A digital twin for Escherichia coli K1 neonatal meningitis

Graphical Abstract Escherichia coli K1 is a major Gram-negative pathogen responsible for neonatal meningitis. Despite significant progress in antimicrobial therapy and neonatal intensive care, clinical outcomes remain problematic due to delayed diagnosis, rapid disease progression and a lack of precision tools for personalized management. Here, we propose a technical and translational digital twin framework for E. coli K1 infection in neonates that integrates clinical, microbiological, physiological and molecular data within a continuously adaptive computational model. This twin would simulate bloodstream invasion, blood-brain barrier traversal and central nervous system inflammation in real time, enabling dynamic prediction of disease and optimization of antibiotic regimens. The framework is intended as a technical resource for clinicians and modellers working in neonatal infectious disease. A digital twin may advance neonatal infectious disease management, i.e. transforming empirical treatment into evidence-based, patient-specific precision care while providing new mechanistic insights into host-pathogen interactions.