# Challenge 4: Modeling Disease Spreading and Flattening the Curve

### Learning Targets:

• Analyze and interpret epidemiological data.
• Perform calculations in order to accurately compare data.
• Identify and use credible sources to find data to support a conclusion.
• Model disease spread and learn how preventative measures impact outbreak intensity.

In the article, “R0: How scientists quantify the intensity of an outbreak” the authors illustrate how the R0 value affects the spreading of an infectious agent. The R0 (pronounced “R naught”), the basic reproduction number, is a mathematical estimate of how infectious a disease is and indicates the number of people an infected individual will go on to infect.

## Consider:

Use the R0 values for COVID-19 and the seasonal flu, rounded to 2 and 1.5, respectively.

### Calculate:

• After three rounds of infection with COVID-19 (one infected individual infects 2 people, and they infect 2 people and so on), how many people are infected with COVID-19? Repeat this calculation for the seasonal flu with an R0 of 1.5.

### Calculate:

• With COVID-19, if one of the two people infected by the original infected person quarantines and is completely isolated, how many people are infected with COVID-19 if you perform the same calculation as in question Q1?

There is a whole branch of science based on computation or the use of numbers and computers to analyze scientific events. Computational biologists often construct models of biological phenomena, such as how an infection spreads through a population. This information can be very useful in predicting the severity of an outbreak or in determining what measures a community may need to take to curb or lessen the impact of an outbreak. In this challenge, you will now use a model for the spread of COVID-19 through a population.

### Set the Parameters:

• Population size 1000, initial-infected 1, infected-chance 10%, average-recovery-time 20, and social distancing 0%. Press set and then go.

### Conclude:

• How many people in the population become infected by the virus? How many people are unaffected? (Be careful here and make sure to read the correct values!)

### Reset the Parameters:

• Now, set the parameters as you did in part b, but change social distancing to 50%. Press set and then go.

### Reflect and Discuss:

• How many people in the population become infected by the virus with 50% of the population social distancing? How many people are unaffected?

## Hypothesize, Design, and Investigate

Using the website in section 4.4 above, design an experiment to test one or more of the other variables in the model (infection chance and/or recovery time). Start by making a hypothesis of how you think the variable will impact the number of people in the population who become infected with the virus. Remember that hypothesis statements are measurable. Run your experiment and report your results.

## Reflect & Discuss

 Question Hints What data or numbers are the most important to know in order to accurately estimate the spread of a pandemic infection? What variables impact the collection of this data? Is R-naught a fixed number for a particular disease or does it change? Don’t forget the environment. What factors can impact R0 during an outbreak? How does social distancing influence the spread of an infection through a population? Using the modeling website, did you notice that even a small amount of social distancing made a difference in the infection curve?