Digital twins and explainable AI across the vaccine life cycle: from mRNA manufacturing to thermostability, cold-chain logistics, and immunisation services.
TL;DR
The evidence indicates that DTs are progressing from static simulations to operational twins for plasmid-to-mRNA production, supporting virtual commissioning, rapid scale-up and real-time optimisation, and XAI-enabled approaches improve stability prediction, and artificial intelligence of things (AIoT) architectures strengthen cold-chain monitoring and route planning.
Abstract
The COVID-19 era accelerated the deployment of mRNA vaccines while exposing persistent weaknesses in manufacturing, thermostability and cold-chain systems, particularly in low- and middle-income countries (LMICs). This narrative review, informed by an explicit search of PubMed, Web of Science, Scopus, IEEE Xplore and the ACM Digital Library (2019-2025), synthesises over 50 studies of digital twins (DTs) and explainable artificial intelligence (XAI) across the vaccine life cycle. The evidence indicates that DTs are progressing from static simulations to operational twins for plasmid-to-mRNA production, supporting virtual commissioning, rapid scale-up and real-time optimisation. In parallel, XAI-enabled approaches improve stability prediction, and artificial intelligence of things (AIoT) architectures strengthen cold-chain monitoring and route planning. At the service level, twins of mass vaccination centres and primary-care immunisation services can improve throughput and temperature management; however, current implementations are often siloed, weakly validated and rarely designed with equity considerations. Priorities for the field include integrated, multi-scale 'vaccine system twins', rigorous validation, effective human-AI collaboration and deployment strategies tailored to LMIC contexts.
