Tag Archives: Hospitalizations

Vax Results, Biden Boosters, Delta, and the Mask Charade

19 Thursday Aug 2021

Posted by in Coronavirus, Public Health, Uncategorized, Vaccinations

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If this post has an overarching theme, it might be “just relax”! That goes especially for those inclined to prescribe behavioral rules for others. People can assess risks for themselves, though it helps when empirical information is presented without bias. With that brief diatribe, here are a few follow-ups on COVID vaccines, the Delta wave, and the ongoing “mask charade”.

Israeli Vax Protection

Here is Jeffrey Morris’ very good exposition as to why the Israeli reports of COVID vaccine inefficacy are false. First, he shows the kind of raw data we’ve been hearing about for weeks: almost 60% of the country’s severe cases are in vaccinated individuals. This is the origin of the claim that the vaccines don’t work. 

Next, Morris notes that 80% of the Israeli population 12 years and older are vaccinated (predominantly if not exclusively with the Pfizer vaccine). This causes a distortion that can be controlled by normalizing the case counts relative to the total populations of the vaccinated and unvaccinated subgroups. Doing so shows that the unvaccinated are 3.1 times more likely to have contracted a severe case than the vaccinated. Said a different way, this shows that the vaccines are 67.5% effective in preventing severe disease. But that’s not the full story!

Morris goes on to show case rates in different age strata. For those older than 50 (over 90% of whom are vaccinated and who have more co-morbidities), there are 23.6 times more severe cases among the unvaccinated than the vaccinated. That yields an efficacy rate of 85.2%. Vaccine efficacy is even better in the younger age group: 91.8%. 

These statistics pertain to the Delta variant. However, it’s true they are lower than the 95% efficacy rate achieved in the Pfizer trials. Is Pfizer’s efficacy beginning to fade? That’s possible, but this is just one set of results and declining efficacy has not been proven. Israel’s vaccination program got off to a fast start, so the vaccinated population has had more time for efficacy to decay than in most countries. And as I discussed in an earlier post, there are reasons to think that the vaccines are still highly protective after a minimum of seven months.

Biden Boosters

IIn the meantime, the Biden Administration has recommended that booster shots be delivered eight months after original vaccinations. There is empirical evidence that boosters of similar mRNA vaccine (Pfizer and Moderna) might not be a sound approach, both due to side effects and because additional doses might reduce the “breadth” of the antibody response. We’ll soon know whether the first two jabs are effective after eight months, and my bet is that will be the case.

Is Delta Cresting?

Meanwhile, the course of this summer’s Delta wave appears to be turning a corner. The surge in cases has a seasonal component, mimicking the summer 2020 wave as well as the typical Hope-Simpson pattern, in which large viral waves peak in mid-winter but more muted waves occur in low- to mid-latitudes during the summer months.

Therefore, we might expect to see a late-summer decline in new cases. There are now 21 states with COVID estimated reproduction rates less than one (this might change by the time you see the charts at the link). In other words, each new infected person transmits to an average of less than one other person, which shows that case growth may be near or beyond a peak. Another 16 states have reproduction rates approaching or very close to one. This is promising.

Maskholes

Finally, I’m frustrated as a resident of a county where certain government officials are bound and determined to impose a mask mandate, though they have been slowed by a court challenge. The “science” does NOT support such a measure: masks have not been shown to mitigate the spread of the virus, and they cannot stop penetration of aerosols in either direction. This recent article in City Journal by Jeffrey H. Anderson is perhaps the most thorough treatment I’ve seen on the effectiveness of masks. Anderson makes this remark about the scientific case made by mask proponents:

“Mask supporters often claim that we have no choice but to rely on observational studies instead of RCTs [randomized control trials], because RCTs cannot tell us whether masks work or not. But what they really mean is that they don’t like what the RCTs show.”

Oh, how well I remember the “follow-the-science” crowd insisting last year that only RCTs could be trusted when it came to evaluating certain COVID treatments. In any case, the observational studies on masks are quite mixed and by no means offer unequivocal support for masking. 

A further consideration is that masks can act to convert droplets to aerosols, which are highly efficient vehicles of transmission. The mask debate is even more absurd when it comes to school children, who are at almost zero risk of severe COVID infection (also see here), and for whom masks are highly prone to cause developmental complications.

