https://exchange.iseesystems.com/public/isee/covid-19-simulator/index.html#page2
This link is what will be used to answer the questions.
Model Exploration
First let’s get to know our model. Click on the Background Information to learn more about COVID-19. Read through each statement and click on the ? to the right of each statement for more information.
Are you able to briefly summarize the background information?
Now click on Model Overview. This guides you through the boundaries of the simulation and thus includes some important assumptions. Read through each model assumption and click on the ? to the right of each statement for more information.
Can you briefly summarize the assumptions in the Model Overview section?
Next click on Disease Assumptions. Click on Rest to Default Assumptions. Notice that this tab includes parameter values.Look at each tab (Severity and Infectivity, Behavior, Duration, Mortality). Be sure to click the ? next to each assumption/parameter.
Can you briefly summarize the assumptions in the Disease Assumptions section?
Look at the Infection Curve graph.
What is on the x-axis?
What is on the y-axis?
What do the colors represent?
Look at the Population Status graph.
What is on the x-axis?
What is on the y-axis?
Why are there so many different numbers on the y-axis?
What is the maximum number of presumed new infections (estimate)?
Look at the Cumulative Measures graph.
What is on the x-axis?
What is on the y-axis?
Why are there so many different numbers on the y-axis? How many total people were tested by day 292?
Can you explain the relationship among these three graphs? Hint: It’s the same data but graphed in different ways – how?
Next click on Policies and Results. This is where you can modify responses to the virus. This includes
Quarantine start date
Our model runs for a year, and the virus enters on day one (in one carrier). This toggle allows you to institute a global (for everyone) quarantine. Note when you slide the bar it shows the start date in days since beginning of simulation.
Quarantine duration
This is how the quarantine lasts. Note when you slide the bar it shows the duration in days.
Quarantine effectiveness
This ranges from 0 (nothing changes in behavior) to 1 (a perfect quarantine where no contact occurs)
Contact tracing effectiveness
If you implement a contact tracing program, this notes how well the test does at identifying positive (exposed or infected) individuals.
Symptomatic test rate
This is the number of tests per day each sick person receives. If this number is less than 1 it implies it takes multiple days to get a test.
Daily testing capacity
How many tests can be run each day. Note this determines if test rates can be met.
The model has many more variables you could change, like mortality rate (Disease assumptions > Mortality), how long each stage of infection lasts (Disease assumptions > Duration), and how people who are infected change their behavior (Disease Assumptions > Behavior tab). Some of these interact while others don’t. For example, the global quarantine is different from how individuals respond to being sick. We’ll next explore some example scenarios. To compare them, we should leave the other assumptions the same among all simulations.
Example Scenarios
Click on The COVID-19 Simulator title at the top of the simulator and select the Do Nothing scenario.
Go to Policies and Results. Describe why the parameter values listed represent ‘Do Nothing’.
Describe what happens to the number of Sick individuals over time with the default parameters. When is the peak number of infected individuals? What is that peak?
Click on The COVID-19 Simulator title at the top of the page and select the Effective Quarantine scenario.
Go to Policies and Results. Describe why the parameter values listed represent ‘Effective Quarantine’.
At the maximum, are there more people too ill to work or who need hospitalization? How do you know?
Click on The COVID-19 Simulator title at the top of the page and select the Additional Testing scenario.
Go to Policies and Results. Describe why the parameter values listed represent ‘Additional Testing’.
How many people were sick when the maximum number of people were sick? (You can and should interact with the graph. Be careful, the answer is not 90.8K.)
Click on The COVID-19 Simulator title at the top of the page and select the Containment scenario.
Go to Policies and Results. Describe why the parameter values listed represent ‘Containment’.
Explain why the Infection Curve looks the way that it does. Do you think this graph is an effective way to display the data? Why or why not.
Compare the outcomes from each four strategies. Support your conclusions below with graphs (that have clear captions and are referenced in your text).
How do the Infection Curves vary for each of the four strategies? Consider Hospitalized, Sick, Healthy, Dead, and Recovered.
How do the Population Status graphs vary for each of the four strategies? Specifically consider Too Ill to Work, and Needing Hospitalization.
How do the number of Cumulative Deaths, Hospital Bed Days, and Cumulative Testing vary for each of the four strategies?
What are the overall important takeaways from comparing the four strategies?
Strategy Design
Design a strategy to reduce or eliminate the virus and its impacts.
Describe your strategy.
Describe how you implemented your strategy (what parameter values did you use).
How did your strategy compare to the four strategies you explored above? You should include screenshots of graphs that support your conclusions.
How realistic (given real world constraints) is your strategy compared to the four strategies you explored above?
