Updated: Oct 9, 2020
One of the great frustrations of many Americans when it comes to COVID-19, are the constantly shifting goal posts. The most egregious of these goal post shifts has been the "flatten the curve" mantra.
Our public health officials have not given us any indication of what a flattened curve looks like. Figure one below, which was released by the CDC, has no units. The implicit goal in the graph--and the once-explicit goal--of flattening the curve is to keep cases low enough such that we don't overwhelm our healthcare capacity.
The goal was not to eliminate cases, but rather to ensure we had sufficient health care capacity to manage the cases we had. At the moment--and actually at no point during our epidemic peak--did we get anywhere close to overwhelming our healthcare capacity. As can be seen in Figure 2 below, both ICU and general beds nationally are running at 65% capacity, with COVID cases taking up only 7.4% of U.S. hospital capacity at the time this graph was created (the number is up to 8% as of today, July 29th).
Source: CDC, data as of July 8th; visualization by Emily Burns
Given this high level of hospital capacity, it looks like we have flattened the curve well beyond our public health officials' wildest expectations. You might be about to say "But what about Texas?!". I'll get to that. But for right now, let's just acknowledge that we are nowhere near running out of hospital capacity--and that is with 60,000-70,000 observed COVID cases/day, and over 1000 deaths daily.
However, the goal posts have moved. This is no longer the definition of success. So now, the question becomes, what and where are the new goal posts? To many, these goal posts are ever-receding. Thus, I would like to propose a benchmark for the flattened curve--the 2017-2018 flu season. In that flu season, according to the CDC, 61,000 people died, 818,000 people were hospitalized, and there were an estimated 44 million symptomatic cases. My proposal is that we map the expected daily cases, hospitalizations and deaths for this recent severe flu season, and use those data--specifically daily deaths and hospitalization--as a benchmark for managing COVID-19 going forward. So, let's see what those numbers look like.
All right, to get started, a rather long flu season is 20 weeks. So let's create a curve that gives us an estimate of the daily deaths due to flu in the U.S. during the 2017-2018 flu season. The curve in Figure 3 below is an attempt to visualize how many deaths per day we saw during the 2017-2018 flu season. I have also made an estimate for what that would look like if there had been no vaccinations, which I will talk about a little later. The CDC estimates that 37% of people in the U.S. got that season's flu vaccine. Unfortunately, it was only 40% effective, so the death rate was not markedly decreased by the vaccine during this season.
Source: Curve fitting by Emily Burns based on CDC estimates of U.S flu deaths in 2017-2018 flu season.
As can be seen from the curve above, had we been watching daily deaths in the U.S. during the 2017-2018 flu season, we would have seen more than 1000 deaths per day for more than a month--topping out with a week of more than 1200 deaths/day. However, these estimates are likely quite a bit lower than the reality. The curve above is actually far "flatter" than the real curve, which you can see in figure 5 below. Had there been no vaccination, the deaths would have been even higher, topping out at least at 1455/day, even in this flat scenario. However, as can be seen in Table 1 below, vaccination rates are highest amongst those populations with the highest mortality rates--those over 65. Presumably those who have the highest number of co-morbidities (which are similar for flu as they are for COVID) are more assiduous in getting their vaccinations, which may mean that in a totally un-vaccinated population, these deaths would be substantially higher.
Table 1: 2017-2018 U.S. Flu Statistics by Age
Now let's take a look at COVID-19 deaths in the U.S. relative to this distribution of flu deaths. Below you see COVID-19 deaths in the U.S. Until early May, more than half of all U.S. COVID-19 deaths were driven by 6 states--NY, NJ, CT, MA, MI and LA. These states make up only 16% of the U.S. population. Yet even now, they account for HALF of all U.S. Deaths (77,800 out of 155,5000). The red trend line shows the 7-day average for U.S. deaths in 44 states + D.C.excluding these 6 hotspot states. I have also included a shaded area that shows where national lockdowns would have been impacting deaths.
