Post #1429: COVID-19 trend to 2/8/2022: Steady trend, continued rise of son-of-Omicron, and better the devil you know than the devil you don’t.

 

New COVID-19 cases continue to fall in the U.S. We’re now down to 74 new cases per 100K population per day.  The 7-day rate of decline is 44%, not materially different from yesterday’s 45%.

Data source for this and other graphs of new case counts:  Calculated from The New York Times. (2021). Coronavirus (Covid-19) Data in the United States. Retrieved 2/9/2022, from https://github.com/nytimes/covid-19-data.”  The NY Times U.S. tracking page may be found at https://www.nytimes.com/interactive/2020/us/coronavirus-us-cases.html

My gut reaction to today’s new data is that we’d passed the point of most rapid descent, and that the rate of decline will now begin to taper off, as we have seen in other countries.  As far as I can tell, there is no objective information to support that.

Here are the ten states that have led the way in the U.S. Omicron wave, plotted in logs so that a constant percentage decline shows as a straight line.  If the ends of those lines are bending toward flat, it’s a pretty subtle effect so far.

 

If there is a dark cloud on the near-term horizon, it’s the BA.2 variant (a.k.a., son-of-Omicron).  The CDC “Nowcast” projection pegs this at 3.6% of all U.S. cases, as of the week ended 2/5/2022.

Source:  CDC COVID data tracker, accessed 2/9/2022.

You might reasonably say, what’s the big deal?  BA.2 doesn’t appear to be any more virulent than Omicron, but it is more easily transmitted.  Original estimates were that it was about 50% more transmissible than Omicron, but more recent estimates put it closer to one-third more infectious than Omicron (reference).  Results from both Denmark and the U.K. appear to agree on that figure (reference).  If it becomes the prevalent strain, that will result in just so many more infections, all other things equal, and slow down our presumed “return to normalcy”.

Well worth noting, the CDC “Nowcast” model shows this strain’s share tripling every week.  On the one hand, you have to take that with a grain of salt, given the small numbers and the uncertainties involved.  On the other hand, if those projections are right, and accounting for the timing of the CDC data (that 3.6 figure would have been reached somewhere around 2/2/2022, BA.2 would be come the dominant strain in the U.S. approximately 2/19/2022, or ten days from today.  But weigh that fear-mongering against the observation that the growth of BA.2 has been all over the map, in various European countries, and that no country actually saw its share of BA.2 grow that rapidly.  My guess, based on European growth rates, is that this preliminary “Nowcast” estimate likely overstates the true rate of growth. 

The fact remains that BA.2 took over Denmark in a matter of weeks, but so far it remains a minor issue in most European countries.  Coincidence or not, it’s worth noting that Denmark still hasn’t reached a clear peak in its Omicron wave, despite having started that wave before the U.S. did.  Draw what conclusions you will from that sample of one.

Source:  Johns Hopkins University via Google Search

Finally, there is a bright side to transitioning from pandemic to endemic with as infectious a variant as possible, as long as that variant is relatively benign.  There’s no guarantee what the next new variant will bring.  Maybe the next one will be as virulent as Delta, or worse.  But that won’t matter unless that new variant is better able to spread better than the reigning champion, which will be either Omicron or son-of-Omicron.  The tougher those are to beat, from an infectiousness standpoint, the less likely we are to have some new and possibly far more virulent strain of COVID-19 take over.

It is truly a case of better the devil you know than the devil you don’t.  Looks like we will probably be able to deal with Omicron OK.  Plausibly we can deal with BA.2 OK.  Both owing to their much-reduced infectiousness and the ability of a booster shot to provide significant protection.  It’s probably better if we can stick with this variant as our endemic COVID-19, rather than roll the genetic dice and face something new.

Post #1428: COVID-19 trend at William and Mary through 2/7/2022

 

I’m going to continue to track Omicron for a while longer yet, at William and Mary.  William and Mary is only updating their data weekly so I guess I’ll post once a week, if there’s anything to write about.

