COVID-19: Down the Rabbit Hole of Death Rate Calculations - Part 7

Note: Written June 30th

Based on my calculations [see my last post], which were based on antibody testing done in New York where they calculated "19.9% of the population of New York City had COVID-19 antibodies," my worst case death rate if we do nothing more than wait for herd immunity we would see 1.97 to 2.68 million of our fellow Americans dead assuming 70% needed to reach herd immunity.

I know that number will be less than that based on the fact that some of us are taking precautions as well as changes in the way we treat the sick resulting in less deaths. How much of a decrease in my numbers depends on a lot of factors, so worst case of doing nothing will see a lot of deaths, even if you reduce the 70% needed down to 50% for herd immunity.

How does the antibody testing done in New York compare to the testing they did in Boston?

Mayor Martin J. Walsh, together with Massachusetts General Hospital (MGH), and the Boston Public Health Commission (BPHC), today announced the study to evaluate community exposure to COVID-19 through a representative sampling of asymptomatic Boston residents resulted in 9.9% testing positive for antibodies and 2.6% of currently asymptomatic individuals testing positive for COVID-19. In conclusion, approximately 1 in 10 residents in this study have developed antibodies and approximately 1 in 40 currently asymptomatic individuals are positive for COVID-19 and potentially infectious. 

Let's do the same process as we did in the last post.

More than 5,000 residents living in East Boston, Roslindale or within the boundaries of zip codes 02121 and 02125 in Dorchester were invited to voluntarily participate in the study, with total outreach representing more than 55% people of color. Approximately 1,000 residents expressed interest in participating and 786 residents were deemed eligible. Of those, 750 residents enrolled in the study and received the required testing. Residents with symptoms or a previously positive COVID-19 test were disqualified from the study.

So...1 in 10 residents in this study have developed antibodies EXCLUDING those who have tested positive and recovered. Let's settle on a date of May 15 for the News Release and May 12 for the last day they looked for antibodies.If we say that everyone's outcome was resolved on May 12, you either had antibodies or you were dead. And if you died, we got your death reported at least by May 19 (7 days).

If this is the case, then the numbers look like this for the IFR:

Source

Interesting...An IFR of 0.86% was also calculated with NYC.

Right now we have so reasonable data from which to work. We know dates, we now counts, we can make some calculations and we can make some assumptions. It is unfortunate that we will not know a 'true' number until the end. What we can know are reasonable numbers based on what we see, and, using those number we can rule some things out.

Looking at the data from Boston and NYC as well as the current data as of 6/26/2020, here is what we get:

For Boston, we had 11,395 confirmed cases for 69,458 (10% of Boston) assumed  past infected.Using these two numbers, we can assume that for every known case (11,395) the are 6.095 times more cases that were not reported.

Using those numbers, we can get an estimate as to how close out calculated IFRs are using US data for the whole country.

If our infected to confirmed cases ratio is correct, then the best fit for the deaths reported is an IFR of 1.17%

If we use NYC's numbers, the ratio of confirmed cases to assumed past infected is 1 to 10.02:

However, that ratio estimates way more deaths then reported.

If we work backwards, that is we have the reported deaths and we have an estimate of infected (as of 5/25), the IFR calculates as follows:

Assuming nothing has changed, that is the same number of non-tested people remains as per Boston (1:6) or NYC (1:10) we should be able to see very close numbers now that we are into this epidemic by another month.

We have death data as of June 26. Assuming there is at least a 14 day lag between a case being reported, death, and posting of that death, we can look at the cases as of June 12.

In my opinion, the best model that takes into account what we have seen based on reporting is the Boston numbers. That is, for every confirmed case there will be six unconfirmed positive cases. 

The IFR, however, is a different story. In the beginning it was 1.17% that would get you close to the actual deaths reported. As time goes on, that number drops to 0.8%.

There are a couple of things going on here that could be impacting the IFR:

1. The number of deaths from COVID is decreasing due to better care
2. More vulnerable have died already 
3. Less older folks are getting infected with youth driving up the numbers and therefore less deaths from that age group.
4. The number of confirmed cases now includes more asymptomatic cases that were not known to be infected early on (decreasing the ratio to be less than 1:6)

To my way of thinking, we should not see to big of a jump in the IFR between what was calculated before May 11 and what has happened from May 12 to June 28 (the latest date I have access to).

