Post #723: Updated: Aerosols, drinking, and bars. Some clear policy advice, for a change.

Edit:  OK, this one now seems to be out in mainstream news outlets.  In Mississippi, they’re blaming fraternity rush.  In Florida, it’s described as a “radical shift” in demographic of new COVID-19 cases. “They also tell us that younger South Carolinians are not taking social distancing seriously.”  “Our average age last week of people that were positive was age 30, the average age of people getting tested was 47,”   And so on.  So it looks like the smarter people have now figured this out, at least as far as age goes.

Source:  Notes in red were added for this posting.  Original graph taken from: Xie X, Li Y, Sun H, Liu L. Exhaled droplets due to talking and coughing. J R Soc Interface. 2009;6 Suppl 6(Suppl 6):S703-S714. doi:10.1098/rsif.2009.0388.focus.

What you see above is a decade-old footnote in the scholarly literature on the production of droplets during human speech.  Yet, I think it may be relevant to the current spike of coronavirus cases in several states.

Because, as I read it, standing shoulder-to-shoulder in a crowded bar and talking at the top of your lungs is NOT the stupidest possible thing you could do.  In the current era, I mean, in order to spread COVID-19.  You need to be sipping a sweet drink at that same time.  That’s the stupidest thing you could do.

Detail follows. Continue reading Post #723: Updated: Aerosols, drinking, and bars. Some clear policy advice, for a change.

Post #721: This is what convinces people that masks work?

For this one, let me just get the facts down first, then provide the commentary.  For me, this was a great lesson in what works and what doesn’t.  Not in terms of fighting the spread of COVID-19.  In terms of convincing people to do the right things to prevent the spread.


Recall that, a couple of weeks back, there was a hairdresser in East Ripshinthicket, Arkansas who worked in some mom-and-pop hair salon.  She went to work for more than a week after she had begun to feel sick with the symptoms of COVID-19.  During which time, she gave COVID-19 to her coworker.  And then the two of them, together, exposed maybe 140 customers to COVID-19 before, somehow, somebody caught wind of it and put a stop to it.  (Reference, reference 2, Reference 3).

OK, I am mocking them.  This really wasn’t some hick town in the middle of nowhere.  It was Springfield, MO, a metropolitan area with a population roughly 160,000.  These were not some uneducated hicks, they were trained employees of a national chain of hair salons that has $1B in annual revenues.   Per above, you could have gone to one of their salons.

The local authorities were a bit miffed.  They just might have an outbreak on their hands.  So they sat back, put their feet up, and waited to see what would happen.  Only time would tell, because, after all, at that point, (pick one):

  1. the train had left the station
  2. the cat was out of the bag
  3. the genie was out of the bottle
  4. that ship had sailed
  5. what’s done is done
  6. that’s water over the dam and/or under the bridge.

Again, untrue.  The local health authorities both advertised the time and place of possible exposure, and went about tracing all contacts using a list derived from the on-line appointment scheduling software used by the firm.  The offered COVID-19 tests to all exposed individuals, but far less than half of those who were exposed agreed to be tested.  And, of course, because of the lack of cooperation from the public, it really did boil down to “only time will tell”.  They had to wait 14 days to see if any cases emerged from the half of the exposed population that had refused to be tested.  (Though, given zero cases within the tested half, they probably had a pretty good guess that no cases would be found, period.)

But nothing happened.  None of those 140 customers got infected.  So they chalked that up to the right-thinking nature of the population, to their natural toughness, and to the goodness of God.

No, actually, they chalked that up to masks.  Both of the infected hairdressers wore masks, as did all of their customers.  Not because they wanted to.  Seems that in that neck of the woods, mask-wearing was frowned up as some sort of evil and unnecessary liberal anti-freedom plot.  No, they wore masks because that’s the law.  Springfield requires them, in that setting, by local ordinance.  (I’d have thought that would be a state-wide ordinance, but as I read it, the state there has guidelines to encourage some things, but that’s about the extent of it.)

So far, so good.  A potentially large cluster of new infections was avoided by adherence to a simple mask protocol:  Both the hairdressers and the customers had to wear masks.

Here’s where it runs off the rails, for me.  If you read this blog, you know I was an early and vehement proponent of mandatory mask use in public.  And I was convinced of that by careful study of the evidence.  That included both observational studies of mask wearers (versus others), contrast of countries that were successful and unsuccessful in containing COVID-19, and the basic physics of transmission of the disease.  And it didn’t hurt that the head of the Chinese CDC just came right out and said that lack of masks was “The big mistake” that both the US and Europe were making.