I doubt that many will read these posts.  Most  W&M parents found out about this blog via a W&M-moderated Facebook group.  The moderators of that group have banned discussion of COVID-19, so I suspect that most on that group will never get back to this blog.

Enough said.  Most of the value-added here is that I keep track of the historical data.  That’s not available on the W&M COVID dashboard.

Based on two weeks of data, the new case rate at William and Mary seems fairly steady at 5 to 7 newly-diagnosed COVID-19 cases per day.

That most recent rate of 7-per-day translates to roughly 100 new cases per 100K students per day.

Contrast to Virginia as a whole:  That’s just a touch higher than the rate for 18-24 year olds in Virginia as a whole, which I estimate to be 77 / 100K / day, for the seven days ending 2/7/2022, based on Virginia Department of Health data.

Contrast to last semester:  That’s maybe four times higher than the peak post-move-in rate last semester.  After the initial post-return-to-campus wave had died off, the highest rate observed was 11 new cases in the week ending 12/10/2021.  That’s under 2 per day on average:

That said, Omicron has a much lower average severity than the Delta variant that was prevalent at the end of last semester.  For example, looking at the Virginia data, if I date the start of the Omicron wave to roughly 12/17/2021, then so far there have been zero deaths in the 18-24 age bracket, from Omicron, in the whole of Virginia.

If I had to sum it up, I would say two things.

First, there does appear to be some need for further vigilance.  COVID-19 has not disappeared from the campus.  That 100 cases per 100K per day rate is high by historical standards.  And I need to remind myself that these numbers didn’t just happen.  The relatively high new case rate occurred in an environment with fairly stringent COVID-19 hygiene in place.

That said, the rate at William and Mary isn’t much different from the rate for Virginians age 18-24 as a whole.  And once you factor in the much lower average severity per case, the health impact of the current case rate probably isn’t much different from the impact of Delta at the end of last semester.

In any case, the numbers are up from last year.  I see that as no cause for alarm, but no clear justification to relax, either.

Finally, I should note the obvious:  Pre-return testing seems to work pretty well at preventing a big return-to-campus outbreak.  This isn’t exactly a controlled experiment, but the one semester when W&M didn’t do pre-return testing, they ended up with a large number of active cases on campus, as shown in the last chart above.  The semesters when pre-return testing was required, that didn’t happen.

Post #1427: COVID-19 trend to 2/7/2022, linear descent continues

 

The U.S. is now at 78 new COVID-19 cases per 100K population per day.  That’s down 45 percent over the past seven days.  Since late January, that number has fallen by a more-or-less steady 10 cases per day.

Cases have now fallen by more than two-thirds since the 1/16/2022 peak of the Omicron wave.

Continue reading Post #1427: COVID-19 trend to 2/7/2022, linear descent continues

Post #1426: Nobody’s going to tell you when to stop wearing a mask. And, yet a third way to triangulate Omicron risk versus flu risk.

 

Let me start with an anecdote.

I went to two different farmers’ markets over the weekend.  These were open-air markets, sparsely attended.   And it was a breezy day, to boot.

In neither case did I think there was any reason whatsoever to wear a mask.  In both cases, I ended up wearing a mask.  Not because I thought it made sense, but because I wanted to fit into the crowd.  In both cases, the overwhelming majority of people in the marketplace were masked.

I’ve now gone full circle on pandemic mask use.  Early on, I couldn’t fathom why people weren’t using face masks.  Now, at least in some cases, I can’t fathom why they are.

In today’s news, I see that two East Coast Democratic governors have set a rough timetable for rolling back K-12 school mask mandates (per this reporting.)  This is in response to the declining new case counts for Omicron.  Right now, it looks like both of them are shooting for a March end of their respective school mask mandates.

