This paper explores the development of predictive algorithms to enhance the cybersecurity posture of healthcare systems and improve the responsiveness of vaccine production and supply chains during pandemics like Disease X. It analyzes potential bottlenecks in vaccine production and supply, proposing AI-driven solutions for mitigating risks such as tampering, personnel shortages, coordination issues, material shortages, limited capacity, and misinformation. The paper further suggests integrating algorithms into cyber risk assessments, transitioning from qualitative to quantitative evaluations and encompassing secondary risks. New data sources and methodologies are proposed for enhanced risk assessment and optimization, including the adaptation of existing frameworks like the FAIR Institute method. Challenges in implementation and testing are discussed, along with potential solutions.

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