Multi-objective optimization models for patient allocation during a pandemic influenza outbreak

Abstract

Pandemic influenza has been an important public health concern with several historical outbreaks in 1918, 1957, and 1968. During an influenza pandemic outbreak, hospitals are often overwhelmed by the surge demand of influenza patients. It is important to prepare response plans to react to a pandemic influenza outbreak. Because of the widespread effect of the disease and the increased demand on limited medical resources, collaboration among hospitals both in planning and in response is necessary. This paper focuses on patient and resource allocation among hospitals in a healthcare network. Mathematical models are built to optimize the patient allocation considering two objectives related to patients’ cost of access to healthcare services: (1) minimization of the total travel distance by patients to hospitals; and (2) minimizing the maximum distance a patient travels to a hospital. Moreover, the models help to predict a resource shortage during an outbreak; this prediction will alert decision makers to consider increasing the medical capacity or requesting additional capacity from state or national agencies. In addition, the model aids in the determination of the optimal allocation of the additional resources, when available, among hospitals by considering the above two objectives related to patients’ cost of access to services. A case study from Metro Louisville, Kentucky, is presented to demonstrate how the models would aid in patient allocation during a pandemic influenza outbreak.