In my humble opinion, that’s how it should be done.  They are accepting responsibility for this key part of school safety during the pandemic and they are actively managing it.  They are planning an orderly transition from masked to non-masked in-person K-12 instruction, based on what I hope are public health objectives.  In any case, for this group activity we call school, everyone in the group will be given the same signals regarding mask use, and when it’s time to take off the masks, everyone can be assured that the decision was done with some forethought as to the common risks involved in that.

This is good, if only because this approach avoids chaos and strife.  Within the large-group activity we call public school, everyone will get the same set of instructions.

Contrast this with the Virginia approach, where the Governor took no ownership at all over this key school safety issue.  Instead of managing the transition for the benefit of all, he issued an executive order to create individual parental-based exceptions to local mask mandates.  It allowed parents to exempt their children, one-by-one, based on their opinions (i.e., political leanings) rather than any public health criteria for the student body as a whole.  As an extra added bonus, it fairly clearly conflicted with existing Virginia statute, and as a result it has now ended up in court.

Which is bad, unless generating chaos and strife is part of your political agenda.

At any rate, because K-12 school is a group activity, run by the government, there likely will be some form of guidance in most places.  Some sort of mask use guidance.  If only because in most places, somebody responsible is supposed to be looking out for the health and welfare of the students as a whole.

We adults are not so lucky.  We’re each going to have to make our own individual decisions about mask use.  And as far as I can see, so far, that’s going to be based on the same amorphous social norms that governed masking up in the first place.  To the extent that dropping the masks will be enforced, it’ll be through peer pressure, not through any explicit advice from any government agency.

In short, as an adult, nobody’s going to tell you that masks are no longer needed. 

Not in the U.S, at any rate.  Other countries seem to be testing the waters for treating Omicron like seasonal flu, e.g., Spain.  But here in the U.S., I doubt the CDC is ever going to come out and say that masks should no longer be used.


Yet a third way to compare Omicron risk versus typical flu risk:  Brief background.

Let me not belabor this.  This is the third in a series of posts that asks the following question:

How low does the U.S. Omicron case load need to get, before the risk posed by Omicron is no higher than that posed by typical seasonal flu?

In Post #1400-3, I did the crude calculation for all persons pooled together, and came up with 16 new cases per 100K population per day.  That calculation was absolutely straightforward and easy to check.

In Post #1400-4, I refined that by generating a separate estimate for the boostered population alone.  That gave me a benchmark of 40 new COVID-19 cases per 100K population per day.   The number is higher because boosters provide significant protection against Omicrion.

But that calculation was anything but transparent.  And it was an extremely conservative estimate, in that I only account for the impact of vaccine and booster on the likelihood of getting infected, not on any further reduction in hospitalizations or deaths once infected.


A duh-piphany, or the most obvious way to compare Omicron risk to flu risk.

The main point of this exercise is to compare the risk of hospitalization or death under Omicron, to hospitalization or death from seasonal flu.

In which case, why don’t I .. uh … just compare those rates directly?  If I want to compare them based on deaths and hospitalizations, then simply do that.  Tabulate Omicron hospitalization and death rates on a 100K population basis, and compare those to typical seasonal flu.

It’s an eclat d’oh.

I mean, when the Omicron hospitalizations per 100K gets down to the level of flu hospitalizations per 100K, then by definition, the average person’s risk of getting hospitalized for Omicron matches the risk of being hospitalized for flu.  No further calculation needed, unless you want to try to separate out the boostered, vaccinated, and un-vaccinated populations.

Disease burden of flu in terms of deaths and hospitalizations per day and per 100K population.

I want to compare Omicron risk to risk from flu on a typical day during “flu season”.

The first issue is that I could not find any hard-and-fast CDC definition of flu season.  It’s just defined as the months — typically winter through early spring — around the peak of this curve.  Typically, somewhere around five months out of the year.

Source:  Calculated from CDC burden of flu, 2017-2028 season, assuming 30M total symptomatic flu cases for the entire year.

