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Worksheet: How R Naught (R₀) Shapes an Epidemic

Part 1: What does a disease's R₀ mean?

  • Navigate to the How R₀ Shapes an Epidemic Notebook at SimPandemic. SimPandemic is an online simulator that lets you explore how various factors change a pandemic's evolution. Read through scenarios 1-2 and study the accompanying tables and graphs.
  • Use the information in the SimPandemic Notebook to answer questions 1-4.
1. Measles has an R₀ that ranges from 12-18. What does this mean in terms of how infectious the disease is?
2. What are three factors that contribute to the R₀ of an infectious disease?
3. In your own words, explain why the R₀ of a disease might change from one community to another.
4. Does an epidemic occur when a disease has an R₀ of less than 1? Explain why or why not. (Hint: Think about what having an R₀ of less than 1 means and the definition of an epidemic).

Part 2: How do different R₀ values change an epidemic?

In this part, you will use the Sandbox to run your own simulations. If you have questions about how to use the Sandbox, read the FAQ. Make sure to save your work as you go (see FAQ). When you have completed all your simulations, write or paste the URL for them here for your teacher to see:


Use what you learn from your own simulations to answer questions 5-7.
5. Follow the directions in the Sandbox portion of How R₀ Shapes an Epidemic Notebook to change the R₀. Run three simulations for each value of R₀ (2, 3, and 4). Record your data below for each value and calculate averages

R₀ of 2:   Simulation Run #1

Total number of infections
Total number of deaths
First day the number of infections/day (blue line) above 1000
Last day the number of infections/day (blue line) above 1000
Total number of infections/day (blue line) were above 1000
Largest value for infections/day (peak value for blue line)

R₀ of 2:   Simulation Run #2

Total number of infections
Total number of deaths
First day the number of infections/day (blue line) above 1000
Last day the number of infections/day (blue line) above 1000
Total number of infections/day (blue line) were above 1000
Largest value for infections/day (peak value for blue line)

R₀ of 2:   Simulation Run #3

Total number of infections
Total number of deaths
First day the number of infections/day (blue line) above 1000
Last day the number of infections/day (blue line) above 1000
Total number of infections/day (blue line) were above 1000
Largest value for infections/day (peak value for blue line)

R₀ of 2:   Average

Total number of infections
Total number of deaths
First day the number of infections/day (blue line) above 1000
Last day the number of infections/day (blue line) above 1000
Total number of infections/day (blue line) were above 1000
Largest value for infections/day (peak value for blue line)

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

R₀ of 3:   Simulation Run #1

Total number of infections
Total number of deaths
First day the number of infections/day (blue line) above 1000
Last day the number of infections/day (blue line) above 1000
Total number of infections/day (blue line) were above 1000
Largest value for infections/day (peak value for blue line)

R₀ of 3:   Simulation Run #2

Total number of infections
Total number of deaths
First day the number of infections/day (blue line) above 1000
Last day the number of infections/day (blue line) above 1000
Total number of infections/day (blue line) were above 1000
Largest value for infections/day (peak value for blue line)

R₀ of 3:   Simulation Run #3

Total number of infections
Total number of deaths
First day the number of infections/day (blue line) above 1000
Last day the number of infections/day (blue line) above 1000
Total number of infections/day (blue line) were above 1000
Largest value for infections/day (peak value for blue line)

R₀ of 3:   Average

Total number of infections
Total number of deaths
First day the number of infections/day (blue line) above 1000
Last day the number of infections/day (blue line) above 1000
Total number of infections/day (blue line) were above 1000
Largest value for infections/day (peak value for blue line)

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

R₀ of 4:   Simulation Run #1

Total number of infections
Total number of deaths
First day the number of infections/day (blue line) above 1000
Last day the number of infections/day (blue line) above 1000
Total number of infections/day (blue line) were above 1000
Largest value for infections/day (peak value for blue line)

R₀ of 4:   Simulation Run #2

Total number of infections
Total number of deaths
First day the number of infections/day (blue line) above 1000
Last day the number of infections/day (blue line) above 1000
Total number of infections/day (blue line) were above 1000
Largest value for infections/day (peak value for blue line)

R₀ of 4:   Simulation Run #3

Total number of infections
Total number of deaths
First day the number of infections/day (blue line) above 1000
Last day the number of infections/day (blue line) above 1000
Total number of infections/day (blue line) were above 1000
Largest value for infections/day (peak value for blue line)

R₀ of 4:   Simulation Average

Total number of infections
Total number of deaths
First day the number of infections/day (blue line) above 1000
Last day the number of infections/day (blue line) above 1000
Total number of infections/day (blue line) were above 1000
Largest value for infections/day (peak value for blue line)
6. Based on your simulations, compare and contrast how a COVID-19 epidemic unfolds if the R₀ is low (2) versus high (4). Give examples using your data from question 5.
7. A disease’s deadliness is a measure of how likely you are to die of the disease if you catch it. In other words, it is the percentage of infections that result in death (see equation). Based on this definition and your simulations, how does R₀ impact COVID-19’s deadliness?
Correct.
Incorrect.
Explain your reasoning.

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