The estimated IFRs should hold close, If the IFRs do not calculate the deaths seen from this period, something has changed. I suspect it is all four of the above with number four being the primary driver as we ramp up testing.

Let's go down that rabbit hole and see.

Part 8

COVID-19: Down the Rabbit Hole of Death Rate Calculations - Part 10

Note: Written July 7

Where are we? Oh yeah, something about deaths only 5.9% above normal...

That statement was made when the graph below ended at the right edge of the purple box. Since this series of blog posts is taking me a bit longer than I thought to write, the number of cases - and therefore deaths - keeps marching on.

The June 25 number of deaths was 127,532. Assuming a 14 day lag from reported cases to reported deaths, the last cases would be June 11 and at that time the numbers were 2,091,812 total COVID-19 cases reported.

We once again come back to the Infection Fatality Rate (IFR) and that brings us back to how many people were actually infected, not the number reported as cases.

In a previous post I calculated that Boston shows us 6 not reported to each reported. New York City shows 10 infected to each one reported as a cases. Those numbers were early on and I think them appropriate for what took place in April. I believe we have a better understanding of what is taking place looking at the Diamond Princess for example.

In this video, a screen shot of the data he was using shows this:

Let's assume that these numbers represent the population in the US as of June 11. What this video contends is that 37 + 21 = 58 out of 155 asymptomatic had the antibodies, Of those that had symptoms, 25 tested positive for the virus.

Assuming that those with symptoms go and get tested, and only positives are reported, we would have 25 reported and 58 unreported. So for every positive reported cases there are actually 2.32 more cases.

25 reported, 58 unreported for a total of 83 infected.

Using this logic...

The number of cases from June 12 through June 22 was 298,465. If we add to that 2.32 times more asymptomatics that were assumed not tested, that gives us a total of 990,904 infected.

With a total of 5447 deaths for that time period (14 day lag in reporting), we get a Case Fatality Rate of of 1.83% and an Infection Fatality Rate of 0.55%.

If my calculations are correct...if my assumptions are correct, by July 20th we will have 24,102 deaths from the cases reported after Memorial Day, May, 25 - a 56 day time frame.

• Cases after May 25 through July 6 = 1,320,681
• Estimated Infected = 4,384,661 (tested plus untested asymptomatic)
• Estimated deaths for this 56 day period starting on June 8th: 24,102
• Actual deaths reported from June 8 through July 20th - 56 days: ????

Out of the rabbit hole for a bit. 

I'll be back after July 20th to see how close my estimate of the deaths were. If it is more than what I calculated then the IFR is higher and we can expect a lot of deaths as we try to get closer to herd immunity of most likely 70% of the US population needing to be infected.

Part 11 coming after July 20th....

COVID-19: Down the Rabbit Hole of Death Rate Calculations - Part 9

Note: Written July 7

You are Donald Trump...or any other non-science/non-public health educated person in a leadership role.

You are looked at to do something...take precautions to keep people safe or hurt the economy. It is not an either/or decision, but unfortunately that is how it was presented.

You may have your own opinions about risk, about life and death...but in the end, the decisions you make are, at the very least, backed up by data that you can point to an say "...this is what I was told."

I am on my 9th post on this topic of the death rate associated with COVID-19 because the data has been contaminated and manipulated to paint a picture that ...it aint that bad...its no different than the flu...it will fizzle out...

So you are Donald Trump and your CDC gives you this 49 page report, that - let's be honest - you will not read. But you can look at the pretty graphs, and the graph you fixate on - because its the one that most people are concerned with - is the number of deaths.

And the CDC shows you this:

Even Trump can look at this graph and come away with an idea as to what is happening. But just to make sure, the CDC tells him:

This is the ninth week of a declining percentage of deaths due to PIC; however, the percentage remains above the epidemic threshold of 5.9% for week 25.

Trump: "What's that mean?"

CDC: "Well boss, we are now only 5.9% higher in deaths then we normally would see at this time."

