The reason for my skepticism is that there are an increasing number of states with numbers that, if correct, indicate a very non-standard behavior of infection growth. I'll discuss a few examples.
Missouri
The new case numbers over the last number of days were 72, 101, 146, 168, 168, 65. For the first four days, the numbers were increasing as would be expected. The day before yesterday, they level off with no increase in the rate of new cases. And then yesterday, the fall to 65. The leveling off is perhaps plausible, although unlikely to happen so quickly. The fall to 65 is bizarre.
Georgia
The numbers here are 172, 254, 221, 278, 476, 445, 237. The large ups and downs and especially the drop to 237 yesterday are not consistent with any model of infection growth that I'm aware of. Maybe everyone decided to go to church yesterday instead of the emergency room.
Louisianna
The numbers are similarly odd to Georgia: 335, 216, 407, 510, 441, 569, 225.
Washington
248, 111, 627, 493, 610, 586. What's with the drop to 111 and then the sudden increase to the largest value in the series of 627 followed by a large drop the next day?
Massachusetts
679, 579, 823, 1017, 698. Is the trend up, down, or sideways?
There are also some states where the number of cases has jumped out at an extreme rate in addition to odd bouncing around. Here are just a couple of examples.
Connecticut
203, 257, 137, 279, 233, 469. This state bounces around in odd ways similar to others but ends with a large increase instead of a decrease.
California
369, 433, 471, 795, 842, 1657. At least all these numbers are going in the same direction, but did the rate of new cases suddenly go to a doubling every day as of yesterday?
What all the examples above indicate, and the many more I didn't highlight, is that there are highly inconsistent and problematic reporting issues. This makes it very hard to accept the validity of the numbers and trends. Even if the day to day noise in individual states is removed by aggregation, it's not at all clear that the total numbers across the U.S. are valid, or if they are artificially low (or, less likely, high) due to inadequate testing protocols.
I'll continue to track the numbers, but I have less and less confidence in their quantitative value. The total number of new cases each day are still increasing while testing capabilities continue to lag. This should mean that a larger fraction of cases go undetected or untested each day. That would lead to a systematic decrease in reported cases that does not reflect the reality of the situation.
2 comments:
In general, centralized lab testing, is inefficient. We see a few things in the field:
1. Throughput of location - how many patients can one location swab in a given day?
2. Throughput of the specific lab or a technical error in the lab - how many samples can they process in a given day?
3. Logistics issues such as a delay in getting tests to the lab and missing a particular run and getting processed in the next batch.
Often when we see tests fall off and not follow the historical trend. We will see a pop the next day from pent up testing volume or the entire week will be up slightly while it levels off to the what we were trending.
This is also why a lot of companies are racing to decentralize the lab and of course democratize general lab testing.
Thanks for the great comment. Agreed. The problem is by the time decentralized and efficient testing starts to ramp up, the social distancing measures are also likely to start taking effect. There's almost no way to disentangle those effects, which would be very helpful from an epidemiological point of view. On the other hand, it would be good to finally have some credible data!
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