I’m going to define “flu season” as those weeks with an estimated 500K symptomatic flu cases or more.  In the example above, “flu season” lasted 18 weeks, and accounted for just about 70 percent of all flu cases during the year.

Source:  CDC disease burden of flu.

Based on that, and rounding the numbers, I come up with the following table comparing hospitalization and mortality rates for typical U.S. seasonal flu and the current levels of Omicron:

On a typical day in flu season, the U.S. sees 2100 flu hospitalizations.  Currently, with Omicron, we’re seeing an estimated 12,000 hospitalizations per day.  Based on that, for the U.S. as a whole, Omicron cases would have to fall to about 17.5 per day before the hospitalization risk from Omicron matched that of typical seasonal flu, for the average American.

(The mortality data are harder to use because a) deaths lag cases by a couple of weeks, and b) we’re only a few weeks past the Omicron peak.  So, compare to the current case count, we’re looking at far too many deaths.  And, accordingly, the ratio of current Omicron deaths to typical flu deaths is much larger than the current ratio of Omicron hospitalizations to typical flu hospitalizations.)

As you can see, all I have really done is re-create my first analysis.  Pooling all individuals together, you’ll have the same hospitalization risk for Omicron as for flu if Omicron gets down to 17.5 new cases / 100K / day.  (My initial analysis came out with 16 new cases / 100K / day).

The only value-added here is that this now directly translates into a COVID-19 daily hospitalization count.  That information is available on a timely basis for all states, via the CDC COVID data tracker.

For now, I’m just going to leave it at that.  Without being very precise about it, this is just another way of saying that at some point when Omicron cases get into the 10’s per 100K per day, your risk of severe illness from Omicron is no higher than your risk of severe illness from flu.

Tomorrow, I’ll take the final step in this process.  I’m going to combine and clean up all the results, and translate them into a set of state-level thresholds comparable to the data publicly available on the CDC COVID data tracker.  With that, you should be able to take those thresholds, bring up the CDC data from your state, and identify the time (if any) at which Omicron risk is below typical flu risk for the average resident, and for the fully-boostered resident, of your state.

Post #1425: COVID-19, last update for this data reporting week.

 

The U.S. COVID-19 case numbers continue to be surprisingly good.  The decline in new COVID-19 case counts continues to accelerate.  The U.S. now stands at 100 new cases per 100K population per day, down 40% in the past seven days.  Most states are now below 100 new cases / 100K / day.

Continue reading Post #1425: COVID-19, last update for this data reporting week.

Post #1424: COVID-19 trend to 2/3/2022, still looking good.

 

The U.S. now stands at 111 new COVID-19 cases per 100K population per day.  That’s down 38% in the last seven days.

The rate of decline has been in that neighborhood for the past three or four days now, and this may well as fast as the decline gets.

Now the big question becomes “where will it stop”?  At what level will Omicron continue to circulate in the population as “endemic COVID-19”?

The good news is that of the ten states that peaked earliest in the U.S. Omicron wave, five are now below 50 new cases / 100K / day.  Better yet, all of the states that peaked early continue to show a steady week-to-week percentage decline in new cases.  Those states are now 3.5 weeks after peak (on average), and there has been no hint of a slowdown in the rate of decline.  The longer that goes on, the better off we’ll be when we reach a state of “endemic COVID-19”.

Continue reading Post #1424: COVID-19 trend to 2/3/2022, still looking good.

Post #1421: Groundhog Day, a fine time to come out of hibernation.

 

As COVID-19 fades from epidemic to endemic, we each have to make our own decisions about returning to normalcy.

My wife and I went back to the gym yesterday, and plan to go to the gym regularly from now on.

You can see Post #1163 (June 23, 2021) for a writeup of the last time we did that.  This time was not all that different.  The mental issues were about the same.  You’ve trained yourself to look at some activity as risky.  You tend to treat the risks of those activities as black-and-white:  Either it’s OK to do something, or it’s not.  And for a long time, going to the gym was Not OK.  So this is akin to breaking a long-standing taboo.  Rational thought only takes you so far.

