One academic summarizes how we should read and interpret COVID-19 models:
Every time the White House releases a COVID-19 model, we will be tempted to drown ourselves in endless discussions about the error bars, the clarity around the parameters, the wide range of outcomes, and the applicability of the underlying data. And the media might be tempted to cover those discussions, as this fits their horse-race, he-said-she-said scripts. Let’s not. We should instead look at the calamitous branches of our decision tree and chop them all off, and then chop them off again.
Sometimes, when we succeed in chopping off the end of the pessimistic tail, it looks like we overreacted. A near miss can make a model look false. But that’s not always what happened. It just means we won. And that’s why we model.
Five quick thoughts in response:
- I would be tempted to say that the perilous times of COVID-19 lead more people to see models as certainty but I have seen this issue plenty of times in more “normal” periods.
- It would help if the media had less innumeracy and more knowledge of how science, natural and social, works. I know the media leans towards answers and sure headlines but science is often messier and takes time to reach consensus.
- Making models that include social behavior is difficult. This particular phenomena has both a physical and social component. Viruses act in certain ways. Humans act in somewhat predictable ways. Both can change.
- Models involve data and assumptions. Sometimes, the model might fit reality. At other times, models do not fit. Either way, researchers are looking to refine their models so that we better understand how the world works. In this case, perhaps models can become better on the fly as more data comes in and/or certain patterns are established.
- Predictions or proof can be difficult to come by with models. The language of “proof” is one we often use in regular conversation but is unrealistic in numerous academic settings. Instead, we might talk about higher or lower likelihoods or provide the best possible estimate and the margins of error.
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