Closing Thoughts

The vaccines are still effective. Data purporting to show otherwise fails to account for the most obvious of confounding influences: vaccination rates and age effects. In fact, the Biden Administration has made a rather arbitrary decision about the durability of vaccine effects by recommending booster shots after eight months. The highly transmissible Delta variant has struck quickly but the wave now shows signs of cresting, though that is no guarantee for the fall and winter season. However, Delta cases have been much less severe on average than earlier variants. Masks did nothing to protect us from those waves, and they won’t protect us now. I, for one, won’t wear one if I can avoid it.

Herd Immunity To Public Health Bullshitters and To COVID

16 Monday Aug 2021

Posted by in Coronavirus, Herd Immunity, Uncategorized

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My last post had a simple message about the meaning of immunity: you won’t get very sick or die from an infection to which you are immune, including COVID-19. Like any other airborne virus, that does NOT mean you won’t get it lodged in your eyeballs, sinuses, throat, or lungs. If you do, you are likely to test positive, though your immunity means the “case” is likely to be inconsequential.

As noted in that last post, we’ve seen increasing COVID case counts with the so-called Delta variant, which is more highly transmissible than earlier variants. (This has been abetted by an uncontrolled southern border as well.) However, as we’d expect with a higher level of immunity in the population, the average severity of these cases is low relative to last year’s COVID waves. But then I saw this article in ScienceAlert quoting Sir Andrew Pollard, a scientist affiliated AstraZeneca and the University of Oxford. He says with Delta, herd immunity “is not a possibility” — everyone will get it.

Maybe everyone will, but that doesn’t mean everyone will get sick. His statement raises an obvious question about the meaning of herd immunity. If our working definition of the term is that the virus simply disappears, then Pollard is correct: we know that COVID is endemic. But the only virus that we’ve ever completely eradicated is polio. Would Pollard say we’ve failed to achieve herd immunity against all other viruses? I doubt it. Endemicity and herd immunity are not mutually exclusive. The key to herd immunity is whether a virus does or does not remain a threat to the health of the population generally.

Active COVID infections will be relatively short-lived in individuals with “immunity”. Moreover, viral loads tend to be lower in immune individuals who happen to get infected. Therefore, the “infected immune” have less time and less virus with which to infect others. That creates resistance to further contagion and contributes to what we know as herd immunity. While immune individuals can “catch” the virus, they won’t get sick. Likewise, a large proportion of the herd can be immune and still catch the virus without getting sick. That is herd immunity.

One open and controversial question is whether uninfected individuals will require frequent revaccination to maintain their immunity. A further qualification has to do with asymptomatic breakthrough infections. Those individuals won’t see any reason to quarantine, and they may unwittingly transmit the virus.

I also acknowledge that the concept of herd immunity is often discussed strictly in terms of transmission, or rather its failure. The more contagious a new virus, like the Delta variant, the more difficult it is to achieve herd immunity. Models predicting low herd immunity thresholds due to heterogeneity in the population are predicated on a given level of transmissibility. Those thresholds would be correspondingly higher given greater transmissibility.

A prominent scientist quoted in this article is Paul Hunter of the University of East Anglia. After backing-up Pollard’s dubious take on herd immunity, Hunter drops this bit of real wisdom:

“We need to move away from reporting infections to actually reporting the number of people who are ill. Otherwise we are going to be frightening ourselves with very high numbers that don’t translate into disease burden.”

Here, here! Ultimately, immunity has to do with the ability of our immune systems to fight infections. Vaccinations, acquired immunity from infections, and pre-existing immunity all reduce the severity of later infections. They are associated with reductions in transmission, but those immune responses are more basic to herd immunity than transmissability alone. Herd immunity does not mean that severe cases will never occur. In fact, more muted seasonal waves will come and go, inflicting illness on a limited number of vulnerables, but most people can live their lives normally while viral reproduction is contained. Herd immunity!

Sadly, we’re getting accustomed to hearing misstatements and bad information from public health officials on everything from mask mandates, lockdowns, and school closings to hospital capacity and vaccine hesitancy. Dr. Pollard’s latest musing is not unique in that respect. It’s almost as if these “experts” have become victims of their own flawed risk assessments insofar as their waning appeal to “the herd” is concerned. Professor Hunter’s follow-up is refreshing, however. Public health agencies should quit reporting case counts and instead report only patients who present serious symptoms, COVID ER visits, or hospitalizations.

Hooray For Florida!

