Table 4.

Conceptual design

M	Design obstacles (O), and design milestones (M)	O
Phase 1: Predicting (forecasting) cyber risk in healthcare systems during a Disease X event.
M1	Construct an automated Bayesian approach with the novel AutoAI algorithms and discrete binary (Bernuolli) probability distribution to forecast data breaches and the probability of a system going down.	O1
M2	Test if the novel AutoAI algorithm can forecast the actual loss including primary and secondary risk/loss from a Disease X event - with new and emerging forms of data.	O2
M3	Advance the FAIR method with unsupervised learning and regression algorithms and apply the AutoAI to forecast the cyber security readiness for Disease X event.	O3
Phase 2: Algorithmic solutions for medical production and supply chain bottlenecks.
M4	Test how the novel AutoAI algorithm can identify and adapt modern technologies to help with resolving shortages of supplies in critical times e.g., 3D printing.	O4
M5	Construct a self-adaptive vaccine delivery system based on the novel AutoAI algorithm, real-time data, and new modern technologies e.g., drones, autonomous vehicles, and robots.	O5
M6	Dynamic coordination with the novel AutoAI algorithmic design for real-time analytics of the vaccine supply chain in Disease X event.	O6