Figure 4: Daily Estimated U.S. Deaths for 2017-2018 Flu Season, Overlaid with Daily U.S. COVID-19 Deaths
Source: Worldometers. Data analysis and visualization, Emily Burns
One thing that is particularly interesting is that during national lockdowns, outside of those hotspot states, deaths--and hence cases--continued to grow at a very rapid rate. Even more interesting is that once most large states had started to open up, we see a precipitous decline in the these 44 non-hotspot states. I expected the surge that we are currently seeing to occur back in Mid-May. The fact that there was at first a dip, I believe reflects the immunological benefit we got as a society by allowing this virus (and any virus) to circulate in populations who are not at-risk. Now we are seeing a surge, but nationally we are still below the peak levels of flu season--with our 7-day average at just over 1000 deaths/day, or less than 3 deaths/million/day.
Just for fun, this is what the disease curve for the 2017-2018 flu season actually looked like--as I mentioned, about twice as steep as what I've shown above, meaning that the actual peak daily deaths were probably about twice the number of my more conservative, flatter curve. The black trend line also shows the percent positive for tests--which topped out at 26% nationally--for four weeks--you will perhaps note that there is quite an up-roar now over certain states having 10-15% positivity rates for COVID-19 tests.
Figure 5: Influenza Positive Tests Reported to CDC by Clinical Laboratories and ILINet
Based on these analyses, it is hard to argue that we have not flattened the curve. Surely inline with, or less than daily deaths due to flu is flat enough? The surge in deaths we are seeing now (deaths being a far better proxy for cases than tests, given how many cases tests miss), is still below--likely substantially below--what would have been seen during the peak the 2017-2018 flu season, a less severe disease for which there was a vaccine--albeit not a terribly effective one.
Based on the derived, gradual curve in Figure 4, above, the deaths/million/day during the U.S. 2017-2018 flu season would top out at 4/deaths/day/million, 5 without a vaccine. Looking at the real curve of flu infection growth in 2017-2018 in Figure 5, we see that in fact the curve is far steeper--approximately twice as steep--and far more concentrated into a few weeks, likely meaning that at its peak, daily flu deaths in the U.S. would have reached 8 or even 9 deaths/day/million. That would be a very large number indeed, close 3000/deaths/day. However, it is almost certainly the reality. Give that, I don't think it is unreasonable that we benchmark coronavirus deaths based on that number, or perhaps lower, with a goal being to keep our deaths/day/million from COVID-19 to 5 or less, or fewer than 1500/deaths/day nationally. The reality is that individual states will peak at different times, meaning that, locally, deaths/million will be higher. I would propose that a reasonable goal would be to keep deaths/million in a given state below that flu threshold, somewhere between 5 and 10/deaths/million/day. New York City topped out at 70 deaths/million/day, New Jersey at 59, Massachusetts at around 35. So this is practically an order of magnitude better than those places. At the moment, nearly all states are within this range. Only Arizona and Delaware slightly exceed it, with Arizona at 11 deaths/day/million, and Delaware at 13. In fact, at the moment, 39 states are below the 3/deaths/day/million which is in itself substantially below the expected deaths/day during the peak of the 2017-2018 flu season.
Figure 6: Deaths/Day/Million by U.S. State; 7-Day Average as of July 30, 2020
Source: Worldometers. Data analysis and visualization, Emily Burns
This is further supported by looking at actual excess deaths. Figure 7 shows excess deaths over time. You can see the jump in deaths due to every flu season, and you can see just how bad the 2017-2018 flu season was. You can also clearly see the massive number of excess deaths at the beginning of the COVID-19 outbreak--again, lead by those 6 states (NY, NJ, MA, CT, LA, and MI). You can then see the excess deaths drop down as those six hot-spot states' death tolls declines, even while the rest of the country was growing apace. While we are now currently approaching the level of excess death that was seen during the 2017-2018 flu season, we are actually just approaching the normal level of deaths for an average flu season. We are currently well below the total number of deaths per week experienced during the peak of the 2017-2018 flu season--as I posited earlier. This is because summer is, as can be seen, generally the lowest mortality part of the year. At a minimum, it seems like COVID-19 weekly deaths must be bench-marked at the level of the flu. As you can see from the green line, we are now creeping close to that now. I am sure we will reach it, and slightly exceed it, but I don't think it is likely that we will once again exceed the peak weekly deaths seen during the 2017-2018 flu season.
