled by data from GISAID logo.

Multinomial Logistic Regression is commonly used to model SARS-CoV-2 variant frequencies. However, please apply caution in interpretation of these results.

WA SARS-CoV-2 Forecast Dashboard

US SARS-CoV-2 Forecast Dashboard

Use of Seattle Flu Alliance (SFA) data sets

Screenshots of the data presented on this page and the underlying data itself may be used under a CC-BY-4.0 license and attribution to must be provided.

The SFA is happy to consider requests for de-identified data (with more variables than is presented on this page) and may consider requests for sharing of limited data ssets as well. Sharing of limited data sets requires execution of a Data Transfer Use Agreement. Requests are reviewed on a case-by-case basis. The SFA does not have the resources to modify the format of the data to be shared - such requests for metadata format modifications will not be considered.