Disease X vaccine production and supply chains: risk assessing healthcare systems operating with artificial intelligence and industry 4.0

OBJECTIVE: The objective of this theoretical paper is to identify conceptual solutions for securing, predicting, and improving vaccine production and supply chains. METHOD: The case study, action research, and review method is used with secondary data – publicly available open access data. RESULTS: A set of six algorithmic solutions is presented for resolving vaccine production and supply chain bottlenecks. A different set of algorithmic solutions is presented for forecasting risks during a Disease X event. A new conceptual framework is designed to integrate the emerging solutions in vaccine production and supply chains. The framework is constructed to improve the state-of-the-art by intersecting the previously isolated disciplines of edge computing; cyber-risk analytics; healthcare systems, and AI algorithms. CONCLUSION: For healthcare systems to cope better during a disease X event than during Covid-19, we need multiple highly specific AI algorithms, targeted for solving specific problems. The proposed framework would reduce production and supply chain risk and complexity in a Disease X event.