Source: https://www.cdc.gov/nchs/nvss/vsrr/covid19/excess_deaths.htm. Emphasis added by Emily Burns.
I think it is reasonable, as citizens, that we demand from our public health officials that we be able to keep our society open to the fullest extent possible, such that we are below the level of weekly deaths observed during the peak of the 2017-2018 flu season. This is not an unreasonable expectation. What happened in NY, NJ, MA, and CT was an aberration, and greatly exacerbated by poor management and technocratic hubris. The entire country ought not be kept on ice while, as a nation, our deaths are lower than the weekly deaths of a recent flu season--even if it was a challenging one.
But what of hospital capacity? Our whole goal of flattening the curve was not to save lives per se (see this post for a look at how many problems that has caused us), but to ensure hospital capacity was sufficient to treat people. It seems reasonable to expect that if the number of deaths/day is lower than the number of deaths/day during the peak of a bad flu season, then hospitalizations would also be manageable. In truth, for each coronavirus death, hospital demand is actually less--because of the greater mortality associated with coronavirus. The mortality rate for hospitalized flu patients in 2017-2018 was 7.6%, taking the total number of flu deaths (61,000), and dividing it by the total number of estimated hospitalizations (818,000) (Table 1 above), or roughly for every 13 hospitalizations, there was one death. The ratio of COVID-19 deaths to hospitalizations is higher. In some states, such as Utah, it is as low as 12% (292 deaths/2324 hospitalizations, 1 death for every 7 hospitalization). Whereas in others, like Massachusetts, the rate is an astronomical 72% (8580 deaths/11,855 hospitalizations--3 deaths for every 4 hospitalizations). Where Florida is at 24%, and Georgia at 20%. Not all states report cumulative hospitalizations, but if you remove outliers, such as Massachusetts, New Hampshire and New Jersey, the national average of deaths/hospitalizations is around 25%--and has not changed much over the course of the epidemic (data from the COVID tracking project, here).
It is worth noting that not all COVID-19 (or flu deaths) occur in hospitals. Many occur in long-term care facilities. That must surely have some impact on Massachusetts' ratio of deaths/hospitalizations. On June 24th, Massachusetts reported that 5000 of its deaths (63% of all MA deaths at the time) were in long-term care facilities. As of today, July 28th, even after mass case surges in most states, 5000 deaths is higher than the total recorded COVID-10 deaths of all types in all but 10 states. Table 2 below shows the only states whose cumulative deaths have exceeded those of Massachusetts' deaths in long-term care homes alone--almost all with populations that are multiples of Massachusetts' population.
Table 2: Total Cases, Total Deaths, Deaths/Million for States with more than 5000 Deaths, as of July 30, 2020
Source: Worldometers. Data analysis and visualization, Emily Burns
But I digress. Let us return to hospital capacity. We are hearing a lot about hospitalizations in Texas, particularly in Houston. Let us take a moment to look at what we might expect in terms of daily deaths in Texas during the 2017-2018 flu season, relative to what we are currently seeing with COVID-19.
Figure 8: Daily Texas COVID-10 Deaths, 7-day Average, Overlaid with Estimated Daily 2017-2018 Flu Deaths in Texas
Texas COVID-19 Data source: https://dshs.texas.gov/coronavirus/additionaldata.aspx#
Estimated 2017-2018 Daily flu deaths based on CDC 2017-2018 Flu estimates, population adjusted for Texas, distribution of deaths fitted by Emily Burns.
Based on Figure 8 above, Texas' daily deaths due to COVID are currently above the estimated daily deaths due to flu in the 2017-2018 flu season. But remember, the curve I have fitted is actually more conservative (flatter) than the real flu. The reality is that the peak was probably about twice that high, given the real curve of flu deaths.
If what I have posited above is true, that for each COVID-19 death, there is a lower hospital burden than for each flu death, Texas Hospitals should actually be under less stress now, given a similar number of daily deaths from COVID-19 and flu.
And believe it or not, that is exactly the case. When I set out to look into this, this is the last thing I expected. I had heard the breathless reports about Texas hospitals, which made no sense to me, given a death rate that was orders of magnitude less than that of New York City, and the fact that Houston hospitals were only half full. I had also seen that COVID-19 patients make up less than half of all ICU patients in Houston. And, while the ICU is at 95% capacity, it was running at 89% capacity at the beginning of June when only 10% of the ICU patients were COVID-19 patients.