Now read what the local health authority had to say.  All in all, the local health department did all the right things.  They required masks for personal care services.  They offered testing.  They carefully traced contacts.  They were more stringent, by far, than the State of Missouri, and they took some heat for that.  But, to me, this is what makes me think (incorrectly!) of these folks as a bunch of yokels.

"Clay Goddard wasn’t religious about wearing a face mask in public, despite being the director of the health department in Springfield, Mo., and the head of the region’s response to the novel coronavirus. He doubted a face covering — even his favorite Kansas City Chiefs-pattern mask — offered much protection.
“You’d probably have better luck stopping the wind,” Goddard said.
 ... Goddard has gone from “an early skeptic” on face masks to a believer."

The local public health director — surely a person of some learning and experience — wasn’t convinced to use a mask, personally, by any of the available evidence.  Not systematic studies, not the CDC guidance, not nothing.  The only thing that convinced him to wear a mask was this one episode.  It was having this case show up, in his back yard.  Then, and only then, did he change his mind about wearing a mask.

So I take a lesson from that.  For many people, probably for most people, a single good human-interest anecdote may outweigh all the scientific evidence in the world.  Usually, that’s a bad thing.  It makes people believe fervently in things that just ain’t so.

But in this case, maybe it’s a good thing.  And I’m glad it got reported out.  All too often, if there isn’t a disaster, then it’s not news.  And you never hear about it again, like all the Cheeseheads that didn’t get infected after the abrupt removal of social distancing restrictions in Wisconsin (Post #709).

So I hope Clay Goddard goes on to proselytize.  I hope he travels across his state, talking about his conversion.  Talking to people whose current mindset matches the way he thought about this a month ago.  It’s great that he has become convinced that masks are useful in stopping spread of COVID-19.  I hope he can leverage that conversion, and convert a bunch of non-believers.  Barring that, I hope that this Springfield, Missouri story — what amounts to an anecdote — can convince people that wearings masks is something that we all need to do.

Post #720: Some followups on coronavirus

 

Source:  Japanese Ministry of Health, Labor, and Welfare.


Yesterday’s count of just 13 cases in Fairfax appears to have been real.  A few times in the past, we’ve seen glitches in Virginia’s data systems result in a very low count one day, followed by a catch-up high count the next.  But that’s not the explanation of yesterday’s low count.  Today’s count of new cases in Fairfax is 50, which is still far below recent trend.  Here’s how the last 28 days of new case counts looks, as of today:


Hydroxychloroquine appears to be dead, for now, pending any controlled trial of the zinc combination that is rumored to be effective.  A few days aga, it was announced that a large-scale controlled trial of that drug (not in combination with zinc) showed no material benefit among gravely ill COVID-19 patients.  Unsurprisingly, the FDA recently rescinded its emergency approval for use of the drug in treating COVID-19.  Presumably, any ongoing clinical trials can still continue.  And I’m not sure that affects continuing use of the drug for COVID-19 patients.  It just means that such use is clearly “off-label” now.

Separately, yesterday’s news about dexamethasone helping to prevent COVID-19 deaths wasn’t really news, as such.  Physicians have been routinely providing steroids to the most severely ill COVID-19 patients, trying to prevent their immune systems from destroying their lungs.  This was by analogy to other situations where patients appear to suffer damage from an out-of-control immune system.  The findings reported yesterday were not really the discovery of some new drug.  They were simply the reported results of a controlled clinical trial of this already-in-use steroid therapy.  And, yes, it is helpful.  And so physicians will continue to use it when it appears to be indicated.


Church services are beginning to appear as centers of COVID-19 spread in the US.  This is not really a surprise:  One of the original US outbreaks was due to a large funeral service.  Put a crowd of people in a room and have them all speak aloud, maybe thrown in some singing, and that’s pretty much a recipe for aerosol-based spread of disease.  Both speaking and singing generate large quantities of aerosol (under-five-micron-sized) drops that can remain suspended in the air for quite some time.  Such aerosol spread can result in a single infected individual infecting many others, especially if that individual is part of the few percent of the population that is a “super-emitter” of aerosols (that is, generates far more aerosols than the average person.) And while this does not seem to matter much outdoors (as the aerosols are soon rapidly diluted below the level that can cause infection), large gatherings indoors remain hazardous due to aerosol spread.

