By early April the model hadshifted somewhat.
Although it still predicted that wed endure around 81,000 deaths, the highest estimate came down to about 136,000.
Its estimates of the amount of hospital ICU beds and ventilators we would need were reduced as well.
Adobe Stock / Angelina Bambina
Does that mean we overreacted in shutting down schools, businesses, and nearly entire cities?
But even if a model doesnt end up fully reflecting reality, that doesnt mean it cant be helpful.
What goes into creating a model of infectious disease spread?
Short answer: a lot.
Well, get ready.
Take the seasonal flu, for example.
That makes it easier to create a more accurate model of how the flu season will go.
Other major challenges have to do with the testing process, Shaman says.
The availability of tests and when people decide to seek testing plays a role too.
Whats more, not all states report the amount of negative tests they get.
That has real-world effects on how governments, hospitals, and individuals prepare for the pandemic.
But more recently the hospitalization rate in the U.S. hasbeen much lower.
So why were the initial projections wrong?
We know were undercounting a number of cases because of testing constraints, Dr. Adalja says.
And if thats the case, then that means were overestimating the hospitality and fatality ratios.
Getting these numbers as right as possible is incredibly important if youre, say, a hospital planner.
And that may be wrong if your hospitalization rate number is overestimated.
Models like these were designed to change as time goes on.
Remember that models are still not a substitute for real-world data.
Theyre tools used by policymakers to think about different ranges of scenarios, Dr. Adalja says.
Theyre not ironclad; you should expect models to change as more data comes out.
In fact, most models end up being wrong for one reason or another, he says.
And the further we look into the future, the more uncertain the projected outcomes are.
But sometimes, like in this case, the reason why models change is actually encouraging.
They then use each of those values to create both short- and long-term death rate models.
But thats the wrong conclusion to draw.
In fact, as Shaman says, thats what it means to flatten the curve.
So what should you take away from these models?
They can use models to project what the future will be like and what preparations we need to make.
