2026/06/30 by Filippo Castiglione, Kayode Oshinubi, Jacob Barhak · 1 voice
Mathematics · Computer Science · Physics and Astronomy · #COVID-19 epidemiological studies #Gaussian Processes and Bayesian Inference #Model Reduction and Neural Networks
paper · pdf · doi:10.1186/s12982-026-02407-x
openalex publication_date 2026/06/30 · openalex created_date 2026/07/01 · openalex updated_date 2026/07/01
Abstract 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 with special attention to ensemble models. This will help to improve preparedness and ensure that computational models can more effectively guide decision-making in future pandemics. The paper counts 17 main recommendations for actions. The recommendations are primarily experience-driven. Those can be briefly summarized as improve data gathering, making resources accessible, and to use ensemble models to explain the pandemic.