Location Planning, Resource Reallocation and Patient Assignment During a Pandemic Considering the Needs of Ordinary Patients

Abstract

During the initial phase of a pandemic outbreak, the rapid increase in the number of infected patients leads to shortages of medical resources for both pandemicrelated and ordinary patients. It is crucial to efficiently utilize limited existing resources and strike a balance between controlling the pandemic and sustaining regular healthcare system operations. To tackle this challenge, we introduce and investigate the problem of optimizing the location of designated hospitals, reallocating beds within these hospitals, and assigning different types of patients to these hospitals. Designated hospitals isolate pandemic-related patients from ordinary patients to prevent cross-infection. Moreover, isolation beds can be converted into ordinary beds and vice versa. We model this problem as a mixed-integer nonlinear programming model and incorporate a compartmental model to facilitate the dynamic adjustment of available resources as the pandemic progresses. We illustrate the effectiveness of our model using real data from the COVID- 19 pandemic. In comparison to two benchmark policies, our model demonstrates superior performance in controlling the spread of the pandemic while addressing the needs of both pandemic-related patients and ordinary patients. We also conduct a series of experiments to uncover managerial insights for policymakers to better utilize existing resources in response to pandemic outbreaks. Results indicate that the optimal strategy is to admit as many pandemic-related patients as possible to flatten the pandemic peaks during its initial phases, especially under high transmission rates. We also demonstrate the flexibility of the proposed model by integrating interventions such as mask wearing and lockdown.