Trump: "6%...so we are pretty good...would you say tremendous maybe?"

CDC: Well if you look at the data, 6.9% of all the deaths that week were due to pneumonia, influenza or COVID-19.

Trump: So 7% of the deaths were not just from COVID-19...its also the flu and pneumonia?

CDC: Well sir, if you look at how things were in 2018 with just the flu, we are less deaths than that...

Sycophants: And no one shuts down the economy over the flu!

Me: But sir...let me explain what's happening....

Sycophants: Shut up Bowman!

Well this is my blog and I don't have to shut up. Let's look at the graph with some context.

In a bad year, like 2018, the flu infected an estimated 45,000,000  Americans - and that was with a large percentage of people having received a flu shot.

On June 20th we had a total of 2,334,098 confirmed cases of COVID-19. Now go back and compare the the flu death bump for 60 million infected with the COVID-19 bump with as of June 20th 2.3 million infected.. .

When the CDC tells the people that as of June 25th we are only 7% higher than what we normally see at this time for all deaths it sort of sounds like we got it under control! We win! USA...USA...

That graph tells us a percentage of death due to infection. This works well if your goal is to convince the public that the red line is now about the same as what we see with the flu...and no one shuts down the economy for the flu. 7% higher...Pffft!

In the end though, it is all about the deaths that I am interested in. And right now, we are looking at this:

• 127,532 deaths as of June 25 from 2,091,812 COVID-19 cases as of June 11 (14 day death lag)
• 46-95,000 deaths from 45,000,000 cases for flu 2017-2018.

...and Mr. Toad's Wild Ride is not over yet.

When the CDC tells the president that we are only 5.9% higher in deaths then we normally see at this time, it sounds...reasonable. But it ignores the epidemic part of the equation...it ignores exponential growth...

The purple box represents what we assume to know about the number of deaths as of June 25th from the cases starting at 0 to June 11th.

The purple box represents 127,532 deaths. What will we expect from the red box? What will we eventually see at the end date of zero infections and the last death associated with COVID-19?

This is what the infection fatality rate becomes critical in the thinking process. If 5.9% above is acceptable because its only 5.9% above what we normally see, and below what we accept with the seasonal flu, then maybe all this worry over it being a concern is misplaced.

5.9% was the right edge of the purple box. What does the red box tell us?

This rabbit hole is way too deep!

Part 10

COVID-19: Down the Rabbit Hole of Death Rate Calculations - Part 8

Note: Written June 30th

How do the number of deaths to number of reported cases to my calculated IFR and my calculated cases/infection ratio pan out?

Something changed...

I suspect that the decrease in the CFR is based on more testing, younger people getting infected, and better health outcomes. All of these are good news, but make it difficult to figure out what the Infection Fatality Rate (IFR) is if our leaders really push for herd immunity as our way out.

The IFR can give us an estimate of the number of deaths we could expect if all we do is wait for herd immunity to control the infection.

My best guess as to the number of infected is based on what we found with the Diamond Princess and the Aircraft Carrier Theodore Roosevelt. What we see there, one with old folks and one with healthy young people, should give us an idea on what is happening in the US as a whole.

For the Diamond Princess:

Among 3,711 Diamond Princess passengers and crew, 712 (19.2%) had positive test results for SARS-CoV-2 (Figure 1). Of these, 331 (46.5%) were asymptomatic at the time of testing. 

For the USS Theodore Roosevelt:

A new U.S. study of the COVID-19 outbreak aboard USS Theodore Roosevelt (CVN-71) found that one in five sailors infected with the virus were asymptomatic, while the loss of smell and taste were the most common symptoms of the disease.

This asymptomatic number is critical in figuring out who is getting tested and who is not. In both these cases testing was provided to all and was not reliant upon a person going in to be tested.

We do know that because of the shortage and cost of tests, many asymptomatic people who knew they were exposed were discouraged from testing or chose not to partake.

We can assume, therefore, that if for every one symptomatic case on the Diamond Princess there was an asymptomatic infected person, for every one case reported you have twice that many infected.