To each his or her own.  Some people never took any precautions during the pandemic.  Others may have done so at one time, maybe even gotten a vaccine shot or two, but aren’t taking any other precautions now.

And then there are people like me, just trying to balance benefits and risks.

Give me a free, effective, and near-risk-free vaccine and I’ll take it.  Find me a $1 mask that reduces my exposure by 95% and I’ll wear it.  Show me a crowded indoor situation I don’t have to be in and I’ll avoid it.  Unless there’s some good reason to be in it.

At any rate, as of yesterday, Fairfax County VA was down to 57 new cases per 100K per day.  That’s a bit above my cutoff of 40 cases — the point at which the health risks from Omicron, for a vaccinated-and-boostered person, appear no larger than those from flu, in a typical week of flu season, using a very conservative estimate of risk.

That’s close enough, all things considered.  You can see the calculations in Post #1163.   Best guess, given my age and gender, the benefits of getting regular exercise once again vastly outweigh the COVID-related risk involved in going back to the gym.  So back I go.

I have just three more things to talk about:

  1. Some perspective on the entire pandemic to date.
  2. A quick recap of comparing risks from Omicron and flu
  3. A some more in-depth look at COVID-19 vaccine protection against hospitalization and death.

 


Some perspective

Source:  COVID cases and deaths: CDC COVID data tracker.  COVID hospitalizations:  Calculated from US DHHS unified hospital dataset  Hospitalization data are missing prior to mid-2020..  Flu:  Twice the median of values in Table 1, CDC Disease Burden of Flu.

Looking back over the entire pandemic, there’s no doubt that COVID-19 posed a far more serious problem than flu.  As shown above, in just under two years of the pandemic, the U.S. had perhaps 30 percent more more formally-diagnosed COVID-19 cases than the number of symptomatic cases you would expect from two years of flu.  But those cases generated at least five times as many hospitalizations, and about 12 times as many deaths.  (I say “at least”, because nobody tracked hospitalizations for the first few months of the pandemic.)

That scorecard for the pandemic as a whole (so far) includes the effects of a lot of proactive measures.  Not just the COVID-19 hygiene rules regarding masks and public gatherings, but also the elimination of a year of in-person schooling.  And, for about half that period, the use of use of vaccines that were far more effective than the typical flu vaccine.

In other words, the table above reflects not just the virulence of the disease, but also those things done to minimize the impact of the disease.  The disease itself was far more virulent, compared to flu, than the table above suggests.

Lest we forget, the original wave — the one that largely caught us unaware and unprepared — reflected the underlying severity of the disease, before we got proactive about it.  Here’s how the case mortality rates appeared at the peak of each of the major waves:

Source:  Calculated as ratio of peak deaths to peak cases, data from CDC COVID data tracker.

I’m supposed to say that maybe that first number (on the left) is exaggerated by the lack of testing.  (Recall that, among other things, the CDC botched the first DNA tests and had to recall them.)  But my recollection is that mortality rates in some European countries exceeded that, at the time.  That’s also below the initial mortality rate reported for Wuhan.  So maybe that reflects a lack of testing in the U.S.  And maybe that’s just how bad COVID-19 was before anyone had come to grips with how to deal with it.

By the time we got to the third wave, we had the Delta variant, which was far more virulent than its predecessors.  But by that time, more-or-less every adult who wanted to be vaccinated had been.  And the net result was a reduction in the case mortality rate.  But that’s not a uniform reduction.  The average case mortality rate fell because the vast reduction for the vaccinated more-than-offset the high rate for the un-vaccinated.

If we look at the CDC’s new analysis of cases and deaths by vaccination status (age-adjusted, age 12 and older only), we can see that the un-vaccinated have had about a 15-fold greater chance of dying from COVID-19.  That’s a result of a four-fold greater chance of being infected, and then roughly another four-fold greater case mortality rate.

