Queueing Models and Assessment Tools for Improving Mass Dispensing and Vaccination Clinic Planning

Abstract

To react to an outbreak of a contagious disease that requires medication or vaccination, county health departments must set up and operate mass dispensing and vaccination centers, commonly known as points of dispensing (PODs), to treat residents who may be affected. Carefully planning these PODs before an event occurs is a difficult and important job. Simulation models can provide an accurate representation of resident flow through PODs, but are not convenient for public health professionals to access. Queueing theory provides a multitude of analytical models appropriate for various situations so many models that it is often difficult to discern which model is correct for a particular circumstance. There are also some situations for which no models are available, particularly those involving batching and multiple servers. A complete set has been gathered of those models that are the most generalized, and hence useful for the widest range of applications. Where no appropriate model was available, modifications to the existing equations are proposed and tested. To implement this general queueing framework, software has been developed which can quickly generate planning models using steady-state queueing network approximations; these models use commonly available spreadsheet software to maximize accessibility for public health emergency planners. The planning models are validated against models created in several queueing software packages, along with simulation models automatically generated from the planning models. The number of stations and staff within a POD are not the only concerns that a public health emergency preparedness and response plan must address. A plan assessment tool is proposed, which can help planners ensure that their POD plans include all relevant information. A layout assessment tool is also developed, which endeavors to give planners suggestions on how to design PODs for maximum efficiency.