So, when I started to look into this and found the following articles from the 2017-2018 flu season about Texas hospitals being filled to capacity, and people being treated in tents, it started to make a lot more sense. See the excerpts from articles describing the situation from the 2017-2018 flu season below. No doubt your eyes will also be popping, wondering if perhaps reporters didn't just copy-and-paste from their stories from 2017-2018 for today's articles.
"At Parkland Memorial Hospital in Dallas, waiting rooms turned into exam areas as a medical tent was built in order to deal with the surge of patients. A Houston doctor said local hospital beds were at capacity, telling flu sufferers they might be better off staying at home. Austin's emergency rooms have also seen an influx of flu patients."
In Houston, area hospitals have filled up, including Texas Children’s Hospital, where a quarter of patients are being treated for the flu.
Let's just pause for a moment on that. One quarter, 25% of the patients were being treated for flu. At its peak, only 20% of Texas hospital capacity was being used by COVID-19 patients. And again, another 50% remained (and remains) available. Nationally, the CDC estimates that only 8% of our hospital beds are currently occupied by COVID-10 patients. The question now is, how aberrant is this, really? I certainly had not heard that COVID hospitalizations in Texas were actually less than they were during the 2017-2018 flu, and yet that seems to be the case--unless of course the prior reporting was exaggerated--which is certainly possible.
In order to get another benchmark, let's try to get a sense for peak hospitalizations during the 2017-2018 flu season. Once again, we'll refer back to the CDC numbers that tell us that 808,000 people were hospitalized during the 2017-2018 flu season. Hospitalizations are not emergency room visits, they are very clearly separated in reporting. Conveniently, the average COVID-19 and the average flu hospitalization are about the same--right at a week. once again, we'll spread this out over 20 weeks. Here is what U.S. hospitalizations would look like during the 2017-2018 flu season.
16,000 new daily hospitalizations at the peak. And likely more than 130,000 people hospitalized at the peak--more than twice the 66,000 people we currently have hospitalized for COVID-19. Also, approximately 16% of all U.S. hospital capacity.
This is of course an estimate, but the math seems to work pretty well with everything else we've been teasing apart. Again, it seems that it could indeed have been significantly higher. Wouldn't that have created a significant strain on U.S. hospitals? Why yes, yes it did. And people wrote about it--a lot. See this article about how "Bad flu seasons test U.S. hospitals."
This is not to try and minimize COVID-19, only to point out that there is a very relevant piece of context that is not being brought to bear on the discussion we are having now about COVID-19.
Up until this point in this post, my argument has been that we need a benchmark for what the "flattened curve" is, and that a reasonable benchmark would be keeping deaths and hospitalizations below the peaks for the most recent severe flu season--2017-2018. Of course, the reality of the situation is that we appear to have abandoned the idea of flattening curve--slowing the virus--in favor of stopping the virus. I believe this particular goal post-shifting to be the most egregious of all. Not only do I find it egregious, and quite possibly the greatest abuse of public trust in public health ever witnessed--I find it perversely naïve. What's more I believe that its naïveté costs lives (the rationale for which I will discuss shortly)--not to mention livelihoods, the futures of tens of millions of children and young adults, to say nothing of the arts, civil society, etc., etc..
Why naive? As noted, for the last several weeks, we have been seeing 50,000 to nearly 80,000 new confirmed cases per day. But how many cases are there really? How many cases are being missed? On June 23, CDC director Robert Redfield indicated that their best estimate for the number of cases there had been to date was 20 million--there were 2.4 million observed on June 23rd. Thus at the time, cases had been undercounted by a factor of 10. We now have more than 4.8 million observed cases (double the number cited at the end of June). It is unlikely that we are missing as many cases as at the beginning of the outbreak (in March, seroprevalence data shows that in NYC 99% of all cases were missed), but nor is it likely that we are capturing all of the cases.