22 Monday Feb 2021

Posted by in Coronavirus, Public Health

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It’s been said that many of the so-called “heroes” of the COVID pandemic who’ve been celebrated by the media are actually villains, and perhaps Governor Andrew Cuomo of New York should top the list. He saw to it that retirement homes were seeded with infected patients by ordering them returned their care homes rather than admitted to hospitals. Deaths in these facilities mounted, and they mounted faster than Cuomo’s administration was willing to admit. But the media and even Democrat state legislators have begun to take note, which is practically a miracle!

It seems equally true that some vilified by the media for their COVID response are actually heroes. Governor Ron DeSantis of Florida might deserve top honors here. Having spent the last month in Florida, I can attest that the business and social environment here is quite open compared to my home state (despite the presence of a few freaked out northerners who can’t quite fathom how stupid they look wearing masks on the beach). Florida’s infections, hospitalizations, and deaths have been lower than in California, New York, and many other states where lockdown measures have been stringent. (The first chart below is just a little busy…)

As I’ve written for much of the past year, COVID is far more dangerous to the elderly than anyone else, particularly those with co-morbidities. It’s also true that blacks (and some other minorities) are more vulnerable than whites, but if we want to save more black lives, we’re still better off prioritizing the elderly than racial groups. DeSantis understands this, and Florida is among the leaders in vaccinating the elderly population. (States don’t report this data on a uniform basis):

This approach to saving lives is obvious, yet critics at outlets like NBC News insist that DeSantis must be pandering to the senior population in Florida. Well, one wouldn’t want to be responsive to voters who happen to face high mortality risks, right? Others such as horror writer Stephen King have jumped onboard to offer their bumbling public health expertise as well.

There were many experts and the usual collection of numbskulls on social media who were wrong about Florida. DeSantis handled the pandemic as it should have been handled elsewhere. But the propaganda to the contrary goes unabated. For example, this article is pathetic. Can these people be serious? Or are they really that stupid? This goes for the Biden Administration as well, which had entertained the notion of imposing federal travel restrictions on Florida!

The political attacks on Florida and its governor reveal the extent to which opponents wish to ignore the evidence in plain sight. The data on COVID outcomes put the lie to the narrative of a public health emergency requiring massive restrictions on personal liberty. We know those policies are powerless to control the course of the contagion. The pandemic, however, was the key to convincing the public to accept a more authoritarian role for government. It’s a blessing that not everyone bought in, and that there are places like Florida where you can still go about your business in approximate normalcy.

November Pandemic Perspective

18 Wednesday Nov 2020

Posted by in Coronavirus, Pandemic, Uncategorized

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I hope readers share my compulsion to see updated COVID numbers. It’s become a regular feature on this blog and will probably remain one until infections subside, vaccine or otherwise. Or maybe when people get used to the idea of living normally again in the presence of an endemic pathogen, as they have with many other pathogens and myriad risks of greater proportions, and as they should. That might require more court challenges, political changes, and plain old civil disobedience.

So here, then, is an update on the U.S. COVID numbers released over the past few days. The charts below are attributable to Don Wolt (@tlowdon on Twitter).

First, reported deaths began to creep up again in the latter half of October and have escalated in November. They’ve now reached the highs of the mid-summer wave in the south, but this time the outbreak is concentrated in the midwest and especially the upper midwest.

Reported deaths are the basis of claims that we are seeing 1,500 people dying every day, which is an obvious exaggeration. There have been recent days when reported deaths exceeded that level, but the weekly average of reported deaths is now between 1,100 and 1,200 a day.

It’s important to understand that deaths reported in a given week actually occurred earlier, sometimes eight or more weeks before the week in which they are reported. Most occur within three weeks of reporting, but sometimes the numbers added from four-plus weeks earlier are significant.

The following chart reproduces weekly reported deaths from above using blue bars, ending with the week of November 14th. Deaths by actual date-of-death (DOD) are shown by the orange bars. The most recent three-plus weeks always show less than complete counts of deaths by DOD. But going back to mid-October, actual weekly deaths were running below reported deaths. If the pattern were to follow the upswings of the first two waves of infections, then actual weekly deaths would exceed reported deaths by perhaps the end of October. However, it’s doubtful that will occur, in part because we’ve made substantial progress since the spring and summer in treating the disease.