If you look at the USS Theodore Roosevelt, symptomatic appears to include "loss of smell and taste." I speculate that in the US population those symptoms would not drive many people to get tested. Unfortunately, the research does not break down what percentage of those tested who positive only reported loss of taste & smell or mild symptoms. What they do report is that for every five symptomatic, one was asymptomatic.

Based on the Boston numbers, I am going to assume an IFR of 0.86%. I think that is reasonable in calculating an upper bound number of deaths should herd immunity be the only course of action.

Using the most recent numbers from above, here is what I calculate

What these two tables attempt to show is how close my model (IFR and Ratio of cases to asymptomatic non-reported cases) can get to the numbers reported in WorldoMeters.

There are a lot of assumptions here, but I am looking to get a reasonable estimate so as to calculate what herd immunity might cost us in lives lost to COVID-19.

As time goes on, I think we will - or have - seen less vulnerable people getting infected as well as better health outcomes for those who are symptomatic.

I also think we are testing more and capturing more asymptomatic people which increases the known cases reported.

This assumption seems to be borne out by my calculations as they switch (more known cases both symptomatic/asymptomatic) and an IFR of 0.86%.

What does this all mean?

Well we need to consider an IFR of at least 0.86% ans we need to recognize that the number of reported cases does not equal the number of total infected. More testing is needed to know how many have had COVID so we can know if out health care has improved and what risk of death we can expect.

Need more data!

Down the rabbit hole of death we go. Time to meet the President.

Next Post Part 9

COVID-19: Down the Rabbit Hole of Death Rate Calculations - Part 6

Note: Written June 26th

Using information from the Diamond Princes, I calculate an Infection Fatality Rate (IFR) of 5.6%. This assumes that the total number of infections can be obtained by knowing the number of confirmed cases and adding 17.9% more to cover the asymptomatic cases that were never tested because,,,well they never knew they were sick.

The issue with this IFR calculation is that it is based on what took place within a population with a median age over 60.

Need more better data!

The WorldoMeters calculates a mortality rate by looking at antibody testing performed in New York and the data out of New York they reported this.

Considering that a large number of cases are asymptomatic (or present with very mild symptoms) and that testing has not been performed on the entire population, only a fraction of the SARS-CoV-2 infected population is detected, confirmed through a laboratory test, and officially reported as a COVID-19 case. The number of actual cases is therefore estimated to be at several multiples above the number of reported cases. The number of deaths also tends to be underestimated, as some patients are not hospitalized and not tested.

Based on the "analyzed the data provided by New York City, the New York State antibody study, and the excess deaths analysis by the CDC. Combining these 3 sources together we can derive the most accurate estimate to date on the mortality rate for COVID-19, as well as the mortality rate by age group and underlying condition."

The survey developed a baseline infection rate by testing 15,103 people at grocery stores and community centers across the state over the preceding two weeks. 

What they found was:

• 12.3% of the population in the state had COVID-19 antibodies as of May 1, 2020.
• 19.9% of the population of New York City had COVID-19 antibodies.

Looking at New York City...:

With a population of 8,398,748 people in NYC, this percentage would indicate that 1,671,351 people had been infected with SARS-CoV-2 and had recovered as of May 1 in New York City. The number of confirmed cases reported as of May 1 by New York City was 166,883, more than 10 times less.

What this says to me is that if you were tested and found with antibodies you were past the active infection period and on May 1 you had ether recovered or died. I am going to calculate this a bit different than WorldoMeters because I am going to assume the deaths on May 1 happened 7 days prior. So I am going to look at the deaths reported in NYC on May 9. [I am using 7 days to account for the lag in reporting the deaths, so if we stop on May1 our deaths should - should - be accounted for in the May 9th report]

The IFR is therefore [total deaths] divided by [total recovered plus total deaths].This gives me the pool to pull from, what portion died and what portion survived.

If the New York serum data is correct, we have an IFR of 0.86 to 1.17%. What this could mean in terms of deaths of real people should we continue down the path of waiting for herd immunity as the stopping point looks like this:

If my calculations are correct, and y'all can check my math, if we rely on herd immunity - as some are recommending - we could see 1.97 to 2.68 million of our fellow Americans dead assuming 70% needed to reach herd immunity.

