It is known that the 2 does series does not really protect all that well against Omicron. So, from the reporting of “breakthrough cases” — we actually can’t really tell how well the *actual* “fully vaccinated” fare against covid.

We know that 79% of MA residents have received at least the 2 shot series. We know that 56% of *those* (meaning about 44% of the entire population) have received at least 1 booster.

So, here’s the problem: I can calculate that in MA, based on the reported numbers of “breakthrough cases’, the “at least 2 does” is about 32% effective at preventing COVID-19 infections*. What I *can’t* tell is that, of those that got infected, how many of them were boosted vs how many not? We don’t know.

* Caveat: it’s possible that the vaccine isn’t preventing COVID, but there is in fact a common cause that is correlated to both vaccination and uninfection — like people actively trying to avoid COVID by not going out, etc. That is surely some part of the apparent success of vaccination. I doubt that it’s most of it.

TL;DR: In Massachusetts, COVID has been rising and the current stall might not be real. Stay away from big crowds (> about 30 people).

Surprising no one, the end of countermeasures allowed COVID to start rising again (actually, it started rising a bit before the end, which makes ending them even less sensible).

I need to make a long post about my methodology and the way I look at all of this. This is not that post. For now, suffice it to say that I have a spreadsheet where I calculate the risk of becoming infected based on the current numbers (infection rates for both unvaccinated and vaccinated and vaccination rates), and then I apply it to a risk budget based on being able to tolerate a 1 in 10 chance that I will get COVID in a year. (This is *my* risk tolerance. Your risk tolerance might be different, particularly if you are older or have higher risk factors for severe impact.)

Many, many caveats apply, but I find it a good guide, and my results are generally compatible with other sites out there doing similar things.

Here’s the current summary: COVID has been on the rise for weeks. It *looks* like that rise sort of stalled over the last week, *BUT* cases are typically undercounted on holiday weekends, and last weekend was a big holiday in MA. So, that stall is quite probably an illusion and things will continue to rise.

Right now, my spreadsheet says that I can have “close contact” (within 6′ of someone for > 15 minutes in 24 hours) with 29 people in a day, rising to 41 people if I know that they’re vaccinated.

Big caveat: a good bet is still a bet. I’m playing the odds, but that’s not a guarantee.

TL;DR: we can expect it to get more contagious, and we don’t really know where the wall will be on that, but there is one.

We can expect it to continue to evade prior immunity (which is a different thing), and the wall on that is much further away.

We don’t know whether it will get more or less severe.

Notably absent from the article is any prediction of long term effects. For example, polio has only about 1/10th the immediate death rate of COVID, but about 30% of recovered people get some level of paralysis 15-30 years later. (Disclaimer: we have no reason to believe it acts like polio, but it already doesn’t act like many other coronaviruses, which do not really have long term effects.)

According to recent data from Scotland, unvaccinated people are 7.2 times MORE likely to be hospitalized because of covid than people with all 3 shots.

Why I looked at this.

This morning, I ran into this article, in which an excellent writer with a lot of passion argues against vaccinations. She also cites her sources, which I really appreciate, so we can look at the same data.

Unfortunately her math skills are not up to her writing, as she failed to grasp the meaning of her sources.

Perhaps she missed this part

I’m not going to go through every claim and every source, but her first one yields an interesting (if unsurprising to my regular readers) result.

The Claim

Here’s the first claim:

"The data is now overwhelming. In Scotland, in England, in Iceland, in Canada. Double-vaccinated are the MOST likely to be infected. Un-vaccinated are the LEAST likely to be infected."

Her cited source for Scotland: https://publichealthscotland.scot/media/11223/22-01-19-covid19-winter_publication_report.pdf

Unfortunately, her cited source did NOT contain the graph she showed about case rates per 100,000k, so I’m just going on the document itself.

The Data

What her source says:

Of people admitted recently because of covid, 40% have had 3 doses, 29% 2 doses, 4% 1 dose, and 27% were unvaccinated.

This kinda looks like what she said, but let’s reiterate:

What’s Missing

What she left out:

In Scotland, according to her source, 92% of people have received at least one dose, 85% have received at least 2 doses, and 67% of people have received all 3.

So, her claim is sort of correct in the raw numbers, but incredibly wrong in the conclusion.

It probably would have made more sense just graphing the vaccination and hospitalization percentages, where red taller than blue is bad.

But it really becomes clear when you look at the relative risk of the populations (i.e. divide the hospitalization percentage by the vaccination percentage.)

The Analysis

For the calculations below I’m going to look at the adult population only in the rest of this, because chlidren are rarely hospitalized from covid (not that they can’t be, just…so far, it’s rare). I’m estimating the adult population by taking the population and subtracting out an estimate of the children.

So, of the adult population of Scotland,

The unvaccinated 7% of the population make up 27% of the cases admitted for covid, the 18% with 2 doses make up 29% of the cases, and last the 72% of the population make up only 40% of the cases.