Recall that we now know that COVID-19 is fairly hard to spread in most cases.  It doesn’t really spread well in a one-on-one situation.  If someone in your home is infected, or if you spend 10 minutes in face-to-face conversation with an infected individual, you have just a 15% chance of becoming infected yourself.  Further, 70% of those infected will never go on to infect anyone else.  So, most infected people, and most interactions with infected people, do not spread infection.

Instead, the virus remains active in the community largely because a small fraction of infected individuals go on to infect many people, and super-spreader events (where one individual infects many others in a single event) are one of the mainstays for keeping the virus in circulation.  Such super-spreader events can only occur in crowds, and so far, appear vastly more likely when that crowd is indoors, as opposed to outdoors.

And so, in addition to numerous church-based events already documented here, new church events appear to be reported daily.  So, here’s the biggest outbreak so far in Oregon, with hundreds of cases traced back to a single church service with “hundreds of worshipers singing, dancing, and jumping around …”. Here’s five churches in West VirginiaCaliforniaHouston.

Some of those might properly be classified as “workplace” incidents, no different from (say) meat packing plants.  That is, in some cases, it was merely the clergy that were affected.  But the largest of them are true church-based within-congregation spread.  And you should expect to see more of that in the coming weeks.

Everybody can take a tip from Japan:  Steer clear of the three Cs.  That’s the graphic at the top of the page.  Note that they specifically say that when you have all three, that generates high risk:  An enclosed space, with a crowd of people, all talking.  But that’s pretty much the definition of a church service.

A lot of pro-church people don’t quite seem to understand this.  They use (e.g.) Home Depot as an analogy.  If you can go to Home Depot, then what’s the problem with going to church.  And it’s the three C’s.  Home Depot is indoors, mostly.  But it’s not crowded, and you don’t have a bunch of people talking or singing in close proximity.  People who think that attending a church service is the equivalent of picking up an item at the Home Depot are simply misinformed.

As a final note, as of yesterday, the Merrifield Home Depot was strictly enforcing the Governor’s mandatory mask policy.   A lot of businesses now have a sign at the door saying that masks are required.  But they aren’t turning down business from people show up without a mask.  Not so at our local Home Depot.  Yesterday, there was a security guard at the front of the store, and I saw her politely turn away a couple of folks who weren’t wearing masks.

I think that’s the way forward.  Like Japan.  Instead of a lockdown, we have a lifestyle change.  You need to shop for hardware?  Wear a mask.  It’s not some huge hardship.  It’s not virtue signalling.  It’s just the way things need to be, for now.

Avoid the three Cs.  That’s not really rocket science, now is it?


Post #719: Why Latinos?

Source:  Commonwealth of Virginia Department of Health, COVID-19 dashboard, 6-16-2020.

This is just a quick note on the racial/ethnic mix of persons diagnosed with COVID-19 in Virginia, and in particular, in Fairfax County.  It is heavily skewed toward Latinos.  And I can’t quite put my finger on why, except to say that this dovetails with an age mix that suggests that working-age people are the ones at risk and being infected.


Continue reading Post #719: Why Latinos?

Post #718: Key graphs updated to 6/16/2020. Total of 11 new cases in Fairfax County today

This post updates some key graphs to 6/16/2020.  The sharp dropoff in new cases in Fairfax continues.  Whatever it is we’re doing, all I can say is, let’s keep doing it.

These numbers tend to fluctuate a bit from day to day, depending on how “backed up” the State reporting office is by 5 PM close of business on the prior day.  That said, today’s count of new cases in Fairfax is 11.  (No, I didn’t miss a zero — eleven cases.)

Maybe that’s an undercount, and we’ll see the rest of them show up tomorrow.  Maybe that’s real, and we won’t.  Either way, that’s low.  It suggests that any activity you’re doing now, in Fairfax, is pretty safe.  Based on the total number of adults in Fairfax, 11 cases represents a 0.001% daily chance of infection.

It’s hard to make any sense out of this.  In part, I think we’re finally seeing the seasonality of COVID-19 showing up in the Virginia data (Post #714).  But who knows?

