I am incredibly proud to celebrate a new publication in Vaccine co-authored by SCSRU graduate consultant and second-year Ph.D. student Xisi Zhang! In a wonderful interdisciplinary collaboration with researchers from the School of Public Health Sciences and the School of Pharmacy, Xisi provided vital statistical expertise to investigate how COVID-19 vaccine hesitancy shifts between mandatory primary doses and optional booster doses among university students. Given the massive dataset and a high volume of complex variables, traditional selection methods fell short. Xisi expertly proposed and implemented the Quasi-likelihood under the Independence Model Criterion (QIC) to evaluate model fit—a sophisticated approach where the custom coding took an immense amount of deep thought, rigorous troubleshooting, and dedicated time to execute perfectly. Xisi’s perseverance and technical skill are a phenomenal example of how developing robust programming solutions can unlock answers to real-world public health challenges.
Congratulation Xisi!