Source:  CDC COVID data tracker.

If I divide that by the fraction of the population (over age 12) that was vaccinated and not, at that time, I end up with the following graph of fraction of COVID-19 deaths at the peak of the Delta wave, by vaccination status:

So, just to be clear, it’s not so much that COVID-19 had gotten tamer, up to the Delta variant.  It’s that we’d gotten a lot better in dealing with it.

Once vaccines came into use, this became mostly a pandemic of the un-vaccinated.

In reality, those proportions shown above are due to more than just the pure effect of vaccination.  I would judge that, after the CDC age adjustment (accounting for differences in age between the vaccinated and un-vaccinated groups), what you’re looking at above is mostly the impact of vaccination.  But it’s amped up by all the other differences in behavior — such as COVID-19 hygiene and willingness to take risks — between the vaccinated and un-vaccinated populations.

To round this out, let me show the CDC’s estimate of the impact of the booster dose.  This is from the end of last year, so this is showing effectiveness against Delta.  As with the numbers above, these are age-adjusted for persons age 12 and older.

Source:  CDC COVID data tracker.

As with the first chart, that likely somewhat exaggerates the true impact of the vaccine.  Plausibly, those getting the booster dose also behave differently from the rest of the population.  But also as plausibly, some part of the difference between this chart and the last CDC chart is the impact of the booster itself.

The CDC’s data don’t extend into the Omicron period, but Virginia continues to show the vaccinated/unvaccinated comparison on a current basis.

Undoubtedly some portion of that is the impact of vaccines, some portion is behavioral, (and in this case, some portion may be due to differences in age and other demographics).  But no matter how you slice it, even with a far-less-virulent Omicron variant, this remains mostly a pandemic of the unvaccinated.


A quick recap, or the tyranny of big numbers.

This section assumes that the reader is vaccinated and boosted.

Let me just briefly recap the calculations of Post #1400, part 4.

In a typical flu season, new flu infections occur at a rate of about 49 / 100K / day.  Beyond infections, flu has reasonably well-known case hospitalization and mortality rate (that is, estimated hospitalizations and deaths per symptomatic case).  Starting from that benchmark, what incidence rate of new Omicron cases generates the same level of health risks as flu?

You have to factor in two offsetting effects, for the vaccinated and boostered population, to compare Omicron to flu.  Vaccine and booster are more effective against Omicron than vaccine is against flu.  Almost twice as effective.  But Omicron is more likely to hospitalize or kill you, if you get infected.  Just over twice as likely.

When I work through the math, the vaccinated and boostered person faces equal risk from Omicron and from seasonal flu once the case rate in his or her area (for all persons) gets down to 40 new Omicron cases / 100K / day.

That result — 40 new cases / 100K / day — looks like a large number, by historical standards.  E.g., when I last returned to the gym, Virginia’s new case counts were in the single digits.

But there’s a reason that the numbers have shifted.  And you need to shift your perception of them.  What looks like a high new case rate, by historical standards, no longer constitutes a high risk rate, for endemic Omicron.  At least, not for the vaccinated-and-boostered.

The first reason is the virulence and incidence of Omicron.  Omicron is much less virulent than prior strains, and the un-vaccinated account for most of the new cases.  So for the vaccinated-and-boostered person, the health risks faced at 40 Omicron cases / 100K / day are not hugely different from (e.g.) high single digit rates of Delta.

Second, once you understand that Omicron is going to be endemic, you have to get your mind around some unavoidable risk, if you are going to get on with your life.  And so, at 40 cases / 100K / day, you are taking some risk.  In this case, the vaccinated-and-boostered person is taking roughly the same risk of hospitalization and death from Omicron as he or she would face from flu, during a typical week of flu season.

