AP Environmental Science
Wheeler High School
Mr. Walstead
Online Disease Lab
From: http://www.learner.org/courses/envsci/interactives/disease/
Lesson 1 > The Virgin Field > Step 1
Most diseases begin with what is called "the virgin field"—a scenario in which humans have no natural or
man-made immunity to the disease. To see the progress of a disease in a particular community, start by
predicting how many sick days will be reported when you run the Kold disease through a medium-sized
population, and record your prediction in the data table. In this first run-through, we'll assume that the
population does not move around the field; they interact with their neighbors, but do not travel long
distances. Make sure the tableau is set to virgin and run the simulator to 100 days (click on Run button) three
times and answer the following:
1. Do you get the exact same results each time? How do the results compare to each other and to your
prediction? What factors might contribute to susceptibility to the disease?
2. If the contagion rate is calculated as the number of new cases per day per total population, what
would the average contagion rate be for Kold?
Lesson 1 > The Virgin Field > Step 2
Unlike some of the other interactive labs, this model has some randomness built in to reflect the real spread
of a disease, which is a matter of probabilities. Despite this variability, you can get a sense for what effect
each factor has on disease spread.
Before running the simulator, predict whether the sick days per capita will be higher or lower with low
population density. Record your prediction in the data table and then run the simulator to 100 days three
times, recording the data each time.
Make a prediction for high population density, record it in the data table, and run the simulator three times,
recording that data in the table. Answer the following:
1. What could be done to prevent the spread of disease in a low population density? What kinds of
challenges would high population density present to these precautions?
2. If contagion rate is calculated as the number of new cases per day per total population, what would
the average contagion rate be for Kold?
Lesson 2 > Vaccination > Step 1
In this lesson we'll look at "Impfluenza". You'll start by examining the disease's effects in a virgin field.
First, compare the disease details (found in Simulation Parameters) of Impfluenza and Kold so that you know
the differences between the two diseases. Based on these differences and what you know about Kold,
predict the sick days per capita for Impfluenza at medium population and medium mixing. Record your
prediction in your data table.
In the simulator, select Lesson Vaccination, then set Countermeasure to None to provide the virgin field
effect. Then run the simulator to 100 days three times. Answer the following:
1. Was your prediction correct? If not, why not?
2. Notice that Impfluenza, unlike Kold, has a death rate. How many people die, on average, when you
run the simulator on the virgin field?
3. How does a death toll change precautionary factors? What kinds of precautions might you take with
Impfluenza that you might not have taken with Kold?
4. Would you consider Impfluenza's death toll to warrant a "state of emergency"? How high would the
numbers have to be for this to happen?
Lesson 2 > Vaccination > Step 2
In this step we'll look at the effects of using a counter-measure by vaccinating a certain percentage of the
population against Impfluenza. This represents a real-life scenario, where the country vaccinates a certain
portion of its population against the expected influenza strains for that year. Change the tableau in the upper
right corner of the simulator from Virgin to Vaccine. Predict and record the sick days per capita at
medium population and medium mixing while the vaccine is in use. Run the simulator three times and record
your data. Compare your results to the table in Lesson 2 Step 1. Then change the parameters to high
population and high mixing with vaccine in use. Predict what will happen and run the simulator three
times, recording your data for each run. Answer the following:
1. For the first set of parameters (medium/medium), how does the vaccine reduce sick days? How large
a percentage of the population would have to be immunized in order to bring the sick days per capita
reliably below 0.1 per capita?
2. How does using vaccination compare to changing the mixing or population density of the field?
Lesson 3 > Counter-Virus > Step 1
What if we were to face an outbreak of a disease such as avian flu? In 1918-1919, the world experienced a
pandemic unlike anything seen since the Black Plague of the mid-14th century in Europe. The Spanish Flu,
or La Grippe, killed somewhere between 20 and 40 million people worldwide. In America alone, 28% of the
population was infected with the virus, the vast majority of whom where between the ages of 20 and 40.
There was no method in place at that time to deal with a pandemic with such a high contagion rate as well as
a high death rate. The disease was new to the world in both form and function.
In this lesson, imagine a new disease for which there is no vaccine and the death rate might be very high.
Examine the details of Red Death and predict how many sick days per capita and the death toll of this
new disease in low population and low mixing and record the prediction in your table. To see if you're
correct, set the tableau to virgin, run the simulation three times, and record your data.
What if you had a high population and high mixing? Record your prediction, change the population and
mixing settings to high, and run the simulator three times. Record your data and compare with your
prediction.
1. Calculate contagion rate for each scenario (rural: low/low, urban: high/high). Would either of these
scenarios be considered epidemic? Why or why not? What practical, precautionary measure would
you suggest for each situation based on your calculated contagion rates?
