Category: COVID through 2022

All my various postings tracking the COVID-19 pandemic through the end of 2022.

Post #941: No peak yet in new COVID-19 cases.

Source:  Calculated from NY Times Github COVID-19 data repository, data reported through 1/6/2020.  Thin lines are six broad geographic areas, thick blue line is the US average.  Circled areas are artifacts of Thanksgiving and Christmas/New Year’s holidays.  

At this point, enough of the data reporting anomalies from the holidays have (arguably) passed that I can now start to talk about trends again.  That’s not 100% certain, but the remaining data reporting issues are probably small enough that they don’t obscure the underlying trend.

And, as near as I can tell, the trend remains up.  Somewhat.  There is no peak, yet, for the U.S. coronavirus third wave.  Right or wrong, the continuation of the upward trend, marked by a question mark above, will be referred to as the post-Christmas surge.  But the underlying picture is complex.

Continue reading Post #941: No peak yet in new COVID-19 cases.

Post #940: Seroprevalence surveys

Source:  Calculated from supplemental data, round 4, Bajema KL, Wiegand RE, Cuffe K, et al. Estimated SARS-CoV-2 Seroprevalence in the US as of September 2020. JAMA Intern Med. Published online November 24, 2020. doi:10.1001/jamainternmed.2020.7976

I’m still trying to nail down the actual fraction of the US population that currently has antibodies to COVID-19.  Bottom line is that the best you can do is take a conservative, educated guess.  I’ll be assuming a 5-to-1 ratio of total cases to diagnosed cases.  The rationale for that follows. Continue reading Post #940: Seroprevalence surveys

Post #939: Holiday air travel

Source:  Calculated from case counts in the NY Times Github COVID data repository.

Above, the colored lines are plots of new COVID-19 cases/ 100,000 /day in six broadly-defined regions of the US.  The thick black line is the U.S. average.  The broad “dips” in that black line are artifacts of the holidays.  The first is Thanksgiving, the second is the year-end (Christmas, New Year’s) holidays.

A lot of things affect reported COVID-19 case counts around the holidays.  In some cases, public health departments simply shut down or are short-staffed, resulting in extremely low counts on the holiday itself, and high “catch-up” counts in the following days. Presumably, those should be a wash, more-or-less, within a few days of the holiday.

But as I noted earlier (Post #929), you actually see a temporary-but-real dip in diagnosed COVID-19 cases.  Above, you see two of them now — one for Thanksgiving, one for Christmas/New Year’s.  My best guess is, people “on the margin” with low-severity cases don’t bother to get diagnosed during the holidays.  But that’s just a guess.

With all the ups and downs of the data, it’s not obvious that such a thing is happening.  When this first occurred, at Thanksgiving, I didn’t notice it.  I only noticed that when I tried to “smooth out” some of the Christmas anomaly, and couldn’t.  And then found that the same thing had happened at Thanksgiving.  And then realize that the dip in the case count is a consequence of the holiday.

I have crudely filled in those dips, above, with dotted lines that continue the immediate pre-holiday trend. Because I think that’s the true story, if you’re trying to figure out what the trend is, right now.  You can’t take either that sharp downslope into the dip, or sharp upslope out of the dip, as an indication of the likely trend.  All you can do is wait until you’re passed the dip, to see where you’re going.

Which finally gets me to my point.  I’m getting ready to look for a post-Christmas surge in COVID-19 cases.  In theory, that should start to show up between 12 days to three weeks after the start of travel and socializing for the holidays.

Which leads to the obvious question:  when does that period start?  With Thanksgiving, it’s such a brief holiday that this wasn’t really much of an issue.  Given how loosey-goosey this whole “surge” concept is, I wouldn’t do any real damage by simply starting the clock on Thanksgiving itself.

But for Christmas, when does that 12-day to three-week clock start?  The clock that will mark the point at which those “surge” cases should begin appearing in the data.

It seems like air travel for the holidays is the signature example of bad behavior during the pandemic.  We’re always being told about the high rates of air travel use over the holidays, and how that presages the forthcoming holiday surge.  (Which, if you follow this blog, you realize did not occur after Thanksgiving).

