This free article from the Washington Post looks at disease modeling and why the models vary so widely. It takes you thru a disease modeling simulation showing how seemingly small changes in a model’s assumptions or inputs can cause a large variation in the results.
Makes a good real-world example for math and statistics, showing both their value and limitations. Have students explore what the underlying math looks like. Can they describe the equations or the process?
This can also be a springboard for further student inquiry: What data would you need to make better models? How do you balance competing goals? Is it possible to optimize for both? What would “optimized” look like?