So how many new cases per day are there really? How many cases can there be? Well, again, let's take a look at the flu, and see if that gives us any indication of how quickly flu can spread, and what portion of those we typically confirm. As a reminder, in 2017-2018 there were an estimated 44 million symptomatic flu cases. As with coronavirus, flu also has a reasonable number of asymptomatic cases. How many cases per day then, would that be at the peak of flu season, and how many of those cases do we catch?
Figure 10: Estimated Daily New Symptomatic Flu Cases in 2017-2018 Flu Season, Overlaid with Weekly Laboratory Confirmed Cases of Flu in 2017-2018.
Source: Influenza Positive Tests Reported to CDC by Clinical Laboratories, National Summary Visualization of estimated new cases by Emily Burns, based on CDC estimates for 2017-2018 symptomatic influenza cases.
So, at the peak of the 2017-2018 flu season, there would have been no fewer than 900,000 new symptomatic cases per day. At the same time fewer than 22,000 cases were laboratory confirmed in a week during the peak. Thus, during flu season, we identify only 0.3% of new daily flu cases--max. What portion of coronavirus cases are we identifying? A hell of a lot more, but still not all.
As most of us are keenly aware, we are currently identifying on average around 65,000 coronavirus cases. In the past, we have undercounted by a factor of 10. If we were still missing 90%, that would mean that there were approximately 650,000 new cases/day. Well within the realm of possibility, given that this is a respiratory virus, like the flu, and is as contagious, if not more so than flu.
But there is a better way for us to identify what portion of cases we are actually capturing nationally. During April and May, there was a fair amount of seroprevalence data that came out to try and identify how many cases of COVID-19 there had really been. Once you know how many cases there really are, then you are able to get to a mortality rate. Once you have a mortality rate, you can start to back into how many cases you are missing, based on each death.
There are many things that can make such a computation challenging, and introduce error. In the case of COVID, these are a few:
The mortality rate is highly variable, depending heavily upon who gets the disease. Thus, if a large portion of elderly people with co-morbidities get the disease, as happened in NY, NJ, CT, and MA, the mortality rate will be higher, and for every death, there will be fewer cases. By the same token, a state where the disease is predominantly working its way through the younger population will have a mortality rate potentially orders of magnitude less.
Approximately 33% of people who have recovered from COVID-19 do not show antibodies, according to a study performed at Rockefeller University Specifically: "In 33 percent of donors, the neutralizing activity of plasma was below detectable levels. It’s possible that for many in this group, their immune system’s first line of defense [T-cell immunity] had resolved the infection quickly, before the antibody-producing cells were called in." This is corroborated by work done in Sweden at the Karolinska Institut showing the same level of under-representation of symptomatic COVID-19 cases when using antibody tests to perform the analysis, and looking instead at COVID-19 specific T-cells.
Mortality rates are almost always higher at the beginning of an epidemic, due to populations being "surprised", treatment learning curve, and unawareness of who is at greatest risk. All of the seroprevalence reporting up to now is, by definition, early in the epidemic--and no systematic testing has been done since early June, so we can't use that data to help us get a better picture of the mortality.
With these caveats, let's look at the seroprevalence studies that we do have, understanding that these represent worst case scenarios. The CDC has started releasing data from widespread seroprevalence studies in various states and localities. These studies look at donated blood during a fixed window to identify whether the donors have antibodies to a specific disease. In this case, SARS-CoV-2.
Source: https://www.cdc.gov/coronavirus/2019-ncov/cases-updates/commercial-lab-surveys.html; Case and Death Data from https://www.worldometers.info/coronavirus/usa/ and https://www1.nyc.gov/site/doh/covid/covid-19-data.page
From the table above, we can see that the range of mortality rates goes from from 0.17% in Missouri, up to 2%(!) in Connecticut--more than a 10-fold difference. While this could be due to skewed samples, it is more likely due to who got the disease in each of these places. Given that we know that both Connecticut and New York are outliers due to policies that shifted the disease burden to nursing homes, it seems like the best places to look for what our current national mortality rate, now, later in the epidemic, would be South Florida, Utah, and Missouri.