I finally decided to look up the numbers.  And got a surprise.  The vast wave of air travel that hits the U.S., just prior to the holidays?  It’s more like a ripple.

Here are the data from the U.S. Transportation Safety Administration (TSA) website.  This is from all “federalized” airports (which I think means all or nearly all that provide commercial passenger service).  I’m unclear on whether this counts screening services provided via contractors, but I believe it does.

This is the fourth quarter of the year, and the blue line is 2020.  The thin wavy line is the actual  daily data, and the thicker line is a seven-day moving average.  Around the Xmas holidays, the peak travel days are the Sundays — the Sunday before Christmas, the Sunday after Christmas, and the Sunday after New Year’s day.  (And so I answer my question:  The “holiday travel season” starts the Sunday before Christmas.)

But an interesting secondary point is that there is only slightly more air travel over the holidays than at other times.  Maybe the peak, crunch times are vastly worse.  But the average isn’t so different.  The 2020 baseline rate of travel (from October, say) is about 900,000 persons screened per day.  The Christmas/New Year’s travel is about 1,000,000 persons screened per day.

In short, for the 2020 travel season, we had maybe an additional 100,000 persons per day flying, for maybe 10 days.  Or about 1M excess air trips.  In the context of a population of 330M people, it’s hard to think that you could possibly identify the impact of additional infections from those additional flights, against the background rate.

(Don’t believe me?  Let’s do the quick and dirty calculation:  In the U.S., we’re now seeing about 65 new cases/ 100,000/ day.  Let me assume that each case is infectious for a generous average of 5 days prior to onset of symptoms.  (And that people who are obviously ill have the good sense not to fly.)  Let me take an estimated 0.32% chance of each infected flyer infecting any of 15 fellow passengers (based on this research on Chinese train travel)  When I sum that up, I get (100,000 persons/day x 10 days) (total additional passengers) x (65 x 5/ 100,000) (rate of boarding persons being infected) x (0.0032 x 15) likelihood of a boarding infected person passing on that infection = 156 new infections.  The direct effect of those additional 1M flights should be to add 156 infections to the US total.)

I’m not saying that it’s smart to fly over the holidays.  I’m just saying that there is no huge increase in air travel over the holidays.  Yeah, about 1M people per day were flying over the holidays.  But about 0.9M people per day were flying before the holidays.  And that, given how small that increment is, the direct effect of that air travel, on US infection totals, is not going to be noticeable.

I tried to find the relevant car travel data, but they haven’t been published for the last week of 2020 yet.  This is where you’d get the report on U.S. Interstate highway travel.

This is no way implies that there won’t be a post-Xmas surge.  But if a surge occurs, it’s pretty clearly crazy to blame it on air travel.  Air travel occurs all the time, and rises only slightly during the holiday season.

Post #938: Yet another series of posts on masks, part one

I posted something yesterday, chiding people for wearing cheap face masks, and in particular for wearing them poorly.  Apparently I hit a nerve with more than a few people, and I’ve been challenged to offer some practical advice.

What you are going to get next on this website is a series of posts on masks.  Solely from the standpoint of protecting yourself, not from the public health standpoint of protecting others.  Starting with some straight-up “buy this” practical advice, before I go off on a deep dive on the whys and wherefores.

But because most people don’t grasp the basic math of masks, I have to do the math first.  And that’s because mask ratings and mask performance tests hide the true relative risk of various types of masks.

 If you just want to get to the quick advice, just skip to the next section.  But you really ought to try to answer the question below.

Mask ratings hide the true relative risk of poor masks  versus good masks.

Here’s a simple question.  Even if you think you really, truly understand masks, take 15 seconds to see if you can get the correct answer.

Question:  An N95 respirator (mask) filters out 95% of airborne particles.  A procedure mask with ear loops filters out about 30% of airborne particles.   Let me loosely call that an “N30” mask.  Roughly speaking, how much better is an N95 mask, compared to an N30 ear-loop procedure mask?

A)  Obviously, it’s about three times better, because 30 x 3 = 90, which is close to 95.

B)  Obviously, it’s about 14 times better, because (100 -30)/(100 – 95) = 70 / 5 = 14.

C)  Obviously, this must be a trick question.