Adding in the adult population of Scotland at 4,545,465 and the most recent weeks hospitalization data of 1040 (from her source), this works out to an unvaccinated person having 7.2x the risk for covid hospitalization as a fully vaxxed person. There is no category at lower risk for hospitalization than fully (all 3 doses) vaccinated.

Further claims fell victim to similar problems. I’m not going to do the math in detail on those here because this will get way too long and boring.

The author is clearly an excellent writer and very passionate, but equally clearly a bad mathematician.

The Conclusion

Her conclusions are the opposite of reality, the opposite of what her source showed, and the opposite of the conclusions stated in the source that she used.

(I spot checked other claims and found similar problems, but did not work through the full math on those.)

Footnotes

You’ll note that it appears that having only 1 dose of the vax is safer than having 2 doses. This is a great example of the difference between causality and correlation.

Data like this is where reachres go: "Gee, that’s funny….I wonder why…"

Then they start going "maybe it’s because…" and "how can I test that theory…"

I’ll note that the percentag of people in Scotland who have 1 dose but not 2 is the lowest of any category. It may very well be that these are the most isolated and difficult to reach, which implies lower vaccination rates…but also lower covid exposure.

Which is it? I don’t know. There are obviously a few theories, and the people whose job it is to do this stuff are likely looking into it.

TL;DR: It hasn’t, yet. Expect Omicron to peak some time mid to late January. Severity looks lower, but we don’t know how much. South Africa is probably not a good predictor for the US, but the UK probably is.

Here is an article by Dr. Jeffrey Shaman, an epidemiologist at Columbia University. Basically: what he said.

Like many profesionals in the prediction business, he’s being very cautious in his predictions. I’ve been preparing to write something very similar, so just posting his is me being constructivly lazy

Also, the dude’s got serous creds as well as a team backing him up, so the agreement with my course numbers based predictions is personally satisfying

I’ll be giving a bit more details in my own analysis soon. As a preview, based on first pass, quick, casual caulcuations for Massachusetts (subject to change as I look deeper!), it looks like Omicron may result in about 1/3rd to 1/2 the death rate, but have at least 3x the case rate. This lower severity surely involves vaccination rates (about 75% of all ages in Mass, and 93% of the most at-risk ages). This is not necessarily going to be what the entire country experiences.

Also, expect death rates to increase as we stress medical systems, which is not something that the good Dr. Shaman gets into.

I’m posting this just so everyone can see how I’m looking at cases and hospitalizations and trying to figure out how bad Omicron is.

When checking hospitalizations, the important thing is not how COVID is spreading *today*, but how it was spreading about 10 days ago.10 is a magic number with COVID. *Most* people will have recovered after 10 days. The (fairly large, with Delta) minority who end up in the hospital really start entering after 10 days. It’s like a fork in the road.

So, the really interesting thing for Omicron is figuring out what percentage of people take that fork. Doing that requires looking at how many are taking the fork, and how many got on the road a while back. How many are getting on the road today isn’t interesting, because they’re *not* at the fork, yet.

Additionally, data is presented very differently. What everyone loves to report is number of new cases. That’s great, but that’s like asking someone “where are you” and getting the answer “I’m going 50 mph”.

For hospitalizations, everyone loves the number of people *in* the hospital. That’s like asking “how many people are going to McDonald’s?* and getting the answer “well there are 20 people there now”. Of course, since McDonald’s has a maximum seating capacity, we *do* care about the number we’re given. When that max is reached, if new people keep arriving they’re going to be standing around hungry. “Hungry” is really bad in this metaphor.

These things are related by calculus. The relationships are reasonably simple, but most people don’t want to deal with the calculus. As it happens, these things are actually the right things to compare

.Most people also don’t want to deal with that 10 day delay. That delay is “particularly* important when we’re observing a new variant and asking “how bad is this one?” Most sites do NOT give this information in a way that’s easy to compare.

So, here are some graphs of these numbers (for Massachusetts). You’ll see that they’re for 2 different date ranges. This is the correct comparison. I’ve also picked the time after Omicron was detected in Massachusetts, so theoretically our numbers should be seeing the spread.

What we’re looking for is the *shape* of the graphs. Because people heal from COVID, if the new case rate is constant, the number in the hospital is *also* constant (roughly). So, if we see new cases increase on a straight line, and 10 days later we do NOT see hospitalizations increase, we say “this variant doesn’t put people in the hospital”. Of course, people don’t always do things things at the same rate, so the lines aren’t nice and smooth. We do things to the data to try to smooth it out but that means we’re increasing the delay even more. Mostly this data is presented as a rolling average. Rolling averages are great at reducing the impact of a single bad day but they obscure an actual change in the rates for a while.

So, after all this, here are the graphs of new cases and hospitalizations. What I’m looking for is hospitalizations to NOT increase like new cases. I would like this — it would mean Omicron is not so bad.And this leads us to the problem: the data isn’t clear, yet. Is that rise in the last few days just random “shit happens”? Or, is it hospitalizations following the new case rate?If we behave like the UK has, it’s the former.