Doing this we would come up with a generalized mortality rate of around 0.25%, compared to the 0.14% mortality rate of the 2017-2018 flu. To get a sense for under-counting, let's now look at what our national mortality rate is, for the deaths and cases we are seeing now. There is about a 7-day lag from reported cases, to death. So let's look at deaths from July 27th-August 2nd (7584), and divide those by the cases reported between July 20th and July 26th (459, 456). This gives us a national mortality rate for the last week of 1.65%.
Thus, if the New York City mortality rate held, we would be undercounting deaths by (1.65%)/(0.69%), or 2.4 times. But if we remember that 1/3 of people who test positive for COVID-19, and fight of the disease don't show antibodies, that changes 0.69% to 0.46%, meaning that we would be undercounting by a factor of four. If we instead look at the 0.25% average of Utah, Missouri and South Florida, we would instead by undercounting by 6.6x. If we again adjust the 0.25% mortality rate to include the 33% of people people who test positive for the virus and fight it off but don't show any antibodies, the number of under-counting rises to 9.9. Thus, the real number of daily new cases we have is between 260,000 (65,000 x 4) and 650,000.
260,000-650,000 new cases every day, despite 67% of U.S. citizens saying they "always" where masks when they go out, and particularly high compliance--from the outset--in states where cases have been surging recently.
So we are seeing a quarter-of-a-million to nearly three-quarters-of-a-million new cases per day. And yet our public health officials are still talking about "stopping" the virus, and the need for stricter mask mandates, despite having nearly 2x higher mask compliance than Canada, and just a hair lower than Germany.
Source: The wording of the YouGov question differed from the question in the Dynata survey.·YouGov/Imperial College of London surveys from June 22-28, via: https://www.nytimes.com/interactive/2020/07/17/upshot/coronavirus-face-mask-map.html
Is this some sort of unique failure on the part of America and Americans? No, it is not. Rather, it is global failure on the part of public health officials to look at this pandemic honestly, and to adjust their strategies as more data has come in.
The variability in mortality rates in different states with different approaches to the virus demonstrates the incredible importance of focusing on saving lives rather than reducing cases. Focusing on cases causes us to take our eyes off the ball. We did it in the northeast, and and in so doing vastly underestimated the spread of the virus, and likely increased its lethality. We don't focus on cases with the flu, we shouldn't be focusing on them with COVID-19. We need instead that our public health officials focus on reducing the overall number of deaths from COVID-19, not tilting at windmills and trying to "stop" a virus that if one looks at it honestly is rampaging through the world virtually unchecked--despite efforts to contain it that are absolutely unparalleled in human history.
There is still time to change the way we approach this plague, and to reduce its lethality. In fact, in nearly half of all U.S. states, the number of deaths due to COVID-19 is still fewer than the number of deaths in those states due to the 2017-2018 flu season. We need not all be like NYC, but if we apply the same deeply flawed policies, we may well be.
The graph above shows the number of deaths in each state, relative to the estimated number of population-apportioned deaths for each state during the 2017-2018 flu season. It also shows that same percentage, had there been no vaccine coverage. 24 of our states have not, or have only just exceeded 100% of the estimated deaths seen in the 2017-2018 flu season. Almost 25%--many of which are in the middle of surges--are still at less than half the number of estimated flu deaths for the 2017-2018 flu season.
The final question really is, how far do we have to go? Does every state need to have the level of death observed in New York, or Massachusetts, before this virus is behind them? The answer is almost certainly no. How do we know? Sweden.
Sweden has logged 5744 deaths as of today, for a population of 10 million, and 568 deaths/million inhabitants. This puts them ahead of their Scandinavian peers (for now), but well behind many of the other large countries in Europe, many of whom are even now seeing cases and deaths surge.
Sweden on the other hand, appears to be "done".
Not only have Sweden's deaths due to COVID-19 gone down to practically zero, since June 8th--approaching two months now--Sweden has reported no excess mortality. Given that there has been no hard lockdown, and very limited mask-wearing (only 14%), this is almost certainly due to herd immunity--but herd immunity at a threshold that is far, far lower than anticipated. How could this be?
Nationally, at the end of May, 6.3% of Swedes showed antibodies for COVID-19. Based on what we saw earlier with the number of people who are exposed but don't generate a large anti-body response, that would mean about 10% nationally. Stockholm was higher at 10%, so again, probably 15% that actually contracted the disease. As we can see from the chart above, the epidemic wasn't done by the end of May, so let's say another 5% or so by the end. Meaning that with just 20% of the population likely to have contracted the disease, the epidemic seems to be finished.