The answer is B, it’s 14 times better.  Why?  The mask rating (N30, N95) shows you what the mask keeps out.  But the viral load you inhale isn’t about what the mask keeps out.  It’s about what the mask lets through.  It’s about 1-minus-the-mask-rating.  And in any given situation, the ear-loop surgical mask will let through and expose you to 14 times as much viral load as the 95 mask.  Because 70% of what’s in the air is 14x as much as 5% of what’s in the air.

In case you still don’t quite get it, let me do the math the other way.  How much better is that N30 ear-loop surgical mask, compared to wearing no mask at all?

Question 2:  Assume that you need to inhale 100 copies of COVID-19, at a sitting, in order to get infected.  Assume that you are going to inhale one cubic meter of air, at a sitting.  How dense can the COVID-19 particles in the air be, before you inhale enough to get infected, based on wearing:

  • No mask.
  • N30 mask (ear-loop surgical mask, worn loosely)
  • N95 respirator.

Answer:

Edit 1/15/2021:  Question 2, same math, but rephrased.  Suppose there’s a room filled with COVID-19 aerosol.  Suppose that, without a mask, you can sit in that room for no more than 10 minutes before you get infected.  How much more time does your cheap, blue ear-loop surgical mask buy you?  That is, how long could you sit in that room and remain uninfected, wearing an ear-loop procedure mask? And then, how long wearing an N95 respirator?

Answer:

  • No mask — 10 minutes.
  • N30 mask (ear-loop surgical mask, worn loosely) – 14 minutes (10/.70)
  • N95 respirator — 200 minutes (10/.05).

Yep, that cheap blue mask buys you a whopping four additional minutes of time, before you get infected.  Which not only makes my point, but which shows you why you want to stay away from close, crowded situations, mask or no mask.

Sure, a loosely-fitting ear-loop surgical mask is better than no mask at all.  But not by a whole lot, in the overall scheme of things.

I hope you now get why I’m so persnickety about masks.  To the point of making my own, so I can be sure of what I’m putting on my face (Post #807, Post #780), and trying to test them (Post #790).  And why I continue to be irked about the inability of citizens to purchase true N95 respirators.  The difference between a good mask and a poor mask isn’t a little bit.  It’s a lot. It’s an order-of-magnitude difference in performance.

Edit:  And I’ll go you one better.  In at least one hospital here in Northern Virginia, the nurses serving the COVID-19 ward wear half-face N100/P100 respirators. Like the one below.  Because if you’re really heavily exposed, allowing even 5% of viral particles past your respirator just won’t cut it.

Source:  Amazon.com.

 

If you just want some quick advice on a reasonably good mask to wear.

I’m not going to go even one inch into all the details.  Fact is, there is a mask, that you can buy, that is easy to wear, and that did very well in a realistic test, by real scientists, published in the Journal of the American Medical Association.  In terms of some quick advice, on what to wear, that’s about as bulletproof as it gets.

The recent test of masks published in the Journal of the American Medical Association found that two-layer nylon masks filtered out about 80% of airborne particles, once the masks had been washed (Post #924, or you can try to pull up the tables in the JAMA article itself).

Because this was actual scientific research, they specified the mask fully as: “(1) a 2-layer woven nylon mask (54% recycled nylon, 43% nylon, 3% spandex) with ear loops (Easy Masks LLC) tested with an optional aluminum nose bridge and nonwoven filter insert in place.”

Click here to buy those exact masks from the manufacturer’s website.  (To be clear, I have no financial interest in this whatsoever.  Also, the JAMA test achieved near-80-percent filtration without use of the nonwoven insert.)

I honestly don’t think there is anything unique about those masks, within that specification.  Except that they are made correctly.  If you go to the website, you will see that they are generously cut, and cover the face from throat to eyes, ear-to-ear.  And that they make them in different sizes, and they tell you how to measure your face, to choose the right mask.  And they make small ones for kids.

If I had to bet, I’d say that this particular North Carolina firm’s masks were chosen because the principal author and all of his colleagues are from North Carolina.  And because the manufacturer seems to do everything more-or-less correctly.