To reinforce the last point, it’s helpful to view deaths relative to COVID case counts. Deaths by DOD are plotted below by the orange line using the scale on the right-hand vertical axis. New positive tests are represented by the solid blue line, using the left-hand axis, along with COVID hospitalizations. There is no question that the relationship between cases, hospitalizations, and deaths has weakened over time. My suspicions were aroused somewhat by the noticeable compression of the right axis for deaths relative to the two charts above, but on reviewing the actual patterns (peak relative to troughs) in those charts, I’m satisfied that the relationships have indeed “decoupled”, as Wolt puts it.

Cases are going through the roof, but there is strong evidence that a large share of these cases are false positives. COVID hospitalizations are up as well, but their apparent co-movement with new cases appears to be dampening with successive waves of the virus. That’s at least partly a consequence of the low number of tests early in the pandemic.

So where is this going? The next chart again shows COVID deaths by DOD using orange bars. Wolt has concluded, and I have reported here, that the single-best short-term predictor of COVID deaths by DOD is the percentage of emergency room visits at which patients presented symptoms of either COVID-like illness (CLI) or influenza-like illness (ILI). The sum of these percentages, CLI + ILI, is shown below by the dark blue line, but the values are shifted forward by three weeks to better align with deaths. This suggests that actual COVID deaths by DOD will be somewhere around 7,000 a week by the end of November, or about 1,000 a day. Beyond that time, the path will depend on a number of factors, including the weather, prevalence and immunity levels, and changes in mobility.

I am highly skeptical that lockdowns have any independent effect in knocking down the virus, though interventionists will try to take credit if the wave happens to subside soon for any other reason. They won’t take credit for the grim lockdown deaths reaped by their policies.

Despite the bleak prospect of 1,000 or more COVID-attributed deaths a day by the end of November, the way in which these deaths are counted is suspect. Early in the pandemic, the CDC significantly altered guidelines for the completion of death certificates for COVID such that deaths are often improperly attributed to the virus. Some COVID deaths stem from false-positive PCR tests, and again, almost since the beginning of the pandemic, hospitals were given a financial incentive to classify inpatients as COVID-infected.

It’s also important to remember that while any true COVID fatality is premature, they are generally not even close to the prematurity of lockdown deaths. That’s a simple consequence of the age profile of COVID deaths, which indicate relatively few life-years lost, and the preponderance of co-morbidities among COVID fatalities.

Again, COVId deaths are bad enough, but we are seeing an unacceptable and ongoing level of lockdown deaths. This is now to the point where they may account for almost all of the continuing excess deaths, even with the fall COVID wave. It’s probable that public health would be better served with reduced emphasis on COVID-mitigation for the general population and more intense focus on protecting the vulnerable, including the distribution of vaccines.

Evidence of Fading COVID Summer Surge

16 Sunday Aug 2020

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Lately I’ve talked a lot about reported deaths each week versus deaths by actual date of death (DOD). Much of that information came from Kyle Lamb’s Twitter account, and he’s the source of the charts below as well. The first one provides a convenient summary of the data reported through last week. The blue bars are reported deaths each week from the COVID Tracking Project (CTP), which are an aggregation of deaths that actually occurred over previous weeks. Again, the blue bars do NOT represent deaths that occurred in the reporting week. The solid orange bars are “provisional” actual deaths by DOD. “Provisional” means that recent weeks are not complete, though most deaths by DOD are captured within three to four weeks. The CDC also produces a “forecast” of final death counts by DOD, shown by the hatched orange bars.   

Note that the recent surge in deaths has been much smaller than the one in the spring, which was driven by deaths in the northeast. The CDC “expects” actual deaths by DOD to have declined starting after the week of July 23rd. However, CTP was still reporting deaths of over 1,000 per day last week. The actual timing of those deaths in prior weeks, and the ultimate extent of the summer surge in COVID deaths, remains to be seen.

Certain leading indicators of deaths are signaling declines in actual deaths in August. Two of those indicators are 1) the positivity rate on standard PCR tests for infections; and 2) the share of emergency room visits made for symptoms of “COVID Like Illness” (CLI). The charts below show those indicators for FEMA regions that had the largest uptrends in cases in June and July. Florida is part of Region 4, shown in the next chart:

Here is the Region 6, which includes Texas:

Finally, Region 8 includes Arizona and California:

Out of personal interest, I’m also throwing in Region 7 with a few midwestern states, where cases have risen but not to the levels reached in Regions 4, 6, and 8:

With the exception of the last chart, the clear pattern is a peak or plateau in the positivity rate in late June through late July, followed by declines in subsequent weeks. The share or ER visits for CLI was not quite coincident with the positivity rate, but close. The decline in the CLI share is evident in Regions 4, 6 and 8. Again, these three regions include states that drove the nationwide increase in cases this summer (AZ, CA, FL, and TX), and the surge appears to have maxed out.     