Maybe Boston presents a better estimate...

Down the rabbit hole to Boston we go!

Part 7

COVID-19: Down the Rabbit Hole of Death Rate Calculations - Part 5

Note: Written June 26th

Where are we, as of this date - June 26 - in terms of our outlook for death from COVID-19 in the US?

I can state pretty conclusively using data from all over the world that at least 2 people will die from every 100 confirmed cases of COVID-19. I showed that in my past blog. This calculation, we are told, is the Case Fatality Rate (CFR). The Big Kahuna we need to know is the Infection Fatality Rate (IFR).

The IFR is the number of deaths divided by the number of people who contacted COVID-19, This includes those that are symptomatic and those that are asymptomatic. The conventional wisdom appears to be that more people are symptomatic -and therefore - not tested.

There seems to be supporting evidence for this which I want to look at.

Arons et al. now report in the Journal an outbreak of Covid-19 in a skilled nursing facility in Washington State where a health care provider who was working while symptomatic tested positive for infection with SARS-CoV-2 on March 1, 2020. [New England Journal of Medicine]

Here we have a 'ground zero' situation. The assumption here is that this one worker passed on the infection to those in the nursing home. 

Residents of the facility were then offered two facility-wide point-prevalence screenings for SARS-CoV-2 by real-time reverse-transcriptase polymerase chain reaction (rRT-PCR) of nasopharyngeal swabs on March 13 and March 19–20.

12 days later the residents were tested with the nasal swab looking for the virus - not the antibodies.

Among 76 residents in the point-prevalence surveys, 48 (63%) had positive rRT-PCR results, with 27 (56%) essentially asymptomatic, although symptoms subsequently developed in 24 of these residents (within a median of 4 days) and they were reclassified as presymptomatic.

This is for the residence - which being in a nursing home - would most likely be impacted by a viral infection like COVID-19.

An important finding of this report is that more than half the residents of this skilled nursing facility (27 of 48) who had positive tests were asymptomatic at testing.

 Yet, four days later 24 of those 27 became symptomatic. The reason this is deemed important in this study is they are looking at transmission. I am looking at how many asymptomatic folks would likely never get tested and should be added to the denominator when we do the IFR calculation.

Looking at this data, it would appear that only three out of 76 would never have bothered to get tested as they remained asymptomatic. But these are old people in a nursing home so they are possible much more susceptible to the virus. Still, we can add this to the list in trying to understand what is actually happening in the real world.

Let's look at older people that are not in nursing homes. The Diamond Princess is, I think, the best example of what we might see in the rest of the world. The only exception here is that the passengers were older than the general population and therefore possibly more likely to show symptoms.

On 5 February 2020, in Yokohama, Japan, a cruise ship hosting 3,711 people underwent a 2-week quarantine after a former passenger was found with COVID-19 post-disembarking. As at 20 February, 634 persons on board tested positive for the causative virus. We conducted statistical modelling to derive the delay adjusted asymptomatic proportion of infections, along with the infections’ timeline. The estimated asymptomatic proportion was 17.9% (95% credible interval (CrI): 15.5–20.2%). [Stanford]

Let's assume that for every confirmed case there are at least 17.9% that we do not know about. What we would now see as the IFR - confirmed cases and asymptomatic never tested - would look like this as of today - June 26:

This assumes that the deaths reported as of June 26 came from the reported cases as of June 4 (see previous post on why 22 days). Based on the reported cases we can add 17.9% more cases we suspect are asymptomatic and were never tested.

Still...this is old people data. Let's look at something a bit more representative.

Dig deeper! The rabbit is in there somewhere!

Part 6

COVID-19: Down the Rabbit Hole of Death Rate Calculations - Part 4

Note: Written June 25th

The Infection Fatality Rate - IFR - is the key to the models. And the models are what we use to determine the effort we need to put into our response.

That 'F' is a person...a child, parent, grandparent...a friend. We owe it to them to get the response as close to perfect as we can. "Oops, I was wrong" ain't gonna cut it here.

