Daily Video

May 24, 2020

How to understand computer-based models for COVID-19



Directions: Read the news summary, watch the video and answer the discussion questions. The video has been edited for length. To watch the video in its entirety or read the transcript, click here.



For months, scientists and public officials have relied on computer-based models to try to predict the trajectory of the coronavirus outbreak. But models are not crystal balls, and all of them involve human assumptions.

  • Since the outbreak of COVID-19 began, scientists have developed numerous predictive models to understand the outbreak and inform policymakers.
  • Some models have over-predicted cases and fatalities of COVID-19, and some models have under-predicted them. But each model is based around its own set of questions and assumptions that are important to understand before interpreting their findings.


Discussion questions:

  1. Essential question: Why are computer-based models for coronavirus beneficial and problematic for scientists?
  2. What do you think journalist Miles O’Brien means when he says, “We live in a complicated world, filled with more data than insight?”
  3. Every predictive model starts with assumptions scientists make that determine methodology (a system of procedures used in a particular area of study) and the results of the model.
    • What are some assumptions that went into the Imperial College model mentioned in this clip?
    • What are some of the assumptions that went into the Institute for Health and Metrics Evaluation (IHME) model?
  4. What can we learn from comparing the results of these models to the recorded numbers of cases and fatalities from COVID-19?
  5. How might politicians, activists and others use (or misuse) modeling data to make political arguments or shape official health policies like stay-at-home orders?
  6. Media literacy: Find a news article that mentions the Imperial College model or the Institute for Health Metrics and Evaluation (IHME) model.
    • Does the article address the guiding questions, assumptions and methodology behind the model?
    • If so, how is that methodology described? If not, how do you think the absence of such details might confuse readers?



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