Accounting for individual choice in public health emergency response planning

Abstract

During public health emergencies, organizations in charge require an immediate and e ffcient method of distributing supplies over a large scale area. Due to the uncertainty of where individuals will choose to receive supplies, these distribution strategies have to account for the unknown demand at each facility. Current techniques rely on population ratios or requests by health care providers. This can lead to an increased disparity in individuals' access to the medical supplies. This research proposes a mathematical programming model, along with a solution methodology to inform distribution system planning for public health emergency response. The problem is motivated by distribution planning for pandemic influenza vaccines or countermeasures. The model uses an individual choice constraint to determine what facility the individual will choose to receive their supplies. This model also determines where to allocate supplies in order to meet the demand of each facility. The model was solved using a decomposition method. This method allows large problems to be solved quickly without losing equity in the solution. In the absence of publicly-available data on actual distribution plans from previous pandemic response e fforts, the method is applied to another representative data set. A computational study of the equity and number of people served depict how the model performed compared to the actual data. The results show that implementing an individual choice constraint will improve the effectiveness of a public health emergency response campaign without losing equity. The thesis provides several contributions to prior research. The first contribution is an optimization model that implements individual choice in a constraint. This determines where individuals will choose to receive their supplies so improved decisions can be made about where to allocate the resources. Another contribution provided is a solution methodology to solve large problems using a decomposition method. This provides a faster response to the public health emergency by splitting the problem into smaller subproblems. This research also provides a computational study using a large data set and the impact of using a model that accounts for individual choice in a distribution campaign.