Infectious Disease Sample Model


During the time of an infectious disease outbreak, the strain on hospital and medical care networks is unprecedented. There is uncertainty in what demand a healthcare facility might see. In response, the Simio team has created a model to assess the capacity of critical resources for a hospital. We hope this model facilitates an assessment of a hospital network’s resources and provokes discussion if action needs to be taken.

A healthcare provider’s ability to combat an infectious disease is directly impacted by their quantity of resources available. The accessibility of beds and machines such as ventilators are crucial factors for patients that require intensive care. Being able to anticipate the demand for these resources in a facility is vital with repercussions linked to the mortality rate. With our model you will be able to experiment with the number of beds, ventilators, and personal protective equipment (PPE) such as masks, gloves, and gowns, to find the level necessary to provide care to the infected population. This model may be useful as is or used as a starting point to customize a model to meet your specific needs.

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The model uses multiple assumptions based off the real system, such as the hospital’s service area population, the current number of infected cases, the contagion rate (R0), and isolation practices (social distancing factor), to calculate how the infection will spread. These properties may be set in the model properties available in the Properties window. The health provider in the area can begin to assess how many infection cases to expect over a span of time. As new cases occur, a percentage of patients will remain asymptomatic or will neglect to go to the hospital. Based on the free roaming infectious people, infection spread rate, and social distancing factor, the model will create new infection cases.

Contingent on the infected cases expected, a portion are anticipated to need intensive care and must be hospitalized. Additionally, another subsection of those hospitalized will be in a critical state that requires a ventilator machine during hospitalization. The population is grouped by age and each age range has a corresponding probability to need hospitalization or a ventilator when infected. This is defined in the model using a Data Table which can be updated to fit a healthcare providers service area if the specific area varies.

The objects shown in the model represent the cases sent to the hospital. The number of hospitalized infection cases enter the model as a Patient Entity and are sent to the Hospital Server. At the Hospital, they will claim a unit of the Hospital’s capacity representing a bed, and if they need a ventilator, they will attempt to seize the secondary Ventilator Resource. Then the patient remains at the hospital for their assigned length of stay.

The model keeps track of the PPE material used by the patients each day and uses an inventory replenishment policy to restock on masks, gloves, and gowns when they reach a certain level. Ordering lead time is accounted for when waiting to replenish the material. This logic is also available in a data table which can be manipulated to experiment with inventory replenishment policies that suit your facility.

While running the model, buttons are available to change the current number of beds and ventilators. This allows you to dynamically experiment with the model as events unfold. For example, the hospital could have reallocated more beds for infection cases in the middle of the run. The charts and labels in the model will update to display the effects of a change.
The model tracks important metrics such as the number of times a critical resource was not available. A variable is counting the instances an infected person was turned away from the Hospital due to a lack beds. Another variable is counting how many times a person had a bed, needed a ventilator, and there were no ventilators available. The quantity of PPE one patient is expected to use in a day is specified in the data table. When there is a shortage of PPE material, the deficit is also counted and saved. A basic experiment is set up in the model to test how changing the system controls will impact the count of the resource deficits.

Model Shortcuts

If you download the model, we set up some quick shortcuts for you to experience different views.  Simply press the following letters to experience different views:

  • o – Overview of the entire model
  • i – Infection chart for population
  • c – Case chart for hospital population
  • h – Hospital facility overview
  • g – hospital ground (3D) view
  • p – Personal protection equipment consumption and availability chart

Model Assumptions

  • The input for the number hospital beds is to be the number the hospital can allow strictly for infected patients. Expect to keep beds reserved for other emergencies.
  • When the hospital has no available beds for infected cases, the cases turned away are sent to another hospital or are self-quarantining. They do not infect others.
  • Once a person recovers, they are no longer in the pool of people who can become infected. It is assumed the infectious disease strain can not mutate quick enough to cause reinfection.
  • Patients who need a ventilator are assumed to be in the ICU and require the ventilator the entire duration of their hospital stay.
  • Patients who need a ventilator and cannot acquire a ventilator are prematurely discharged.  

Get the Model!

There are 2 ways to get the model. Either way you get it, it's free to use with our Personal Edition!

  1. Join Simio Insiders to see it in our latest sprint!
  2. Fill out the form below.