  • Maybe we’ve just finally run out of uninfected people who are unwilling to wear a mask in public? So that we just had to burn through the non-compliant portion of the population?
  • Maybe we’ve infected enough of the at-risk workforce (e.g., healthcare workers, retail workers) that it’s slowing the spread that way.  So a key portion of the population has reached something like herd immunity?
  • Maybe the fraction of the population who can easily catch this is far smaller than the total?  And so all the easy targets have already been infected?
  • Maybe the well-educated population of Fairfax County has finally figured out how to do everything right?  Masks, distancing, and so on.

For sure, other than the Governor’s mandatory mask ordinance, it’s not like we’ve been doing hugely more to limit the spread.  And it’s far too soon to have reached “herd immunity” for the population as a whole.

My point is, if there is a reason for this slowdown in Fairfax, we’d surely like to pin it down, so that we could do whatever-it-is we’re doing in Fairfax, throughout Virginia.  But right now, it just is what it is.  There’s no apparent reason for it.

Anyway, outside of NoVA+, we’re getting to the point where any impact of Phase II re-opening should just start showing up in the data.  Phase II started about 11 days ago  outside of the NoVA+ areas.  And would begin to show in the red lines in the graphs below.  But, so far, nothing appears to be showing up.

Continue reading Post #718: Key graphs updated to 6/16/2020. Total of 11 new cases in Fairfax County today

Post #716: More epidemiology: Why is there no clear pattern?

I’ve been trying to make some sense of the current spread of infection of COVID-19 in the US.  I have to admit that’s been more-or-less a complete and total failure.  I’ve managed to turn up a lot of interesting facts, but nothing resembling a pattern.

So, for this post, I’m just going to explain briefly why that is, and then start arranging a list of states in some sensible format.  I’m doing that because, as far as I can tell, most of what gets written up in the popular press is un-usable, from the standpoint of actually understanding what’s going on.

You’ve seen a lot of popular press articles recently on rapid growth of COVID-19 in many states.  By my estimate, those articles are only partially true.  For one thing, they ignore places where there’s been “bad behavior”, but no problem (Post #709).  From a scientific standpoint, those area are every bit as interesting as the ones where case counts are growing.  For another, they commingle areas where there is a real threat to public health emerging (Arizona) with areas that are, really, no worse off than Virginia is (Texas).

The upshot of this analysis is my list of states and the level of COVID-19 problem that they currently face.   I think everybody has it right in that Arizona, pretty much all-of-a-sudden, is facing some serious problems.  But other states that are featured in the popular press do not — certainly not if my standard is how things stand currently in Virginia.  Which, already noted (Post #715), seem to be heading in the right direction now.  For no particular reason that I can put my finger on, other than a generally reasonable population, and good weather.


 

What makes this so difficult?  Why is there no clear pattern across the states?

In the main, what makes any analysis of COVID-19 so difficult is that there are no natural constraints on the disease.  Nobody has immunity and the number of persons infected so far is a tiny fraction of the population.

As a result, the US population is just one big wide-open field.  And as far as i can tell, what you  observe in one state versus another is largely a matter of happenstance.  E.g., it just so happened that nobody in Montana had traveled to China recently when the pandemic was starting.  But lots of people in New York had done so.  And so, even now, infection rates in New York are vastly higher than in Montana.  That doesn’t reflect any truly fundamental difference between the New York State and Montana populations.  More-or-less, that’s just an accident.  There is no more fundamental explanation.


What does a good epidemiological study look like?

In short, a good study is one that actually gives you some explanation of why things are happening.  That’s possible because most studies focus on diseases that are well-established and stable in the population.  And, typically, diseases where there is some clearly defined cause-and-effect link that you can hope to clarify.  Even if that link isn’t obvious.

A classic epidemiological analysis is the analysis of state-to-state variations in skin cancer.  There, the cause of disease is relatively well-established as lifetime sun exposure, tempered by skin color.  Briefly, the paler you are, and the more time you spent in the sun, the higher the odds that you’ll develop some sort of skin cancer late in life.  And once you have all those factors on the table — average sunlight, altitude, and racial mix, most of the variation in skin cancer rates makes sense.

Here’s an example I did years ago regarding oxygen use.  It took a while to uncover the cause-and-effect, but in the end, despite huge state-to-state variation in home oxygen use, the variation made some sense.

