(Disclaimer: I don’t know what I’m talking about, pointers to real literature would be more useful than this, every sentence deserves to be aggressively hedged/caveated, etc.)
Increasing test capacity: I’ve seen some people suggest that the second wave is just an artifact of increased testing in these states. If that were the case, then there would be no rise in covid cases to be explained. But then I would expect the fraction of tests that returned positive to be decreasing, and we aren’t seeing that. This one seems like wishful thinking to me.
I don’t think the increase in testing capacity fully explains the “second wave,” but I think it does totally change the quantitative picture.
Intuitively I expect that (rate of change in positive test %) is better than (rate of change in confirmed cases) as a way of approximating (rate of change in actual cases). It also doesn’t seem great, especially over multiple weeks, but I’ll use it here until someone convinces me this is dumb.
Johns Hopkins aggregates testing numbers here. Picking CA as a second-wave state, it hit its minimum positive test rate of .04 on May 24. That rate rose by 20% by June 21, to 0.048 (and has kept going up).
If there was a 7 day lag, we’d expect to see a 20% increase in deaths by from May 31 to June 28. Eyeballing the google deaths data things look basically flat. So I guess that means a drop of ~20% in fatality rate over that month.
Trying again, let’s take Georgia. Minimum of .058 on June 10, up 50% to .091 by June 21. Google seems to have deaths roughly constant or maybe decreasing from June 17 to June 28, which is a ballpark ~30% drop in fatality rate to offset the ~50% increase in infections.
One problem with these numbers is that I think the test numbers are for day the test occurred, but the death numbers are for the day they are reported. Would probably be better to use numbers for the day the death actually occurred, though I think that probably requires going at least a few days further back in time (which is going to make it harder to interpret cases like Georgia that hit the minimum only 3 weeks ago).
Delayed initial testing: When things were first taking off in first wave states, our testing capacity was way behind where it needed to be. Perhaps this heavily suppressed the initial “confirmed” numbers for the first wave, and so we should expect to see second wave deaths rise in the next few weeks?
It seems like the average time lag between showing symptoms and dying from COVID is something like 18 days (here, data from China but if anything I expect longer lags here). So if we were testing people earlier it seems like we could easily have more like a 2 week lag than a 1 week lag. That could mostly explain Georgia and California.
Overall I can’t really tell what’s going on, my sense is that your story in the post is basically right (and demographic changes sound likely) but that the mystery to be explained is *much* less than a 5x change in fatality rate. I feel like the constant death rate in the face of exploding cases is suspicious but best guess is that it’s a coincidence, death rates will end up rising and IFR will end up modestly lower than the initial wave.
I would love to see a version of the analysis in the OP controlling for big increases in testing, and getting a more careful handle on lags between testing and death. Hopefully someone has already done that and it’s just a matter of someone here finding the cite.
If there was a 7 day lag, we’d expect to see a 20% increase in deaths by from May 31 to June 28. Eyeballing the google deaths data things look basically flat. So I guess that means a drop of ~20% in fatality rate over that month.
The CDC site says the lag on reporting deaths is between 2 and 8 weeks—and can be longer.
When you train on old data, you get a lag of about 10-12 days between changes in cases and corresponding changes in deaths. There are several reasons that could be not true on new data:
1. Cases are getting caught earlier by more/faster testing.
2. Cases are leading to fewer or slower deaths (due to either treatment or population effects)
3. The lag on old data is using the reported date of death, but that’s not the same as the date of the reporting of the death, which has an additional lag.
I suspect a couple of things might be worth considering, but I’m not the expert here either so take everything with the view I am speculating/thinking aloud not stating any findings.
I don’t think testing will tend to lower the CFR as that testing will move things towards the real IFR rather than the CFR. This probably related to point 1 & 2 above.
I think the 10-12 days from the old data to say we see movement in the death data due to the new cases probably has some type of skew in it, the older the data the more likely it will be complete. That should be driven by the the death reporting distribution (and perhaps even corrections). The closer the old data gets to the new threshold of new deaths it should under report due to the lag. Perhaps we need to look at the distribution of reported deaths over that 8+ week period before trying to assess the results after the 10-12 days. I’m not sure if that is what you are saying in point 3.
(Disclaimer: I don’t know what I’m talking about, pointers to real literature would be more useful than this, every sentence deserves to be aggressively hedged/caveated, etc.)
I don’t think the increase in testing capacity fully explains the “second wave,” but I think it does totally change the quantitative picture.
Intuitively I expect that (rate of change in positive test %) is better than (rate of change in confirmed cases) as a way of approximating (rate of change in actual cases). It also doesn’t seem great, especially over multiple weeks, but I’ll use it here until someone convinces me this is dumb.
Johns Hopkins aggregates testing numbers here. Picking CA as a second-wave state, it hit its minimum positive test rate of .04 on May 24. That rate rose by 20% by June 21, to 0.048 (and has kept going up).
If there was a 7 day lag, we’d expect to see a 20% increase in deaths by from May 31 to June 28. Eyeballing the google deaths data things look basically flat. So I guess that means a drop of ~20% in fatality rate over that month.
Trying again, let’s take Georgia. Minimum of .058 on June 10, up 50% to .091 by June 21. Google seems to have deaths roughly constant or maybe decreasing from June 17 to June 28, which is a ballpark ~30% drop in fatality rate to offset the ~50% increase in infections.
One problem with these numbers is that I think the test numbers are for day the test occurred, but the death numbers are for the day they are reported. Would probably be better to use numbers for the day the death actually occurred, though I think that probably requires going at least a few days further back in time (which is going to make it harder to interpret cases like Georgia that hit the minimum only 3 weeks ago).
It seems like the average time lag between showing symptoms and dying from COVID is something like 18 days (here, data from China but if anything I expect longer lags here). So if we were testing people earlier it seems like we could easily have more like a 2 week lag than a 1 week lag. That could mostly explain Georgia and California.
Overall I can’t really tell what’s going on, my sense is that your story in the post is basically right (and demographic changes sound likely) but that the mystery to be explained is *much* less than a 5x change in fatality rate. I feel like the constant death rate in the face of exploding cases is suspicious but best guess is that it’s a coincidence, death rates will end up rising and IFR will end up modestly lower than the initial wave.
I would love to see a version of the analysis in the OP controlling for big increases in testing, and getting a more careful handle on lags between testing and death. Hopefully someone has already done that and it’s just a matter of someone here finding the cite.
Small addition on:
The CDC site says the lag on reporting deaths is between 2 and 8 weeks—and can be longer.
When you train on old data, you get a lag of about 10-12 days between changes in cases and corresponding changes in deaths. There are several reasons that could be not true on new data:
1. Cases are getting caught earlier by more/faster testing.
2. Cases are leading to fewer or slower deaths (due to either treatment or population effects)
3. The lag on old data is using the reported date of death, but that’s not the same as the date of the reporting of the death, which has an additional lag.
Are you saying it’s (at least partly) #3?
I suspect a couple of things might be worth considering, but I’m not the expert here either so take everything with the view I am speculating/thinking aloud not stating any findings.
I don’t think testing will tend to lower the CFR as that testing will move things towards the real IFR rather than the CFR. This probably related to point 1 & 2 above.
I think the 10-12 days from the old data to say we see movement in the death data due to the new cases probably has some type of skew in it, the older the data the more likely it will be complete. That should be driven by the the death reporting distribution (and perhaps even corrections). The closer the old data gets to the new threshold of new deaths it should under report due to the lag. Perhaps we need to look at the distribution of reported deaths over that 8+ week period before trying to assess the results after the 10-12 days. I’m not sure if that is what you are saying in point 3.