Killfeed

• Proposes a digital twin model for neonatal E. coli K1 meningitis integrating clinical, microbiological, and physiological data. • Aims to enable real-time prediction of disease progression and personalized antibiotic management in neonates. • Framework models pathogen invasion, blood-brain barrier traversal, immune response, and treatment effects. • Includes modules for bacterial virulence, host immune response, pharmacokinetics, and clinical vital signs. • Uses continuous data assimilation with Bayesian inference and machine learning for individualized predictions. • Designed for validation through retrospective data, silent operation, and pilot clinical deployment. • Potential to improve neonatal outcomes, guide therapy, and support research into pathogen-host interactions.