In the Medicare program, virtually the only reason for prescribing home oxygen is for chronic obstructive pulmonary disease (COPD).  And yet, Medicare’s spending for oxygen, across the states, seemed to have little to do with COPD.  There’s five-fold state-to-state variation in spending, with only a vague hint of some link to the disease that the spending is supposed to be for.

At some point, I was asked to look at this issue.  My client — a major manufacturer of oxygen equipment — was not particularly happy with the answer that the US DHHS Office of the Inspector General (OIG) seemed to be leaning toward, which boiled down to “fraud and abuse” answer.  Not that that’s a bad answer, in general, in the US health care system.  My client simply did not think it was true.

The first step in any such analysis is to sort the data a few ways, and see if anything catches your eye.  After a while, it dawned on me that many of the high-spending states were in the US Mountain region.  Like so:

Rocky mountain high?  Mile High Stadium?

It finally dawned on me that the primary driver of Medicare oxygen spending was altitude above sea level.  For a given COPD population, Medicare was spending a lot on oxygen in some states mostly because there wasn’t muich oxygen in the air.  Like so, a nice straight line:

Together, elevation and COPD prevalence explained nearly all (85%) of the state-to-state variation in oxygen spending.  That was enough to convince the relevant authorities that the state-level variation on oxygen spending wasn’t the result of massive amounts of fraud and abuse, but instead was (mostly) the result of natural forces.

And that is what a successful epidemiological study looks like.  You actually get an answer, and it appears to make sense.

And nothing about COVID-19 yields anything that looks like that.  Nothing.  Not the states that have high current prevalence.  Not the states where it’s growing rapidly and not.  To me, so far, the lineup of the states looks like one big accident.


Putting the state data into a sensible format.

The first step in trying to make sense of things is to put the state data in some uniform format.  Something more than just a simple count of cases.  At the minimum, put the new case counts on a per-capita basis.

You’ve seen a lot of popular press articles about how many states are having increases in new COVID-19 cases.  And yet, much of that seems to come from picking those states after-the-fact.  The popular press looks at the states with rising incidence, and talks about them.  It ignores the ones where behavior was superficially similar, but there is no increase in cases.

And as a result, you don’t hear about the Eat Cheese or Die states (Post #709).  Those who somehow skated past an expansion of the pandemic.  You only hear about the ones where trouble is brewing.

That’s good reporting, but bad science.  That’s why I have insisted on running an analysis of a fixed, predetermined list of states.  This list was, at one point, the New York Times list of states that re-opened early and states that did not.  Up until my last analysis, there was no difference between these two sets of states.  I did that last a month ago, and posted the results in Post #694.  Here’s how that contrast looked three weeks ago (updating Post #694.)

But nobody was seeing any impact three weeks ago.  Let me start by redoing that, updating to yesterday’s counts, and extending the time periods over which I compare those two groups of states.  I’m going to drop the pre-opening period (during which the two groups of states appeared similar).

And now, when I look over three time periods — May 1 to present, May 15 to present, and June 1 to present — sure enough, there’s a modest average difference between the two sets of states.  In the late-reopening states, on average, they’ve fallen to about a 1.3% per day increase in cases, over the past two weeks.  In the early-reopening states, by contrast, there’s been no improvement in new case growth.  In fact, the closer we get to the current day (here, June 14) the more their case growth rebounds.

But that doesn’t come close to telling the full story because there is so much variation among the states.  The problem is, much of what you see tabulated by state is useless from the standpoint of understanding where there are and aren’t problems.

First, it makes no sense to show the raw count of new cases, for the simple reason that some states have a lot more people than others.  So, in the table below, I should you the new cases per capita, from the last week of data, annualized.  In other words, the first column of numbers shows what fraction of each state’s population would be newly infected over the course of a year, if the currently-observed rate of new infections remained constant.

Second, you want to know whether or not that situation is getting worse.  So the second column shows the change in that annualized per-capita infection rate, comparing those week to the week prior.  Positive numbers flag states where the new infection rate (on a per-capita basis) is rising.  Negative numbers then (obviously) show where it’s falling.

Finally, as a kind of overall “misery index”, the last column of numbers is the sum of the first two.  In effect, that’s a crude projection of where that state will stand, next week, if it sees the same change in new infection rate as it saw this week.

I have sorted the data by the “misery index”, descending.   States at the top would appear to have a serious problem, in that they have both high and rising new infection rates.  States at the bottom either have a low rate of new infections per capita, or their rate of new infections appears to be falling rapidly.

