AUSim Vaccine Clinic Evaluation, Design, and Setup Project

Hopefully in the near future, COVID-19 vaccination clinics will be setup and operated in thousands of locations all around the world. Our goal with this project is to develop and provide analysis and design tools and guidance to support the setup and operation of these clinics. We are very early in this project and welcome any feedback and/or contributions to the effort – just contact me at or @auie86 on Twitter.

We use a combination of discrete-event simulation and rough-cut capacity (queueing) analysis to assess performance and evaluate clinic configurations. The rough-cut capacity model (here’s the Excel version of the model) provides a preliminary view of the resource requirements based on a target inoculation rate, staffing levels, and mean-value estimates of the process times. If you are setting up a clinic, this is where you start. In order to get started with this model, focus on filling in the yellow-shaded cells and pay attention to the green (good) and red (bad) shaded cells. If you’re not familiar with the rough-cut capacity concept, have a look at the following video:

The rough-cut capacity analysis provides accurate mean-value analysis, but does not account for the clinic layout (and the imposed travel times for patients and workers) or variation in process times and patient arrival times/patterns. This is where the simulation models come into play. The following pages provide the details (text, videos, and model downloads) of our simulation approach.

  • Simulation model template – Describes the Simio simulation model template, custom objects, and data-generated modeling approach.
  • EAMC Auburn Community Clinic Case Study – Describes an implementation of the simulation model template for a local vaccination clinic.
  • Inoculation Module model – A Simio project including a custom inoculation module object that let’s the user evaluate different configurations of inoculation stations (clinicians giving shots and assistants doing the other work) and estimating the patient throughput rate (and process times) that are input to the main clinic model.

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