Lesson 3 > Counter-Virus > Step 2
Let's assume that there hasn't been time to develop and distribute a vaccine. However, we may not be dealing
with an entirely virgin field. There may be a disease similar to Red Death which would provide immunity
without a high death rate. (A situation like this occurred in the days of smallpox, when it was discovered that
the similar but less lethal cow pox made people immune.) If we release this "reduced" virus into the
population before Red Death comes along, some people will become sick and may even die from the reduced
virus, but would the immunity provided make up for that? This final scenario may not be very realistic, as we
don't fight diseases by releasing other diseases into a population. However, it does show how the different
aspects of a disease (sick days, transmission rate, death rate, and immunity) interact. Change the tableau to
countervirus. Choose one of the "C-Viruses" from the Countermeasure pull-down and review the features of
that virus with its details button. Which of the three Countermeasure viruses (slow, medium, or fast) will do
the best job in reducing the death toll in the population while also minimizing sick days per capita? Make a
prediction based on everything you've learned about the effects of vaccination and disease transmission,
record it, and then run the simulation three times for each of the C-Virus choices. Answer the following:
1. Can you think of any environmental factors that might contribute to the spread of the disease? How
would a counter-virus affect these environmental factors and/or the environmental factors affect it?
2. Can counter-viruses be used to fight disease internationally or would they be most effective at a local
level? What could health officials do to insure that the highest number of those at risk around the
globe are receiving the most effective preventative health care possible?
DATA TABLES: DISEASE
LESSON 1
Lesson 1:
Step 1
Prediction
Population
Number
xxxxxxxxxxx
Starting Number of
Contagious People
Sick Days
Reported
Contagious
xxxxxxxxxxx
Contagion Rate
xxxxxxxxxxx
xxxxxxxxxxx
Simulation Run 1
Simulation Run 2
Simulation Run 3
Lesson 1:
Step 2
Prediction 1 (low)
Population
Number
Population
Density
Starting
Number of
Contagious
People
Sick Days
Reported
Contagious
Contagion
Rate
xxxxxxxxxx
xxxxxxxxxxx
xxxxxxxxxxx
xxxxxxxxxxx
xxxxxxxxxxx
xxxxxxxxxx
xxxxxxxxxxx
xxxxxxxxxxx
xxxxxxxxxxx
xxxxxxxxxxx
Contagious
Dead
Simulation Run 1
Simulation Run 2
Simulation Run 3
Prediction 2 (high)
Simulation Run 1
Simulation Run 2
Simulation Run 3
DATA TABLES: DISEASE
LESSON 2
Lesson 2:
Step 1
Population
Number
Population
Density
Starting
Number of
Sick Days
Reported
Contagious
People
Prediction
(medium/medium)
xxxxxxxxxx
xxxxxxxxxxx
Population
Mixing
Population
Density
xxxxxxxxxxx
xxxxxxxxxxx
xxxxxxxxxxx
xxxxxxxxxxx
xxxxxxxxxxx
Sick Days
Reported
Contagious
Simulation Run 1
Simulation Run 2
Simulation Run 3
Lesson 2:
Step 2
Prediction 1
(medium/medium)
Starting Number
of Contagious
People
Percentage of
Population
Affected
xxxxxxxxxxx
xxxxxxxxxxx
xxxxxxxxxxx
xxxxxxxxxxx
xxxxxxxxxxx
xxxxxxxxxxx
xxxxxxxxxxx
xxxxxxxxxxx
Simulation Run 1
Simulation Run 2
Simulation Run 3
Predication 2
(high/high)
Simulation Run 1
Simulation Run 2
Simulation Run 3
DATA TABLES: DISEASE
LESSON 3
Lesson 3:
Step 1
Population
Mixing
Population
Density
Percent
Vaccinated/
C-Virus
Used
Starting
Number of
Contagious
People
Prediction 1
(low/low)
xxxxxxxx
xxxxxxxx
xxxxxxxx
xxxxxxxx
Simulation Run 1
Sick Days
Reported
Contagious
Immune
xxxxxxxx
xxxxxx
Dead
Simulation Run 2
Simulation Run 3
Prediction 2
(high/high)
xxxxxxxx
xxxxxxxx
xxxxxxxx
xxxxxxxx
xxxxxxxx
xxxxxx
Simulation Run 1
Simulation Run 2
Simulation Run 3
Lesson 3:
Step 2
Prediction:
Slow
Population
Mixing
Simulation Run 1
(slow)
Simulation Run 2
Simulation Run 3
Simulation Run 1
(mid)
Simulation Run 2
Simulation Run 3
Simulation Run 1
(fast)
Simulation Run 2
Simulation Run 3
Medium
Population
Density
Fast
Percent
Vaccinated/
C-Virus
Used
Starting
Number of
Contagious
People
Percentage of
Population
Affected
Sick
Days
Reported
Dead