My point is, don’t let the big numbers fool you.  You can’t directly compare Omicron daily case counts to prior strains.  And, psychologically, you need to get out of the mind-set of avoiding risks, and into the mindset of determining what’s a reasonable risk.


Reduced risk of hospitalization and death.

In the calculations above, I made one extremely conservative assumption about the effectiveness of vaccine and booster against Omicron.  I gave vaccination and booster no credit whatsoever for reducing case hospitalization rates and case mortality rates.  That is, I assume that they reduced the rate of infection, only.  And that they had no further effect on reducing the odds of hospitalization or death, once infected.

To be clear, that is not the mainstream consensus.  Pretty much from Day One, health authorities have said that vaccines work better at avoiding serious illness than they do at merely avoiding any infection.  Further, that is typical of vaccines for other diseases (e.g., diseases of childhood, flu, and so on).  Breakthrough cases tend to be milder than cases in the unvaccinated.

The problem is that, to me, the data on hospitalization and death appeared confusing.  First, hospitalization and (particularly) death are such rare outcomes that the clinical trials data often don’t have enough cases to give a precise answer in that area.  So we lack the hard numbers.  And then, reliance on observational data means that you end up looking not just at the effect of vaccination, but also at any other differences between the vaccinated and un-vaccinated populations.  As as result, the estimated impact on (e.g.) deaths seemed exaggerated.

In this section, I just want to emphasize how much I may have grossly overstated actual risks by making that assumption.  In other words, at 40 Omicron cases / 100K / day, the actual risk of serious illness faced by the vaccinated and boostered individual may be vastly less than the same risks imposed by seasonal flu.

First, let me return to the CDC data on death rates.  Whatever is causing the vastly lower death rate (under Delta, for the fully-vaccinated population), it’s pretty consistent across age groups.  These mortality curve look pretty much the same for all age groups.

Source:  CDC COVID data tracker.

That suggests that the mortality effect is not primarily driven by differences in behavior.  I doubt, for example, that the 65+ population is hitting the singles bars to the same extent as the age 18-29 population.  What’s constant across these graphs is the disease and the vaccination, but not the behavior.

Second, other countries show the same huge impact on mortality rates in their own observational data.  A reader pointed me to a recent weekly report out of Great Britain, where they compare the boostered population to the un-vaccinated population, by age:

Source:  UK Health Security Agency, COVID-19 vaccine surveillance report, Week 3, 20 January 2022.

Their observational data show on-order-of ten-fold differences in the mortality rates, across all age groups, despite only trivial differences in reported infection rates.  In other words, they show vastly different case mortality rates based on vaccination status.

Finally, there are good first-principles reason to think that vaccination would reduce the number of severe cases.  Even if Omicron is able to avoid existing anti-COVID antibodies, other parts of the immune system would remain primed to fight COVID.  The (slower) action of these other immune reactions might not prevent any infection, but plausibly would prevent the most severe infections.

The upshot is that at 40 new Omicron cases per 100K per day, the risk of severe disease from Omicron may be substantially less than the same risk from typical seasonal flu, for the vaccinated and boostered population.

This just reinforces the main point, though.  If you’re vaccinated and boostered, and if you don’t worry about the risks of being out-and-about during a typical flu season, then you really shouldn’t give Omicron a second thought, once the new case rates drop below 40 per 100K per day, or so.

At that level, if you are vaccinated and boostered, your risk of any infection with Omicron is under half the risk of picking up a case of flu (in a typical year).  And your risk of a severe infection — risk of being hospitalized or dying — is no higher than that from flu, and might be as low as one-tenth the risk you face from flu, depending on whose data you happen to believe.

I’ve reached the point where I’ve fully grasped this, rationally.  It’s still going to take a while to shed some habits and reactions developed during the pandemic.

Post #1420: COVID-19 trend to 2/1/2022, approaching normalcy

 

The U.S. is now down to just over 130 new COVID-19 cases per 100K population per day, just shy of half the level of the Omicron peak.  Cases fell 34% in the last seven days.  When plotted in logs, it’s clear that the rate of decline of new cases continues to get steeper, albeit slowly.