Here is a chart showing the share of CLI visits to ERs for all ten FEMA region from mid-June through last week. Clearly, this measure is improving across the U.S.

Nationwide, the CLI percentage at ERs has decreased by about 47% over the past four weeks, and the positivity rate has decreased by about 28% in that time. In addition to these favorable trends, COVID hospitalizations have decreased by about 40% over the past three weeks. All of these trends bode well for a downturn in COVID-attributed deaths.

The summertime surge in the virus was not nearly as ravaging as in the spring, and it appears to be fading. We’ll await developments in the fall, but we’ve come a long way in terms of protecting the vulnerable, treating the infected, approaching herd immunity thresholds (which means reduced rates of transmission to susceptible individuals), and the real possibility that we can put the pandemic behind us. 

COVID at Midsummer

04 Tuesday Aug 2020

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It’s been several weeks since I last posted on the state of the coronavirus pandemic (also see here). The charts below show seven-day moving averages of new confirmed cases and reported C19 deaths from the COVID Tracking Project as of August 3. Daily new cases began to flatten about three weeks ago and then turned down (it can take a few days for such changes to show up in a moving average). Daily C19-attributed deaths began climbing again in early July, lagging new cases by a few weeks, and they slowed just a bit over the past several days. Obviously, both are good news if those changes are maintained. The other thing to note is that deaths have remained far below their levels of April and early May.

The daily death count is that reported on each date, not when the deaths actually occurred. Each day’s report consists of deaths that were spread across several previous weeks or even a month or more. That makes the slight downturn in deaths more tenuous from a data perspective. There are sometimes large numbers of deaths from preceding weeks reported together on a single day, so reporting can be ragged and the final pattern of actual deaths is not known for some time. More on that below.

States

The increase in cases and deaths during late June and July was concentrated in four states: Arizona, California, Florida, and Texas. Here’s how those states look now in terms of cases and deaths, from the interactive COVID Time Series site:

 

New cases began to flatten or drop in these states two to three weeks ago, driving the change in the national data. Daily deaths have not turned convincingly, but again, these are reported deaths, which actually occurred over previous weeks. One more chart that is suggestive: current hospitalizations in these four states. The recent declines should bode well for the trend in reported deaths, but it remains to be seen. 

Meanwhile, other parts of the country have seen an uptrend in cases and deaths, such as Illinois, Missouri, South Carolina, and Tennessee. Here are new cases in those states:

It’s worth emphasizing that the elevated level of new cases this summer has not been associated with the rates of fatality experienced in the Northeast during the spring. There are many reasons: better patient care, new treatments, more direct summer sunlight, higher humidity, and tighter controls in nursing homes.

More On the Timing of Deaths

Back to the discrepancies in the timing of reported deaths and actual deaths. This is important because the reported totals each day and each week can be highly misleading, even to the point of frightening the public and policy makers, with consequent psychological and economic impacts.

The latest summary of provisional vs. reported deaths is shown below, courtesy of Kyle Lamb, who posts updates on his Twitter feed. This report ends with the last complete week ending August 1. It’s a little hard to read, but you might get a better look if you click on it or turn your phone sideways. Some of the key series are also graphed below. 

The table shows the actual timing of deaths in the fourth column, with dates alongside. The pattern differs from the statistics reported by the Covid Tracking Project (CTP) in the top row (shaded orange), and from the totals of actual deaths by reporting day in the third row (shaded gray). The reporting dates are always later than the dates of death. This can be seen in the chart below. The most obvious illustration is how many of the deaths from around the peak in mid-April were reported in May. In March and April, the daily reports were short of the ultimate actual death counts because so few deaths with associated dates were known by then.

 

The right-hand end of the red line shows that many deaths reported by CTP have not yet been placed at an actual date of death by the CDC.  At this point, the actual date of death has not been placed for over 10,000 deaths! Again, those will be spread over earlier weeks.

The blue line is dashed over the last four weeks because those counts are most “highly” provisional. Small changes in the actual counts are likely for dates even before that, but the last four weeks are subject to fairly substantial upward revisions. Eventually, the right end of the blue line will more closely approximate the totals shown in red.

