WG/GLIMPRINT Seminars, Barhak, COVID Lessons Learned, July 10, 2025

Lessons Learned from Modeling COVID-19: Steps to Take at the Start of the Next Pandemic
Jacob Barhak, PhD

The COVID-19 pandemic spurred many computational modeling efforts. Many mistakes were made and many lessons were learned. This study attempts to list the key lessons learned from a modeling perspective, highlighting both the successes and shortcomings observed during the pandemic. Additionally, this work attempts to compile a set of critical steps and best practices that the authors believe would prove helpful and should be implemented before the start of the next pandemic to avoid inaccuracies in modeling pandemic scenarios. This will help to improve preparedness and ensure that computational models can more effectively guide decision-making in future pandemics.

To learn more, see:
Lessons Learned from Modeling COVID-19: Steps to Take at the Start of the Next Pandemic:
https://www.preprints.org/manuscript/202411.2193/v1
Presentation to MIDAS:
https://www.youtube.com/watch?v=xvmehz_pnUo&ab_channel=MIDASNetwork

*Contents*
00:00 - Introduction
03:37 - Lessons Learned from Modeling COVID-19: Steps to Take at the Start of the Next Pandemic
53:10 - Questions and Discussion

For a copy of the slides for this video visit: https://www.clinicalunitmapping.com/show/Lessons_Learned_COVID19_Latest.pdf

Moderator: James A. Glazier

If you found this video useful, please check out our other videos on computational modeling, infection and immunology: https://youtube.com/playlist?list=PLiEtieOeWbMKh9VcQoinSwODcSZKMTGat

Please consider joining our IMAG/MSM WG on Multiscale Modeling and Viral Pandemics: https://www.imagwiki.nibib.nih.gov/content/msm-viral-pandemics-meetings

Please also consider joining the Global Alliance for Immune Prediction and Intervention: http://glimprint.org/

Видео WG/GLIMPRINT Seminars, Barhak, COVID Lessons Learned, July 10, 2025 канала James Glazier