To me, this table gives me the kind of ordering of the states that I was looking for.  For perspective, I’ve highlighted Virginia in green.  While we have a fairly high rate of new infections per capita, that’s now falling so fast that you would not project big problems here in a week or two.

And when I rank the states this way — that makes some sense to me — I get a somewhat different picture from what you may see in the popular press.  Yes, Arizona has a real problem, with a high and rising infection rate.  This has made the news.  But Alabama is not far behind.  And yet, it’s hard for me to think of two states that are more different than those, in terms of climate and population.

Note that North Carolina is near the top of the list.  But in terms of climate, I’d say North Carolina has a climate nearly identical to that of Virginia.  So if there is any seasonality to COVID-19, something about human behavior or biology in North Carolina is completely offsetting it, compared to Virginia.

And in general, when I look at that list, I see — nothing.  No rhyme or reason whatsoever.  Both New York and California were hit hard by this, early on.  New York is now seeing a relatively low and falling rate of new infections, California is seeing a relative high and rising rate.  So far, I see no way to make any sense of this.  Nothing comparable to the analysis of oxygen use.


A

From macro to micro:  No denominator means no way to know the odds.

Consistent with that scrambled picture at the macro level, there appears to be no way to determine what’s risky and not at the micro level.

As you plot your return to a more normal existence, you’d like to have some idea of what’s a high risk situation, and what’s a low risk situation, in terms of likelihood of contracting COVID-19.  Wouldn’t we all.  The need to know your risk is now so mainstream that an entire Washington Post article was devoted to this idea, this past week:  How can we tell what’s risky and what’s not?

Having thought long and hard about this over the past week, it’s clear to me that we are never going to get any precise estimate of just how risky most real-life situations are.

And the reason for that is simple:  No denominatorThe risk of any activity is the number of persons who get infected through some activity (numerator) divided by the number of persons who engaged in that activity (denominator).

In an ideal world, we might plausibly get some notion of the numerator — the number infected through some activity.  Under no stretch of the imagination are we likely to get any good estimate of the denominator — the total number of people who engaged in that activity.  At least, not for most common activities.

Let me just take a simple example:  Indoor dining in the Town of Vienna.  Plausibly, through contact tracing, we might be able to identify which new COVID-19 infections in Vienna were contracted in restaurants.  Plausibly, the Commonwealth might even make such information public.  (It does not do so now.)  So that’s information that is not now made public, but could (in theory) be known.  In theory, we could know the numerator in our risk calculation.

But to know the risk of eating in a Vienna restaurant, you also need to know the denominator:  How many people, in total, ate in a Vienna restaurant that day?  And that’s a piece of information that nobody gathers, and that nobody has access to.  That’s where you just have to go totally by the seat of your pants.

For example, suppose you want to know the risk of (e.g.) going to your dentist for a routine checkup.  It’s not enough to know that (e.g.) N people appear to have gotten infected, via routine dentistry, in the past month, in Virginia.  (The numerator).  To know your odds, you have to know how many people went to the dentist.  (The denominator).  And that’s something that literally nobody can tell you.  Even making a crude guess, in today’s crazy climate, is probably out of the question.

All you can do is identify where new cases are and are not coming from.  And kind of wing it from there.


Summary.

So far, US epidemiologists have been remarkably, almost astonishingly unhelpful, in providing any guidance whatsoever on where people are picking up their infections.  That is, we here in the USA can’t even get information on the numerator, let alone the denominator.  Nothing about the state-level data seems to make sense, and nobody seems to be stepping up to put it into any context that makes sense.

Certain health care settings are tracked, sure.  So you can, in theory, estimate the odds if you are (e.g.) living in a nursing home.

But for the rest of us, living our daily lives, the Commonwealth (and I would say US epidemiology in general) has provided little in the way of useful information on what we should and should not be doing.  Maybe that’s just the way things are.  Maybe  nobody has quite figured this out yet, but somebody eventually will.

Post #715: Key graphs updated to 6/14/2020. Drop-off in new cases accelerates.

This post updates some key graphs to 6/14/2020.  Fairfax has seen a sharp dropoff in new cases.  We’ve been building up to that for the past couple of weeks, so that is not surprise.

