Abstract

Abstract Computational models that predict an individual's response to a vaccine offer the potential for mechanistic insights and personalized vaccination strategies. These models often stem from small cohort studies focusing on single vaccines limiting their generalizability. The ability to assess the performance of resulting models would be improved by comparing their performance on independent datasets. We established a prototype platform that evaluates Computational Models of Immunity to Pertussis Booster vaccinations (CMI-PB). It aims to generate experimental data specifically for model assessment through annual data releases and contests. In a preliminary 'dry run', over 30 existing computational models were tested to predict immune responses from pre-vaccination multi-omic profiles with only one successful model based on age. The performance of new models built using CMI-PB training data was much better but varied significantly based on the choice of pre-vaccination features used and the model-building strategy. This suggests that previously published models developed for other vaccines do not generalize well to Pertussis Booster vaccination. Overall, these results reinforced the need for comparative analysis across models and datasets, which CMI-PB aims to achieve. We seek wider community engagement for our first public prediction contest, which will open in mid 2024.