The data here doesn’t give a clear idea of how the transitions from 1 to 2, or from 2 to 3 are proceeding. Nonetheless, it may offer some clues. So first, let’s backtrack and think: let’s say California going to level 2 or level 3 did in fact effectively stop coronavirus in its tracks. What should we see?
Ideally, we should see the number of people with coronavirus getting the test drop a lot. However, that doesn’t necessarily mean that the total number of people getting the test drops, because many people who don’t have the disease may also start getting tested, causing the total number of people getting tested to increase. So, more accurately, we should see one of these:
- A drop in the incremental number of tests each day. - A drop in the confirmed positive rate on tests (but this metric is available at a further lag of 5 to 7 days).
I think it could take longer before either of these reflects the change in true cases. Here’s an argument. Suppose:
- Current testing policy is declining to test many symptomatic people due to lack of capacity. I believe this is true (high-risk people, essential workers, and known contacts to existing cases are being prioritized.) - As test availability improves, testing policy will change to test broader categories of symptomatic people, up to the testing capacity. - The number of true cases is substantially higher than the number of other ailments that basically look the same. As a result, P(positive test | symptomatic) remains high and doesn’t change much even if you halve the number of true cases. Probably variance in testing policy and test accuracy will drown out the change.
If this is right, as long as the number of true cases remains above the threshold of testing capacity, we get roughly the same number output on the metrics you mentioned, no matter whether it’s 10 times above capacity or 1000 times above capacity. So if we’re way above capacity right now, we won’t see a decrease in true cases show up in those metrics for a while.
What I wrote there was assuming that the number of new true cases drops to a fairly low level. Whether that happens now or a week or two or three later is unclear; if the 2 → 3 backlog is growing. then resolving that backlog will add more delay.
I posited us already being at this point as the “optimistic” scenario.
I think it could take longer before either of these reflects the change in true cases. Here’s an argument. Suppose:
- Current testing policy is declining to test many symptomatic people due to lack of capacity. I believe this is true (high-risk people, essential workers, and known contacts to existing cases are being prioritized.)
- As test availability improves, testing policy will change to test broader categories of symptomatic people, up to the testing capacity.
- The number of true cases is substantially higher than the number of other ailments that basically look the same. As a result, P(positive test | symptomatic) remains high and doesn’t change much even if you halve the number of true cases. Probably variance in testing policy and test accuracy will drown out the change.
If this is right, as long as the number of true cases remains above the threshold of testing capacity, we get roughly the same number output on the metrics you mentioned, no matter whether it’s 10 times above capacity or 1000 times above capacity. So if we’re way above capacity right now, we won’t see a decrease in true cases show up in those metrics for a while.
What I wrote there was assuming that the number of new true cases drops to a fairly low level. Whether that happens now or a week or two or three later is unclear; if the 2 → 3 backlog is growing. then resolving that backlog will add more delay.
I posited us already being at this point as the “optimistic” scenario.
I’ll reword the post to clarify this.
I did some rewording of the post that made it a little more wordy, but fingers crossed that that part has now become less confusing.