Lesson Scenario- Simple Sick Model (AgentSheets)

Basic Model:

Description

This is an agent-based model that shows the spread of disease. The user is able to control the initial amount of healthy/sick people and the infection rate. The sick are red sad faces and the healthy are blue smiley faces. When a sick face comes into contact with a healthy face, the healthy face has a chance of becoming sick (the infection rate).

Background Information

This model shows the spread of diseases that eventually turns into an epidemic because of how large the population is. Similar to real life, when a disease starts to affect more than a few people in one area, such as a city, then it becomes an epidemic. For example, the swine flu, the regular flu, and meningitis all started out small but then spread to many people and affected people from other cities and states. Also, when a sick and healthy person comes into contact, the healthy person does not become sick immediately. It may take a healthy person interacting with sick people continuously to become sick. This depends on the infection rate of the disease.

Science/Math

The math subject that is presented in this model is probability. Get sick = number interactions * infection rate. It is not usual that a person will become sick the moment they are with a sick person. Usually, there are a certain number of interactions and a chance they will become sick. Also, the population graphs for the sick and healthy follow the equation, HAVE = HAD + CHANGE. For each, the current amount of sick/healthy (HAVE) is equal to the previous amount (HAD) plus the CHANGE in the population (ie. healthy getting sick, sick dying/getting better, etc.)

Teaching Strategies

First, have a discussion with students about various diseases and what is an epidemic. Examples illustrating an epidemic include the flu, swine flu and, more historically, the Black Death. Next, have the students open the model and run it with the default values. Students should run the model at medium speed to see what is going on. Then, have the students open the simulation properties (click on the circled arrow) window and change the infection rate. The different rates can be broken down into groups. The groups can be infection rates of 1, 10, 15, and 30. It may be easier to track if more agents are added. The agents are added by clicking the gray rectangle, the blue face, and dragging the faces next to the red face so a rectangle forms. It will be seen that the higher the infection rate, the faster the disease will spread.

Implementation

How to use the Model

The elements of the model are the faces and infection rate. These two are the only parameters that can be changed. The blue faces represent healthy people; the red faces represent sick people. By using the system properties window, the user can change the infection rate. The higher the rate, the more likely people will become sick and vice versa. Running the model is simple. If the user wants more population density, they can add more agents onto the environment. To increase density, the added faces must be put into a clump. While running the model the user can choose to manipulate the speed at which it runs. There is a slider bar at the bottom that says "slow" and "fast". Users maneuver the slider bar to their preference. For more information about AgentSheets reference the AgentSheets tutorial at: http://shodor.org/tutorials/agentSheets/Introduction.

Learning Objectives

1. How population density affects the spread of disease
2. Understanding Probability

Objective 1

Population density is how tightly packed a population is in an environment. To test this, students should experiment with different amount of population clumps. Then, record how long it takes for the disease to spread. Also, have the students change the layout of the world tiles below (able to simulate a quarantine area of a building/hospital)

• Does it take longer for the disease to spread in more densely populated areas or less densely populated areas?
• Is this true in reality?
• Does having a quarantine area help stop the spread of disease? In the short-term? In the long-term? Why?

Objective2

The infection rate is the chance that a healthy person will get sick if they interact with a sick person. To see how probability affects the model, have students pick different infection rates to test and set.

• How does the probability affect the rate diseases spread?
• As the number of molecules increased in the system, what seemed to happen to the movement of the molecules? Thinking back to the mathematical equation or the classroom analogy, what do you think happened with the pressure?
• Suppose the disease killed the infected very quickly. Would this affect the rate at which the disease spreads? How? Why?

Extensions

1. Additions to the model to make more complex/realistic (ie. recovery rate, doctors, death, immunity)
2. Explore the idea of geographical isolation (by changing the worksheet layout)

Extension 1

In reality, most people are usually able to recover after a while of being sick. This extension would be causing the sick to become well again after a period of time. Then, since they have gotten over the sickness they will become immune for an adjustable amount of time. Meanwhile, doctors would cure the sick they encounter. Also, there should be an adjustable percent chance that the sick is not able to recover and pass away.

• How do these changes affect the spread of disease? Individually? As a whole?
• What will be the ending result after a long period of time?
• If you tasked the students to infect (and kill) the entire population using disease, what would the values be for each parameter? Would making an extremely high infection rate and death rate yield the desired outcome?

Extension 2

Different layouts, whether within the confines of a building or due to the landscape of the outside environment can affect the spread of disease. Have the students first build a worksheet with a quarantine zone full of infected connected to a "healthy zone" using a narrow hallway. Next, have them try to build a world that does not allow for the spread of disease (yet still has everything connected).

• Does having a quarantine zone work? What eventually happens when you have all the healthy closely gathered in one location?
• Which layouts best slow the spread of disease? What common traits do they all share, if any?
• If you had to plan to build a hospital, what layout would you choose and why? Would this layout be the most efficient way to optimize your space and to allow speedy transport of critically injured patients into surgery?

Related Models

Simple Sick Model (NetLogo)

This is a very basic model that begins with a population of 199 healthy people and one sick person. When the sick person touches a healthy person they immediately become sick as well. This model shows how drastic situations can be due to the spread of disease.

Directable Fire!! (Interactive)

This is an applet where the user starts a fire in one tree and sees how it affects the other trees. There is the option of resizing the forest, manipulating the burn speed and manipulating the probability of the trees around the tree on fire to burn.