README

Combining genomic data and infection estimates to characterize the complex dynamics of SARS-CoV-2 Omicron variants in the United States

This paper is now published in Cell Reports

Rafael Lopes1,*, Kien Pham1, Fayette Klaassen2, Melanie H. Chitwood1, Anne M. Hahn1, Seth Redmond1, Nicole A. Swartwood2, Joshua A. Salomon3, Nicolas A. Menzies2, Ted Cohen1,*,†, Nathan D. Grubaugh1,4,*,†

1 Department of Epidemiology of Microbial Diseases and Public Health Modeling Unit, Yale School of Public Health, New Haven, CT, USA

2 Department of Global Health and Population, Harvard T. H. Chan School of Public Health, Boston, MA, USA

3 Department of Health Policy, Stanford University School of Medicine, Stanford, CA, USA

4 Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, USA

† Co-senior authors

* Corresponding authors: rafael.lopes@yale.edu, theodore.cohen@yale.edu, nathan.grubaugh@yale.edu

Data Availability

The findings of this study are based on metadata associated with 3,103,250 sequences available on GISAID from September 1st, 2021 up to April 22nd, 2023, and accessible at https://doi.org/10.55876/gis8.231023hd (GISAID Identifier: EPI_SET_231023hd). All genome sequences and associated metadata in this dataset are published in GISAID’s EpiCoV database. To view the contributors of each individual sequence with details such as accession number, Virus name, Collection date, Originating Lab and Submitting Lab and the list of Authors, visit https//doi.org/10.55876/gis8.231023hd

Pipeline Running order

All the codes to reproduce the paper analysis are in 'Scripts/' folder. At 2023-10-30, the pipeline running order is:

• manuscript_figures.R, make all the manuscript figures.
• manuscript_table.R, make all the manuscript tables.
• 01_metadata_cleaning.R, clean metadata from GISAID and set variant categories, count, and frequencies.
• (Optional) 02_plot_metadata.R, plot figures with variant counts and frequencies.
• 03_infections_per_variant_estimates.R, estimates infections per variants.
• (Optional) 04_plot_infections_estimates.R, generate plots of infections.
• 05_variant_rt_estimates_daily.R, estimate Rt per variant per state.
• 06_rt_ratios.R, estimates Rt ratio per pair of variants.
• 07_attack_rate_svi.R, attack rate vs SVI correlation and figure4 of the manuscript.