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

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.