Last post we talked about the German study that said the IFR was 0.37%. We previously talked about the CDC using 0.26%. The other paper NPR referenced puts it at 0.64%.

The CDC's current "best guess" is that — in a scenario without any further social distancing or other efforts to control the spread of the virus — roughly 4 million patients would be hospitalized in the U.S. with COVID-19 and 500,000 would die over the course of the pandemic. [NPR]

To get 500,000 deaths you will need this many infected people at these different IFRs. The lower the IFR the more people who can be infected to get the same number of deaths, 500,000:

We need to know what that IFR is, because the number of infections does not care about the number of deaths, until the number of deaths limits the number of people who can be infected. At some point we reach this thing the call herd immunity where the number of previously infected gets in the way of the virus finding new uninfected people.

We have three things in play here. One, the virus dies out because of weather. Second is we reach herd immunity or third, we develop a vaccine. All of these things can be in play at the same time and that impacts the number who will get COVID-19.

In the US population there is some unknown number of people who COULD get COVID-19. In the absence of anything else coming into play, we can reasonably make a guess that at some percent of the population we will have reached herd immunity and the virus will go "poof" and "be on its way out."

 According to the Mayo Clinic:

Even if infection with the COVID-19 virus creates long-lasting immunity, a large number of people would have to become infected to reach the herd immunity threshold. Experts estimate that in the U.S., 70% of the population — more than 200 million people — would have to recover from COVID-19 to halt the epidemic.

Let's go with that number of 200,000,000 people needing to get infected in the US for herd immunity to be viable:

My concern is that the ACTUAL number for the IFR is at least 1%. If I am correct, then to reach herd immunity at least 2,000,000 Americans will die. Now that number is if we do nothing but rely on herd immunity. If you look at compliance with social distancing and mask wearing, we are probably about 50% compliant based on what I see day-to-day.

Diving deeper into the rabbit hole of death rate, let's look at some numbers.

First, let's agree on some time frames. From the CDC:

• Time from exposure to symptoms onset: 6 days on average.
• Time to seek care as an out patient: 2 days average.

...and for Mr. Death:

Let's do death math!

We need to make some assumptions here. The numbers we get are not perfect, but with so much data things should wash out and be close to what we need to say confidently this is what we are seeing.

We need to assume that the positive tests we had on May 1 either recovered or died. And if they died, we knew about those deaths by May 21.

Now you may be saying, but Bowman, you are calculating the Case Fatality Rate with this! Yes...but I want to figure out the Infection Fatality Rate by understanding how many untested people we have that were also infected with COVID-19 by using these known cases.

I have three ways to get an estimate of that number that I think are both valid and "good enough" to get an idea. We can look at the data from the Diamond Princess cruise ship, Testing done in New York looking for antibodies, and testing in Boston looking for antibodies.

These assumptions above will get me to a number of deaths from COVID-19 that I need to calculate the IFR. Once I know the number of asymptomatic cases that have not received a positive case result I should be able to get a ratio of tested to the total infected. So if you say we had 22,258 new cases on June 2, we would have some number more of asymptomatic people that were not tested.

That ratio, I think, should remain constant only changing when we test more asymptomatic people and catch them with the virus. As an example, if 10% of the population had the antibodies with a population of 100,000, then 10,000 people were infected. If on that date we go two days into the future the accumulated number of those testing positive will give us a ratio. So if  one thousand tested positive, you could say that for every one positive test there are nine other asymptomatic.

Now whether the pool should be asymptomatic plus confirmed or assume that the 10% includes all of those who were infected is open for debate. I can run the numbers both ways to get an idea.

If this ratio holds over time, then I can get an estimate of how soon we should hit herd immunity. The problem here is that I don't know how many COVID-19 tests looking for the virus are now finding those asymptomatic that would not have been tested when this data was collected in New York and Boston.

I suspect that since we are doing more testing we are catching the asymptomatic that went for testing because they knew they were exposed. If that's the case, then later on the cases reported will now be closer to the all infected and my ratio will not be valid.

So here we go deeper, deeper, and deeper into the rabbit hole of death rates by heading over to Boston.

Part 5

COVID-19: Down the Rabbit Hole of Death Rate Calculations - Part 3

Note: Written June 25th.