Again, how is this possible? What about the 60-80% that need to get the disease for herd immunity to kick in? Well, maybe the number is not so high after all.
60-80% was predicated on the idea that we were all "immune naive" to this disease, that we had no pre-existing natural defenses. 60% of the U.S. is approximately 200 million people. The reason 200 million people don't get the flu each year is because many of us have various pre-existing immune defenses like T-cells from prior flus that can help us fight off new flus, even if we haven't seen that exact one. Recall that there were 44 million people who got the flu in 2017-2018. 48 million people were protected from the disease due to the vaccine (37% got the vaccine, which was 40% effective), meaning approximately 280 million people were exposed to the disease, and 44 million people got it, for an attack rate of 15%. Had there been no vaccine, an additional 7 million or so would have been sickened, or around 51 million, nationally. Again, the reason it's not 60-80% is because most of us have been exposed to other flus and a good chunk of us can fight off a flu before it even sickens us. It is probably not a coincidence that H1N1 "disappeared" after 60 million people were sickened. Perhaps rather than disappearing, there was a large portion of the population whose prior exposure to other flus resulted in offering a measure of protection to H1N1.
The thinking at the beginning of this pandemic was that since this was a novel coronavirus, and very few people had been exposed to SARS or MERS, that 60% of people would need to get the disease before it "petered out". But that calculus did not take into account the extensive level of population exposure to the common cold--which is also a coronavirus--and that that kind of exposure might offer protection similar to the protection to flu conferred by exposure to prior flus. Now, multiple teams of researchers across the globe have noticed that not only do some of us have T-cells from prior exposure to common cold coronavirus that react with and neutralize this coronavirus--most of us do, more than 50% in fact.
There is the typical "hemming and hawing" about what this means, but Sweden seems to make a pretty strong argument that it may in fact be very important. It has recently been shown that antibody responses are weak in most people, 99% in fact, and that the antibody presence decreases precipitously over time--particularly for mild infections. The T-cell response on the other hand is extremely robust. At this point, everything points to both a robust and typical immune response, with immune indicators waning for those people for whom the disease was experienced as a mild one. Scientific American even went so far as to publish this article: https://www.scientificamerican.com/article/concerns-about-waning-covid-19-immunity-are-likely-overblown/
The clues are falling into place, and the picture seems to be getting more and more clear. The importance of previously existing cross-reactive T-cells in neutralizing SARS-CoV-2 would explain a lot about why children--who have some of the greatest exposure to common cold coronaviruses--do not seem to get the disease as often, nor to get as ill. It also explains the much higher susceptibility of the elderly, whose T-cells are declining.
The problem is, there are a lot of people--a lot of politicians, and a lot of public health officials--who have a really vested interest in this not being true. This is because if Sweden is "done," it means lockdowns likely increased lethality, in addition to the gob-smacking economic damage and the developmental damage done to children. It means people will have to admit they were wrong, that their cure was indeed worse than the disease.
The problem though, and why I feel compelled to write this article, is that many states and countries are prepared to administer another dose of "lockdown" cure, as well as to impose seemingly indefinite mask mandates. There is substantial evidence that lockdowns can increase lethality. Simply on an exposure basis, lockdowns allow those who are wealthy, and hence more healthy and less likely to get seriously ill, to stay out of harm's way, leaving exposed those who are "essential," and often poorer and in worse health. And of course the elderly, who are reliant on help from others, remain exposed.
For evidence supporting this, we need only look at Italy, New York, and Massachusetts. It is clear that you can increase the number of people who get seriously sick from this disease, if the disease is not directed away from those who are at risk. In the town of Bergamo, 57% of the population tested positive for antibodies to the disease. The average age of people testing positive for antibodies was 54. The average age in Italy is 49. Given that mild infections are less likely to give rise to test-visible antibodies, the number of infected is almost certainly higher, and many young people are not showing up in this sample. Simply comparing to Sweden, it looks an awful lot like Italy's lockdowns simply forced their elderly into extremely close quarters with young people who already had COVID-19, thereby ensuring the demise of a large number of those elderly people. Looking at seroprevalence data in Sweden, the number of people showing antibodies over 65 was half the number of those under 65--which would means Sweden's mean age of infection would be lower than its national average, not higher (like Italy's), indicating that Sweden's approach also better protected the elderly.