Data source for this and other graphs of new case counts:  Calculated from The New York Times. (2021). Coronavirus (Covid-19) Data in the United States. Retrieved 2/2/2022, from https://github.com/nytimes/covid-19-data.”  The NY Times U.S. tracking page may be found at https://www.nytimes.com/interactive/2020/us/coronavirus-us-cases.html

 

We now have 11 states where the new case rate is below 100 / 100K/ day.  They outnumber the 8 states where the rate remains above 200.  Taking a rate of 40 / 100K / day as a conservative guess for when even the most cautious can return to “normal” (Post #“1400-4), most of the Northeast region is now approaching normal.

Map courtesy of datawrapper.de.

If you want a quick check on conditions in your area, you can do no better than the NY Times COVID-19 map.  As of today, the seven-day average new case count in Fairfax Count, VA is 57, and all the jurisdictions in this area (the DC ‘burbs) are at that level or below.

Source:  The New York Times, comment added in black.

So, if you were wondering what the new normal looks like, it’s light orange.

Post #1419: COVID-19 trend to 1/31/2022, sharp declines everywhere

 

U.S. new COVID-19 cases fell 38% in the past seven days, to just over 140 new cases / 100K / day.  That estimated rate of decline is a slight exaggeration, owing to the very last ghost of the data reporting artifacts of the MLK holiday.  But the bottom line is correct: We’re now seeing sharp declines in new cases counts in just about all states.  As a result, the national average is falling rapidly.

Data source for this and other graphs of new case counts:  Calculated from The New York Times. (2021). Coronavirus (Covid-19) Data in the United States. Retrieved 2/1/2022, from https://github.com/nytimes/covid-19-data.”  The NY Times U.S. tracking page may be found at https://www.nytimes.com/interactive/2020/us/coronavirus-us-cases.html

You can see the qualitative difference between the Omicron wave and prior waves in the next graph.  The arch shape of a top for the Omicron wave is now completely formed.  It’s also clear just how much more rapidly the Omicron wave progressed relative to prior waves.

The graph above exaggerates the actual impact of the Omicron wave, owing to the much lower health risks per case for Omicron relative to Delta.  My most recent estimate is that the case hospitalization rate for Omicron is about 40% of that for Delta.  If use the CDC’s data on the fraction of cases, by variant, over time, and assume that each Omicron case is 40% of a Delta case, I get this risk-adjusted view of the recent pandemic:

With that rough adjustment, this year’s winter wave was about as bad as last year’s winter wave.  And that’s consistent with a finding of modestly more hospitalizations per day, and modestly fewer deaths per day, than during the last winter wave.

But even with that adjustment — getting rid of the exaggerated height of the case counts — the Omicron wave is still qualitatively different for the speed of change.  By eye, it’s much more compact side-to-side than prior waves.  The arch is sharper, as it were.

I’ll be sending my Patreon patrons full-color prints of this graph once the pandemic is officially declared over (/s).

It’s hard to find new ways to belabor the fact that new cases are falling almost everywhere.  But let me give it a try.

  • Arguably, only one state saw an increase in cases in the past seven days.
    • On paper, three states saw increases (ME, MN, SC).
      • SC is clearly just an artifact of data reporting issues.
      • MN is probably just an artifact of data reporting issues.
      • ME has a new case rate that is more-or-less level at 75 / 100K / day.
  • Maryland is down to 40 new cases / 100K / day.  (But Maryland cheats.  Last I checked, they were one of the last U.S. states that only includes PCR (DNA) tests in their counts, not antigen (rapid) tests.)
  • DC is down to 55 new cases / 100K / day.  DC was one of the areas hit earliest and hardest by Omicron.
  • Just ten states remain above 200 new cases / 100K / day.