I’ve seen lots of folks confused about the IHME uncertainty bands: why does it look like the estimate for tomorrow is more uncertain than the estimate for months from now? Two points:

https://abs.twimg.com/emoji/v2/... draggable="false" alt="☝️" title="Up pointing index" aria-label="Emoji: Up pointing index"> this is an artifact of the model choice
https://abs.twimg.com/emoji/v2/... draggable="false" alt="✌️" title="Victory hand" aria-label="Emoji: Victory hand"> the uncertainty is not quantifying what you think https://twitter.com/LucyStats/status/1247999935531888641">https://twitter.com/LucyStats...

https://abs.twimg.com/emoji/v2/... draggable="false" alt="☝️" title="Up pointing index" aria-label="Emoji: Up pointing index"> the IHME model is a *curve fitting* exercise - the curve they’ve chosen is Gaussian (normal), this comes with many assumptions! E.g.:

https://abs.twimg.com/emoji/v2/... draggable="false" alt="👥" title="Busts in silhouette" aria-label="Emoji: Busts in silhouette"> it’s symmetric, meaning the rate of increase is assumed to be the same as the rate of decrease
https://abs.twimg.com/emoji/v2/... draggable="false" alt="🏔" title="Snow capped mountain" aria-label="Emoji: Snow capped mountain"> the biggest uncertainty is at the peak

They are using the observed data (solid line) and not reporting uncertainty since it is known, and predicting the future data (dashed line) with uncertainty. Since they predict we are riiiight at the peak, it looks like we are the most uncertain about tomorrow

If you look at this screenshot I took a few days ago before their predicted peak, you see the maximum uncertainty was a few days into the (then) future because, again, with this particular curve the maximum uncertainty is at the peak https://abs.twimg.com/emoji/v2/... draggable="false" alt="🏔" title="Snow capped mountain" aria-label="Emoji: Snow capped mountain">

https://abs.twimg.com/emoji/v2/... draggable="false" alt="🤷‍♀️" title="Woman shrugging" aria-label="Emoji: Woman shrugging"> the confusion this generates highlights why the model assumptions play such an important role in how we evaluate the model itself - it is really not enough to look at the bands and try to understand the implied uncertainty, there is a lot more going on under the hood!