SUCCEED

Virus Hunter Investigation

Objective: Students will learn how to analyze data and model situations using excel to model the spread of a disease or virus.

Materials: Each student will need

• A pencil and paper
• A computer with Excel

Source: Mathematical Biology, J.D. Murray, Springer - Verlag, 1989.

Case Facts:

The week after Spring Break all 500 students and teachers at Contagious High School in Contagious, Maryland went home sick with influenza (the flu). It has been discovered that three students came back after Spring Break, each had a different infectious flu. The rates at which each of the flus can infect another person (Infection Rate) and the rates at which a person with the flu gets better (Recovery Rate) are commonly known for each of these strains. You have been asked to determine which of these originally infected students was the source of the flu that was so virulent it affected nearly everyone in the school.

Modeling the Flu

Have your students build a computer model of this epidemic with the following questions in mind:

• Which of the three students was responsible for infecting everyone in the school? How can this be determined?
• How many students would have gotten sick from the flu if only Bobby Getwels had come back sick and the others had stayed home? Amess Besick? Majorly Ruddy?

To build this model, you should have your students use the 1927 Kermack - McKendrick model known as the SIR algorithm. This algorithm looks at the change in three populations: susceptible (S), infected (I), and recovered (R). It assumes that once you recover, you don't get sick again.

Encourage discussion about how to build the model by looking at the interactions between each individual:

• What is the relationship between S, I and R? Build your model based on this relationship.
• How can these be written out?
• What kind of assumptions are implied?

The algorithms (equations) of importance are:

• Get Sick= Susceptible * Infected * Infection Probability
• Get Better= Infected * Recovery Rate

This algorithm assumes a constant population; that is, no immigration or emigration of potential players. This also assumes, naturally, that no one dies of the illness! In this model 1/a is the average infectious period.

Student Name	Flu Strain	Infection Rate	Recovery Rate
Bobby Getwels	Ick Flu	.002	.5
Amess Besick	Plu Flu	.001	.5
Majorly Ruddy	Gacky Flu	.08	.2

Once the students have built their models, have them show the three different populations on their graph.

Extending the Model

Have a discussion about the following scenario. If time, have the students model it, or variations on it, as well.

• What would happen if some of the students had been vaccinated prior to the beginning of the epidemic?
• Let's assume that 50% of the susceptible students are vaccinated each day -- some of them getting the shot while the epidemic is happening in order not to get sick (this assumes, falsely, that instant immunity is realized once you've received the shot.) Experiment with the 50% vaccination rate to determine how it changes the intensity and duration of the epidemic.

Using the Model:

[Technical note: you should graph susceptible, infected, and recovered on your graph. Scale each of these variables to a logical range, such as a minimum of 0 people to a maximum of 500 people.]