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6 Dec, 8 tweets, 3 min read
COVID hospitalizations are breaking records, yet overall utilization seems relatively unchanged and mostly normal for this time of the year. How can this be?

How can hospitals both be overwhelmed but also have about the same number of beds available as a month ago? (1/x)
Let's say we randomly select a group of people from the population every day, and have them spend the night in a hospital. Of course they are not random, but that is not the point.

When there is more spread, more of the people who are selected each day will have it. (2/x)
Let's look at NC numbers. The situation is similar in many places.

COVID hospitalizations increased by about a thousand in the past month, but overall utilization is the same. How is this happening?

It is all about COVID unit capacity. (3/x)
Because of isolation requirements, hospitals have separate units for patients with COVID. Regardless of what they are admitted for, positive patients stay there.

The problem is, when it is the virus season, more people will have it. (4/x)
As a result, these units are quickly filling up with patients admitted for all kinds of issues when the virus is widespread. Because staff need PPE in these units, more PPE is being used.

Hence hospitals are getting stretched and are having to open new COVID units. (5/x)
I suspect this is at least in part the reason why the CDC shortened the isolation requirement for COVID patients. New rules should allow hospitals to move patients out of COVID units after 7 days if they have no COVID symptoms. (6/x)
forbes.com/sites/williamh…
Are there people hospitalized "for" COVID also? Of course there are. The virus makes some people very sick.

But that percentage is not big enough to shoot the hospitalized numbers from 40K to over 100K in a month. (7/7)
Evidence to the second tweet above:

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More from @covidtweets

Prof Profile picture
29 Nov
I am beginning to think that the widely accepted model of COVID mainly transmitting between close contacts via droplets is insufficient to explain the data and failing the Occam's razor. Instead, aerosols being the main route of transmission does better in both. (1/x)
Before I explain my reasoning, usual caveats: I am not a virologist/epidemiologist/medical professional/etc. I am just a scientist in another field who is observing the data and trying to make sense of it. So take the following with the usual grain of salt. (2/x)
First, what is Occam's razor? It means generally the theory which require the least number of assumptions is correct. A good example is geocentric vs. heliocentric models, the latter of which requires only one assumption - planets orbit the Sun. (3/x)
ImageImage
Read 13 tweets
Prof Profile picture
23 Nov
Additional analysis on Kansas mask data: Does the size of the first wave (case levels before June) have an impact on the change in cases between August 11th - November 20th? (1/x)

Using population adjusted case numbers, below are the correlations between first wave cases, mask mandate, and change in cases after August. There is a significant negative correlation between first wave size and change after August. (2/x)
The correlation between masks and change is positive, meaning mandate counties had bigger changes. However, this is likely confounded by first wave size, as three counties with bigger first waves are non-mandate counties. (3/x)
Read 6 tweets
Prof Profile picture
23 Nov
The new CDC study on mask effectiveness compares Kansas counties with and without mask mandates and finds that masks are effective.

Are they, really? (1/x)

cdc.gov/mmwr/volumes/6…
The first red flag comes from the data they used for analyses. They pick two seemingly random weeks from before and after the mandates and compare the changes in case numbers. They find that cases doubled in non-mandate counties and slightly declined in mandate counties. (2/x)
While this looks like cherry-picking, the results are actually similar when we compare mandate vs. non-mandate counties in terms of overall increase since August. On average, cases per 100K increased by x19 in non-mask counties vs. x9 in mask counties. (3/x)
Read 8 tweets
Prof Profile picture
22 Nov
While it is too early for interventions to take effect in ND, I wanted to post an update regarding our quasi-experiment comparing SD/ND after ND implemented restrictions and mask mandates, effective Nov. 14th, and SD did not. (1/5)
First, cases: While ND is trending higher, both seem to have turned downward around the time ND implemented the mandates. No divergence so far. However, it is important to note that infections take around a week to be counted as a case, so any effect would not show yet. (2/5)
Second, hospitalizations: This also seems to have stabilized and turning downward in both states, although it is more pronounced in ND. (3/5)
Read 5 tweets
Prof Profile picture
20 Nov
Many times, we go see a doctor or take a pill when we are feeling the worst, right before we would naturally be feeling better. Then we attribute the outcome to the intervention....

Like those who think lockdowns turned things around in Europe. (1/x)
Topol shows a few countries as examples to how "it can be done". He is missing one though, so I added that...

When these countries enacted restrictions, cases were already peaking. Lockdowns, again, did nothing. Below I show a few examples... (2/x)
UK enacted LD on Nov. 5. However, cases were flat since Oct. 24, and with a minimum 7-day delay between an infection and it being counted as a case, infections were stable since mid-Oct. Lockdowns were so effective that they worked two weeks before being implemented! (3/x)
Read 7 tweets
Prof Profile picture
19 Nov
"In places where there is modest SARS-CoV-2 transmission (like Denmark during these months), there is insufficient evidence to suggest wearing a mask as you go about daily errands will protect you from infection."

Let's do a thought experiment... (1/x)
Imagine you are in combat, wearing an armored vest, which covers the areas of your body you are most likely to get shot. There are not many enemies, and a small number of bullets are coming. What are the chances that you will survive? (2/x)
But remember, if there are enough bullets, one of them will eventually hit you in the throat. Now the enemy deployed a brigade and they are raining bullets on you. What are the chances that the vest will save you? (3/x)
Read 7 tweets

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