Before we continue, let's go over why I think this death rate is important to get right and to understand. From the NPR article:

The new CDC guide for modeling deaths and hospitalizations underscores how politicians, government officials and medical experts still disagree on how deadly the coronavirus is and how much caution to take when reopening the nation's economy.

If the CDC is telling our politicians, hey...its 0.27%...someone is going to whisper in their ear that that number is not that much higher than the flu, and nobody shuts down the economy for the flu do they!

If you are going to model how much death and hospitalizations we can expect, and you plug in that number of 0.26% for your Infection Fatality Rate (IFR) then you are going to calculate a lower number of deaths than what we will actually see.

How then should we look at the Infection Fatality Rate? How about we use data provided by the Imperial College of London's COVID-19 Response Team? Let's look at their report dated 24 May 2020

The number of deaths is then a function of the number of infections and the infection fatality rate (IFR).

Deaths = Number of Infected x IFR.

The CDC tells us this on their FAQ page:

The mortality rate is the percentage of people who died due to COVID-19 out of the total number of people with COVID-19 reported. Since this is an ongoing outbreak, the percentage can change daily. There are several reasons for this, such as there may be delays in reporting of additional confirmed cases of COVID-19 and not all COVID-19 cases will be detected.

That's the Case Fatality Rate. Basically it says, as of  recently, at least two out of every 100 positive tests will result in death.

That is consistent throughout the world. That's how the data works out using the formulas provided.

But there is a different story going on here that is also important, and that's the actual number of people who get COVID-19. This number includes all of those who were tested plus all of those who were asymptomatic and were not tested.

That's the IFR, and that's the number that seems to be the one that gets held up as "see, it's not that bad."

From a public health point of view both numbers are important I feel. The CFR tells me what my hospitals will expect and the IFR tells me how close we may be getting to herd immunity. Basically, the more people with antibodies the less people that can get the disease.

Why is there a difference in the number of reported cases and the number of people with the antibodies? Well it has to do with the testing that is done.

• The first type is a diagnostic test. This type of test tells you if you have a current infection by looking for parts of the virus itself. Swabs that take samples from the back of the nose, mouth, or lower respiratory tract are used for these tests. FDA-authorized diagnostic tests for SARS-CoV-2 are accurate for finding a current infection. This means a positive or negative result from a test is likely to give you a true test result.

• The second type of test is a serology (or antibody) test. These tests tell you if you had a previous infection by looking for antibodies in the blood.  Antibodies are proteins made by the immune system when a germ enters a person’s body. Our immune systems help us fight off germs and diseases. The test uses a blood sample to look for antibodies made in response to SARS-CoV-2 rather than looking for the virus itself. It usually takes 1-3 weeks for the body to make antibodies in response to an infection. We do not know how long the antibodies stay in the body after the infection is over. Serology tests have limited ability to diagnose COVID-19 and should not be used alone to diagnose COVID-19. Results from these tests should also not be used to make decisions about staffing or the ability of an employee to return to work, decisions about the need of available protective equipment (PPE), or the need to discontinue social distancing.

If we want to understand the IFR we need to know who has been infected and that requires the second test. However...there is some controversy in doing that:

The survey results, from Germany, the Netherlands, and several locations in the United States, find that anywhere from 2% to 30% of certain populations have already been infected with the virus. The numbers imply that confirmed COVID-19 cases are an even smaller fraction of the true number of people infected than many had estimated and that the vast majority of infections are mild. But many scientists question the accuracy of the antibody tests and complain that several of the research groups announced their findings in the press rather than in preprints or published papers, where their data could be scrutinized. Critics are also wary because some of the researchers are on record advocating for an early end to lockdowns and other control measures, and claim the new prevalence figures support that call.