In Massachusetts, seroprevalence data from Brookline, a wealthy a suburb outside of Boston, showed 7% of people had antibodies--the same proportion as people in Sweden, generally, at the roughly the same time. Yet, Massachusetts' deaths/million at the time were more than 3 times as high--746/million, to Sweden's 223. Massachusetts' deaths/million now stand at 1253, to Swedens 568 deaths/million. And while Sweden looks poised to claim victory over coronavirus, Massachusetts is still mired in partial lockdowns and masks. And Massachusetts is one of the youngest (73% of its population is under 50), healthiest and wealthiest states in the nation.
Some will make the "health" argument, saying that Sweden is a healthier country with no minorities, and we must have lockdowns in the U.S. because our population is not as healthy. That is a red herring. First, as demonstrated by the higher relative age of infection in Italy vs. Sweden, lockdowns do not protect the elderly--they expose them. Second, Massachusetts, at 2/3rd the size of Sweden, has 5000 deaths in nursing home facilities alone. Sweden has 5700 deaths total. Lockdowns do not protect the elderly.
Nor do they protect the poor and minorities. In a prior article, I showed that the penetration of cases by neighborhood in Boston was almost directly tied to level of education. More education, less likelihood of getting the disease, and vice versa--which makes perfect sense when public health policy instructs those who can work from home--typically well-educated knowledge workers--to stay at home. We see a similar trend in Queens, NY, where one clinic registered with 68% of people having antibodies to COVID-19 vs. just over 20% city-wide. Again, if one thinks about this at all linearly this makes sense: poorer people are more likely to have jobs where they cannot work from home, and thus are more likely to be exposed. The fact that poorer people are more likely to have life-threatening co-morbidities and to live in multi-generational homes makes policies that would force them to be the only workers exposed that much more immoral. The only people lockdowns protect are the rich, and they do it at the expense of the poor, which creates more death.
The last question is, where are we on our epidemic curve? Do we even want to know? It seems quite likely that New York, and possibly Massachusetts are quite close to being "done" with COVID-19. And yet there seems to be no interest whatsoever in trying to test that hypothesis and figure out if we might be close to "done." It seems instead that we are absolutely determined to stay in this post-lockdown limbo until there is a vaccine, even in communities where the disease has worked its way most of the way through. Returning to the original premise of this article, this is why I believe that we must establish benchmarks for living with this disease, and those benchmarks ought to be based on something tangible. I can think of no better choice than the flu.
If we do not commit ourselves to living with this disease, we may well end up with its ghost quietly but determinedly, gnawing away the fine fibers that stitch our society together, long after the disease itself has departed.
Predictions: I predict that for the 44 non-hotspot states + DC, who represent 78,000 deaths and 280 million of the U.S. population, that we are 1/2- 2/3rds of the way through this epidemic, in terms of deaths (as long as we don't lockdown again, which I believe would increase the mortality rate again). Our deaths/million people for those 44 states is 278. Thus, I predict it will likely double, not surprisingly, putting those states roughly in-line with Sweden at 566 deaths/million (again, just for that 280 million--the overall number will be higher, thanks to NY, NJ, MA, etc.) This would mean our final death toll will be between 200,000-225,000, nationally. I believe we could decrease deaths significantly by taking an active approach to inform different groups of their risk (which is lower than their risk of death by suicide for a massive swath of the population) and encourage those in low-risk groups who felt comfortable to mix freely, along with guidance to avoid exposure for a few more weeks to higher risk groups. Somewhat comically, if tragically, simply letting kids play through the summer, and 20-and 30-somethings do what they naturally do would have achieved this, in a matter of months, if Sweden is any gauge. The prescription ought still to be the same, but now we no longer benefit from the total separation of kids and adults, as kids go back to school, which is tragic. We have wasted the summer seeking a perfect solution, in the face of overwhelming evidence that no such solution was available. Having done so will cost lives.