flu-covid-coadministration

Data and code from:

Ipsilateral and contralateral coadministration of influenza and COVID-19 vaccines produce similar antibody responses eBioMedicine 103, 105103 (2024) doi: 10.1016/j.ebiom.2024.105103

David Pattinson¹, Peter Jester¹, Chunyang Gu¹, Lizheng Guan¹, Tammy Armbrust ¹, Joshua G. Petrie², Jennifer P. King², Huong Q. McLean², Edward A. Belongia ², Peter Halfmann¹, Gabriele Neumann¹, Yoshihiro Kawaoka^1,3,4,*

1. Department of Pathobiological Sciences, School of Veterinary Medicine, University of Wisconsin-Madison, Madison, WI 53706, USA
2. Marshfield Clinic Research Institute, Marshfield, WI 54449, USA
3. Division of Virology, Department of Microbiology and Immunology, Institute of Medical Science, University of Tokyo, Tokyo 108-0071, Japan
4. Department of Special Pathogens, International Research Center for Infectious Diseases, Institute of Medical Science, University of Tokyo, Tokyo 108-0071, Japan

Fig. 1: Vaccine responses after ipsilateral (n = 35) and contralateral (n = 81) coadministration of COVID-19 and influenza vaccines. See study for details.

Repository contents

• data.csv Contains long format data. Columns are:
  • experiment: 1 or 2 for the first or second replicate.
  • virus: BVic, BYam, H1N1, H3N2 or SARS-CoV-2
  • pre_sample: This individual's pre-vaccination sample*.
  • post_sample: This individual's post-vaccination sample name*.
  • log_pre_titer: Pre-vaccination titer on the log scale.
  • log_post_titer: Post-vaccination titer on the log scale.
  • log_delta_titer: log_post_titer minus log_delta_titer
  • sites: Ipsilateral or Contralateral indicating whether this individual received their flu and COVID vaccines ipsilaterally or contralaterally.
• analysis.ipynb is an IPython notebook containing code to rerun the Bayesian model used in the study, and to recreate Figure 1 (the legend and tick labels were manually edited for the manuscript version.)
• effects.csv is generated by the IPython and contains summaries of posterior distributions of model effects.
• requirements.txt contains python package versions used in this analysis. Install them (in a new virtual environment) via pip install -r requirements.txt

*sample names were hashed for anonymity.