At some point, I’ll belabor exactly why this is a reasonable choice.  But for now:

The upshot is, based on their cheapest mask, for under $20 (including shipping), you can get two copies of the mask tested in that JAMA article.  It’s roughly an N80 after washing.  You can reduce your exposure to airborne virus 3.5-fold, compared to a standard ear-loop surgical mask. 

With no fear of counterfeits.  With an actual legit test of that exact mask, on the books.  Easy-on, easy-off.  I’m sure if you did your homework, you could find well-fitting two-layer nylon masks for less.  But at some point, it’s not a lot of money, given what’s at stake.

Is this the best mask you could possibly use? No.  You can see where this sits on the scale of risk, in the graph below (redone from above).  At least you can see the difference without using a ruler to measure the bar.  But is this a substantial upgrade, if you’re still using disposable ear-loop surgical masks?  Yes, the odds are overwhelming that it substantially out-performs a blue disposable ear-loop mask.

Be sure to wash these before you wear them.  Filtration improves greatly after washing.

And if you insist on using up that pack of blue masks that you bought, look into the tied-and-tucked method for improving the fit and filtration of those masks, in this YouTube video.

Post #937: It’s déjà vu all over again, I hope.

Thanksgiving is a relatively brief holiday interval.  Per Post #909, that introduced a short-lived artifact in the data that looked like this:

When that arrived in the data, my reaction was a) shock, and b) a sincere hope that this was some transient effect of Thanksgiving.  Which it in fact turned out to be. Continue reading Post #937: It’s déjà vu all over again, I hope.

Post #935: If you have 10-cent lungs, by all means, wear a 10-cent mask.

Source:  Carnegie-Mellon Delphi Group Covidcast.  This is based on an ongoing Facebook survey of mask use.  That website has a wealth of very current information related to COVID-19.  The still images are early September (left) and December 31 2020 (right).

I once had a friend who was a motorcyclist.  And, separately, was just about as big a cheapskate who ever walked the earth.  (And I say that with the utmost admiration.)  Except that he always wore a top-of-the-line full-face Bell helmet.  When I asked him why he spent the big bucks, he said “If you have a $10 head, wear a $10 helmet.”

To which I have now added my ten cents.

This post is just a short rant on masks.  The point is simple enough.

Getting people to wear a mask is a good start.  Getting people to wear a good mask may be more of a challenge.  Or, at least, that’s been my observation in Northern Virginia, of late. Continue reading Post #935: If you have 10-cent lungs, by all means, wear a 10-cent mask.

Post #934: COVID-19 trends through 12/31/2020

This is just a final set of state-level trend graphs through the last day of 2020.  At this point, you can see a clear Christmas artifact in the data reporting.  It’s a pretty good match, for depth and duration, to the reporting artifact that appeared just after Thanksgiving.

If I had to pick one word that has been over-used in reporting on COVID-19, it would be surge.  Seems like everything is a surge these days.  That notwithstanding, it’s too soon to start looking for any “post-Christmas surge” in cases.

As of the end of the year, it still looks like California has peaked, and that South in general is seeing a mix of slowly rising and slowly falling rates. Continue reading Post #934: COVID-19 trends through 12/31/2020

Post #933: The CDC’s eight-times estimate.

This is a post about the count of U.S. residents who have had a COVID-19 infection, but were never formally diagnosed.

To get to the bottom line, the answer is, lots.  There are lots of people who have had a COVID-19 infection but have not been diagnosed.  The number of actual COVID-19 infections is several multiples of the cases that have been formally diagnosed.  But if you want much more accuracy than that, I think you’ll be disappointed.  Particularly if you want a state-level estimate, instead of one for the U.S. as a whole. Continue reading Post #933: The CDC’s eight-times estimate.

Post #932: The new British strain of COVID-19.

With so much bad news in my face every day, it’s hard to tell what’s really dangerous from what’s just run-of-the-mill awful.   I get to the point where I just shrug stuff off if it doesn’t directly affect me and mine, here and now.

They’ve been having an exceptionally tough pandemic December in the United Kingdom.  New lockdowns, border with France closed, case counts rising.  And that has something to do with a new, worse strain of COVID.

But so what.  Things are tough all over.  How bad can it be, really? Continue reading Post #932: The new British strain of COVID-19.