Here’s how I’m looking at this: both graphs have a spike on the right side. That’s troubling. The new case rate was somewhat flat while hospitalizations we’re increasing. What? Well, I’ve read something about Omicron actually infecting (and presumably hospitalizing) people faster than Delta, so maybe that 10 days is actually 8 days now…hmm.

I can’t really tell what *is* happening, but that’s pretty common. So I look for what I think might be happening and try to support it:IF Omicron has MUCH lower hospitalization rates than Delta and IF it has the same progression times, and IF the time period I’ve captured shows the spread of Omicron, THEN I should not be seeing hospitalization rates spike up significantly.<Looks at data> Nope, I can’t say that. I see a spike. That means I *cannot* support my theory that Omicron is better than Delta.

So, what might be wrong?

Well, I’ll have to look at when Delta was spiking, and see the hospitalization rates there (this would be the theory that Omicron is a bit better, but not amazingly better). Maybe Omicron has different progression, so 10 days isn’t the right delay. (But those spikes seem to line up well.)And, last…maybe I just need more data to get a more clear picture. That’s kinda where I am right now.

What does more data look like? It means longer periods of time with Omicron spreading so the averaging works better

.It also could mean looking at a different result, like death rate. Death rate lags new cases by about 18 days, so we aren’t even close to having that data, yet.

Check back in around Jan 12th to see how many people we’ve killed, and we’ll be able to see if all those optimistic holiday gatherings were actually a good idea.

DO

• use a rapid test to tell if the symptoms you have are covid
• use a rapid test to tell if you are or are not contagious RIGHT NOW (but that can change in as little as 6 hours)
• Use a PCR test to tell you whether or not you had covid 5 days ago

DO NOT

• use a rapid test to show you do not have COVID.  The negatives are not accurate, particularly when you have no symptoms.
• use a PCR test (or any other test) to test if you got it from exposure yesterday.  A PCR test will not be accurate until about 5 days after exposure.
• do not assume you are not contagious if you’re not feeling symptoms.  You’re contagious for probably 36 hours before you feel it.

The Short Version

A rapid test CAN tell you if you’re contagious at that moment.  It’s pretty reliable, but not so reliable that you should go breath on someone at risk.  But you can probably go hang out at that party if everything there tested negative on a RAT test right before coming.

A rapid test CAN tell you if the symptoms you have right now are COVID.  At any time, whether you have symptoms or not, if it tells you that you have COVID, you almost certainly have COVID.  

A rapid test CANNOT tell you you do NOT have COVID.  COVID could be incubating in your body and just not be enough for a RAT test to measure.

If you’ve been exposed but don’t have symptoms, you need a PCR or molecular test 5 days after exposure to know if you have COVID.  A PCR takes a while after exposure to have valid results, but the results are definitive, positive or negative.

Omicron usually takes about 2-4 days between exposure and symptoms.  (Delta and prior about 5 days, usually).

You are most contagious from about 24-36 hours before you have symptoms to about 48 hours after.

Slightly longer version

Rapid tests really measure the amount of virus in your nostrils.  This correlates pretty well to being contagious.  If you have symptoms, then you’re probably contagious (some vaccinated people can have symptoms and not be contagious).

They are only accurate if you have symptoms, and even then a negative should be retested several days later (their false negative rate is very high, false positive fairly low). If you do NOT have symptoms, a negative rapid test is NOT useful to determine if you have COVID. Notably, you CANNOT use a rapid test to determine if an exposure gave you COVID before you feel it coming on. 

As of 6/2022, expert belief is that a rapid test can determine if you are contagious at that moment (and that can change in 6 hours).  So, it’s good practice to test before going to e.g. a large gathering.

PCR (molecular) tests can show you if you have COVID before you have symptoms, but you do need 5 few days after exposure for the test to be valid. If you’re exposed in the morning and test that evening, the results are useless as the virus hasn’t had time to replicate enough to register.

By the time you get the results back for a valid PCR test, you’ve probably already been contagious for at least 24 hours.  Mask up and avoid high risk people at least (best to avoid all people) until you get the negative results back.

So, if you’ve had an exposure, CDC exposure guidelines are to get tested 5 to 7 days post exposure. (They day of exposure counts as day 0, not day 1.) However, unlike rapid tests, the results are highly accurate both negative and positive, assuming you’ve waited long enough.

A note on contagion: You are probably contagious about 36 hours before you show symptoms.  You are most contagious from this time to about 48 hours *after* you show symptoms.  IF you are fully vaccinated (including booster!), you are probably not very contagious 5 days after symptom onset, but may be *slightly* shedding virus (and therefore slightly contagious) up to 10 days after symptoms.  You can probably consider yourself “recovered” and no longer contagious after a rapid test comes back negative.

What’s The Difference Between COVID-19 Rapid and PCR Tests?

Updated: 6/15/2022