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Comment by goalieca | Hacker News Reader

There’s a huge responsibility for any scientist who can confidently claim to predict the future.

Most of the COVID projections were off by a lot. The parameters reduced social distancing to a single scalar and often generated those values as outputs rather than genuine inputs.

I looked at various models over the summer knowing that while they may be wrong, they may be useful. But they did not help much at all for understanding trends and relationships. Take for example models that have been modified for mask mandates. During the summer, most of them had trended down to zero with masks “on” but the opposite has since happened.

There is a genuine amount of circular reasoning where models are used to validate social distancing efforts which in turn are incorporated into revised models.

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> I looked at various models over the summer knowing that while they may be wrong, they may be useful. But they did not help much at all for understanding trends and relationships. Take for example models that have been modified for mask mandates. During the summer, most of them had trended down to zero with masks “on” but the opposite has since happened.

Most of the models I've seen seem to be focused on generating long-term predictions. The big questions people wanted answered at the beginning were "how long will the pandemic last?" and "when will we hit the peak?". And IF you can calibrate your model correctly, for those questions, SIR and its relatives make sense.

But those aren't the questions we should be asking. For public policy decisions, what we really want to know is "is the epidemic spiraling out of control?". For answering that question, a simple exponential fit does well enough in the short term.

Why aren't more models focused on the short-term, when long-term predictions heavily depend on finicky human behavior?

> There is a genuine amount of circular reasoning where models are used to validate social distancing efforts which in turn are incorporated into revised models.

Do you think this reasoning amplifies or reduces social distancing efforts?

> Do you think this reasoning amplifies or reduces social distancing efforts?

Very likely amplifies the effect of social distancing because that is the original bias of researchers. They sought to show that school closures had an effect based on observational studies. Same with other measures. Their statistical modelling attempted to adjust for variables they thought were important.

There were many models that would constantly predict region A to spiral out of control because it's measures were more relaxed than region B. Other assumptions must be wrong but there was very little challenge to the orthodoxy.

Yeah, since nothing much is known with any certainty there should be large error bars around every prediction. However, due to finite resources most people end up just varying a few things and the true extent of the uncertainty remains hidden.

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