Joint allocation of emergency medical resources with time-lag correlation during cross-regional epidemic outbreaks

Abstract

During cross-regional epidemic outbreaks, patients with suspected infection have a high demand for testing resources, while patients with confirmed infection have a high demand for treatment resources. The allocation of testing resources affects the demand for treatment resources in multiple regions and at different periods, which features a time-lag correlation. In this study, we developed a joint allocation model for allocating testing and treatment resources with consideration of their time-lag correlation to minimize the loss of patients and maximum the fairness of allocation in all regions. We devised a sequential solution generation strategy (SSGS) and combined it with the NSGA2 and MOPSO algorithms to solve the proposed joint allocation problem. We demonstrated numerically that NSGA2 coupled with SSGS yields a better solution. Furthermore, we compared the solutions of our joint allocation model with those of an independent allocation model. Our numerical results show that the joint allocation scheme, which considers the time-lag correlation between the two types of resources, has better performance—with a lower loss of patients and higher fairness of allocation. In particular, when the impact of the allocation of testing resources on the demand for treatment resources was increased, the joint allocation scheme performed better in terms of both the objectives.