Due to the recent COVID-19 outbreak, makeshift (MS) hospitals have become an important feature in healthcare systems worldwide. Healthcare personnel (HCP) need to be able to navigate quickly, effectively, and safely to help patients, while still maintaining their own well-being. In this study, a pathfinding algorithm to help HCP navigate through a hospital safely and effectively is developed and verified. Tests are run using a discretized 2D grid as a representation of an MS hospital plan, and total distance traveled and total exposure to disease are measured. The influence of the size of the 2D grid units, the shape of these units, and degrees of freedom in the potential movement of the HCP are investigated. The algorithms developed are designed to be used in MS hospitals where airborne illness is prevalent and could greatly reduce the risk of illness in HCP. In this study, it was found that the quantum-based algorithm would generate paths that accrued 50-66% less total disease quantum than the shortest path algorithm with also about a 33-50% increase in total distance traveled. It was also found that the mixed path algorithm-generated paths accrued 33-50% less quantum, but only increased total distance traveled by 10-20%.
Keywords: COVID-19; air-borne diseases; field hospitals; front-line workers; pathfinding AI.