To get an indication of trends in the actual timing of deaths, I plotted the weekly actual deaths reported for the last four reporting weeks going back in time. In the table, those are the four lowest, color-coded diagonals. In the graph below, which should include the qualifier “by recency of report week”, actual deaths in the most recent report week are represented by the blue line, the prior weekly report is red, followed by green (three weeks prior), and purple (four weeks prior… sorry, the colors are not consistent with those in the table). The lines extend farther to the right for more recent report weeks.

The increase in actual deaths occurring in July has declined or flattened in each of the four most recent report weeks. Only the second-to-last week increased as of the August 1st report. On the whole, those changes seem favorable, but we shall see.

Closing

It’s getting trite to say, but the next few weeks will be interesting. The increase in deaths in July was a sad development, but at least the extent of it appears to have been limited. Even with a somewhat higher death count, the fatality rate continued to decline. Let’s hope any further waves of infections are even less deadly.

Case Fatality, Stale Ratios and Exaggerated Loss

14 Tuesday Jul 2020

Posted by in Analytics, Pandemic

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I hope someday I won’t feel compelled to write or worry about the coronavirus. However, as the pandemic wears on, it seems to take only a few days for issues to pile up, and I just can’t resist comment. Today I have a couple of beefs with uses of data and concomitant statements I’ve seen posted of late.

People are still quoting case fatality rates (CFRs) as if those cumulative numbers are relevant to the number of deaths we can expect going forward. They are not. Just as hair-brained are applications of cumulative hospitalization and ICU admittance rates to produce “rough and ready” estimates of what to expect going forward. Or, I’ve seen people express hospitalizations as ratios to CFR, as if those ratios will be the same going forward. Again, they are not. Let me try to explain.

The chart below shows the course of the U.S. CFR since the start of the pandemic. It’s taken from the interactive Covid Time Series site. My apologies if you have to click on the chart for decent viewing (or you can visit the site). The CFR at any date is the cumulative number of deaths to-date divided by the cumulative number of confirmed cases. It is a summary of past history, but it is not well-suited to making predictions about death rates in the future. The CFR began to taper a little before Memorial Day, and it is now at about 4% (as of July 13).

Out of curiosity, I also generated CFRs for AZ, CA, FL, GA, and TX, which now average about half of the national CFR. There’s an obvious lesson: if you must use CFRs, understand that they vary from place to place.

Again, CFRs are cumulative. Their changes over time can tell us something about recent trends, but even then they are flawed. For example, case counts have risen dramatically with more widespread testing. Those testing positive more recently are concentrated in younger age cohorts, for whom infections are much less severe. Treatment has improved dramatically as well, so there is little reason to expect the CFR’s of recently diagnosed cases to be as high as the latest CFRs shown above.

There is no easy way to calculate an unflawed “marginal” CFR for a recent period, though an effort to do so might improve the predictive value. Deaths lag behind case counts because the progression from early symptoms to death can take several weeks. Even more vexing for constructing a valid, recent fatality rate is that reporting of deaths is itself delayed, as I explained in my last post. Each day’s report of deaths captures deaths that may have occurred over a period of several weeks in the past, and sometimes many more.

Finally no CFR can capture the true mortality rate of the virus without ongoing, ubiquitous testing. As the state of testing stands, the true mortality rate must reflect undiagnosed cases in the denominator. The CDC’s latest “best” estimate of the true mortality rate is just 0.3%, and 0.05% for those aged 50 years or less. Those figures are based on serological tests for the presence of antibodies to C19 in more random samples of the population. Those findings reflect the extent of undiagnosed and/or asymptomatic cases.

The point is one shouldn’t be too blithe about throwing numbers around like 4% mortality based on the CFR, or even 1% mortality as a “nice, round number”, without heavy qualification. Those numbers are gross exaggerations of what we are likely to see going forward.

The same criticisms can be leveled at claims that hospitalizations will proceed at some fixed ratio relative to diagnosed cases, or some fixed ratio relative to deaths. Again, new cases tend to be less severe, so hospitalizations are likely to be a much lower ratio to cases than what is reflected in cumulative totals. Because of improved treatment, the ratio of deaths to hospitalizations will be much lower in the future as well.

CFRs are not a useful guide to future COVID deaths. The true mortality rate is a much better baseline, particularly for subsets of the population matching the current case load. Finally, and this is the only disclaimer I’ll bother to provide today, we all know that suffering is not confined to terminal cases, and it is not confined to the hospitalized subset. But don’t exaggerate the extent of your preferred interpretation of suffering by applying inappropriate cumulative calculations.