As you can see from my just-prior post, my best explanation is that we’re finally seeing the seasonality of COVID-19 showing up in the data.  For sure, other than the Governor’s mandatory mask ordinance, it’s not like we’ve been doing hugely more to limit the spread.  And with just 1 person per 100 known to have been infected in Fairfax County (and likely no more than 5% of Fairfax residents actually having been infected), it’s far too soon for the “natural” end of the epidemic (typically estimated as herd immunity occurring with 70% of the population infected).

Whatever we’re doing under Phase I of re-opening, it’s not creating a spike in new cases.  If there are new cases attributable to this modest relaxation of restrictions, they are being swamped by this ongoing downward trend.

Continue reading Post #715: Key graphs updated to 6/14/2020. Drop-off in new cases accelerates.

Post #714: The seasonality of coronaviruses and the current decline of cases in Virginia

Seasonality of human coronaviruses (other than COVID-19) in Stockholm, Sweden.  Source:

Potential impact of seasonal forcing on a SARS-CoV-2 pandemic DOI: https://doi.org/10.4414/smw.2020.20224 Publication Date: 16.03.2020 Swiss Med Wkly. 2020;150:w20224 Neher Richard A., Dyrdak Robert, Druelle Valentin, Hodcroft Emma B. Albert J.

 

Continue reading Post #714: The seasonality of coronaviruses and the current decline of cases in Virginia

Post #713: Another promising treatment: monoclonal antibodies

Convalescent plasma is a well-established technique for treating diseases that have no other type of treatment, such as Ebola.  You take blood from individuals who have recovered from a disease, separate out the antibodies that they have developed that fight the pathogen in question, and inject those antibodies into someone else.  Those antibodies then provide “passive immunity” to the person who was injected — they allow that person to fight to pathogen.

This technique has been in use for more than a century, for a wide range of diseases.  Case reports from China and elsewhere suggested that it has some significant benefit in treating COVID-19.  (A fairly exhaustive literature review can be found on this University of Michigan web page. )

Providing antibodies to an infected individual is helpful, but it’s not a silver bullet.  Although there were good early case reports, at least one clinical trial found only modest (and not statistically significant) improvement among critically ill COVID-19 patients.  But that had limitations, including small sample size and a focus on the critically ill (for whom almost nothing appears to work well, including anti-viral drugs).  The modest clinical improvement for the critically ill COVID-19 population appears similar to convalescent plasma results for the earlier MERS coronavirus epidemic (per this reference).  And, administration of convalescent plasma at earlier stages of disease appears to have better results (per this commentary).

Regardless of effectiveness, a shortcoming of convalescent plasma is availability.  Recovered donors with adequately high antibody levels can donate enough blood to treat a handful of cases.  And the equipment that separates out the antibodies appears to be relatively uncommon.

Thus, whatever the level of benefit, this treatment is limited by supply.  (Which is, I think, why it has largely been reserved for critically ill patients, so far.)  There simply does not appear to be a way to extract these naturally-occurring antibodies in large quantities.

So, why not just manufacture the antibodies instead?  And that’s precisely what several drug companies are now attempting to do, using monoclonal antibody techniques.  Under this approach, scientists develop cells that produce an antibody for some specific substance, culture (grow) those cells, then extract the antibody.

Monoclonal antibodies are now a mainstay of treatment for both autoimmune disease and cancer.  Any drug (biological) whose generic name ends in “-mab” is a monoclonal antibody.   Hence, Remicade (infliximab), Humira (adilumimab), Avastin (bevacizumab), among others.  Currently, it looks like 6 of the top 10 drugs sold in the US, by dollar volume, are -mabs.   The list of commercially-available -mabs includes hundreds of substances (per Wikipedia).

This is almost a plug-and-play technology at this point.  Several different systems are commercially available for developing -mabs to produce specific antibodies of interest.  These are common, but they are still high-end science, involving techniques that would have been unthinkable 30 years ago.  For example, one of the oldest and most successful -mabs — Remicade — is actually a mouse-human hybrid.

And so, that machinery is now being focused on COVID-19.  This is now at the clinical trials phase for one of them.  My only point being that this is a completely routine and feasible (if expensive) technology.  There’s no question that they can produce these COVID-19-specific monoclonal antibodies, in large quantities.  At this point, their effectiveness is not clear, particularly for the most severely ill patients.  But these should, at the minimum, add another set of therapies to the list of treatments for COVID-19.