Remember that comparison to the flu? From that same article:

A German antibody survey was the first out of the gate several weeks ago. At a press conference on 9 April, virologist Hendrik Streeck from the University of Bonn announced preliminary results from a town of about 12,500 in Heinsberg, a region in Germany that had been hit hard by COVID-19. He told reporters his team had found antibodies to the virus in 14% of the 500 people tested. By comparing that number with the recorded deaths in the town, the study suggested the virus kills only 0.37% of the people infected. (The rate for seasonal influenza is about 0.1%.) The team concluded in a two-page summary that “15% of the population can no longer be infected with SARS-CoV-2, and the process of reaching herd immunity is already underway.” They recommended that politicians start to lift some of the regions’ restrictions.

That article's date was April 21, 2020. Let's look at those numbers in Germany:

If you apply the same logic to the US, that is a 0.37% death rate and 15% of the population already infected, you would expect to see similar results, correct? We started at the same time and the virus should behave similar in terms of spread and asymptomatic. Let's look at the US numbers:

That looks different...well for one thing, in the US our politicians "started to lift some of the regions’ restrictions.:

What's going on?

Deeper into the rabbit hole...deeper still.

Part 4

COVID-19: Down the Rabbit Hole of Death Rate Calculations - Part 2

Note: Written June 24th.

I think firstly we need to get our fatality calculation described and agreed upon. For simplicity sake, let's use the CDC, since it is the CDC's IFR of 0.26% that started this blog post.

The CDC has an educational lesson called:

Principles of Epidemiology in Public Health Practice, Third Edition An Introduction to Applied Epidemiology and Biostatistics

Lesson 3: Measure of Risk, Section 3: Mortality Frequency Measures is where we will start. Let's go down to close to the bottom and look at the Case-Fatality Rate:

Let's next look at the example they give:

If we can agree on this calculation - from the CDC - then the Case Fatality Rate CFR is the number of deaths divided by the number of cases. I will get into the lag time from death to cases but for now, this is a crude way of calculating what the CFR is.

A few days ago I went to get data from the WorldoMeters site. Using that data, number of deaths and number of cases on June 20th, this is how the CFR calculates:

That CFR calculated, using the CDC definition and methodology, is quite a bit higher than 0.26%. So what gives?

What does a "case" and "death" mean as it relates to COVID-19? The Worldometers site defines a "case" as follows:

We will get back to that second to the last sentence in a bit. Right now let's make sure we can agree on what a "case" and "death" is when calculating the Case Fatality Rate (CFR) for COVID-19. Here is what the change they refer to for the CDC:

What that means is that we are getting closer to a true number when the reported cases and deaths also include not just confirmed cases based on testing only, but ones where a reasonable certainty of it being COVID-19 is present. 

This means that my calculation identified previously include both. So why is the CDC's IFR almost 10 fold lower than the CFR I calculate.

Deeper we go into that rabbit hole...

On to Part 3.

COVID-19: Down the Rabbit Hole of Death Rate Calculations - Part 1

Note: This was written on June 24th, Today is July 21. 

I have been away from this blog for a bit. Retired and changed career paths. I started writing again on this blog on June 17th to help me understand why my YouTube feeds we starting to show videos that were learned people arguing against social distancing and lock down, basically stating that the virus is just going to "poof" and be gone.

These video recommendations were showing up at the same time we were seeing this in the US:

Source

I was eight draft posts in when I kept getting bogged down on the mortality rate for COVID-19. Prior to these videos showing up I was getting some pretty mathematical - science based videos recommended. In particular this one got me thinking about the death rate for COVID-19.

Source

If the death rate is "ACTUALLY 20%", then why does the CDC state it is 0.26%?

Source

Now you can see by that USA Today headline that there is some disagreement with that. NPR address this and cites a paper titled "A systematic review and meta-analysis of published research data on COVID-19 infection-fatality rates."

Conclusion Based on a systematic review and meta-analysis of published evidence on COVID-19 until May, 2020, the IFR of the disease across populations is 0.64% (0.50-0.78%). 

The IFR -  infection-fatality rate - this meta analysis calculated is 0.67%. What is an IFR?

IFR is the ratio of deaths divided by the number of actual infections with SARS-CoV-2. (source)

The term infection fatality rate (IFR) also applies to infectious disease outbreaks, and represents the proportion of deaths among all the infected individuals. It is closely related to the CFR, but attempts to additionally account for all asymptomatic and un-diagnosed infections. (source)

That last one brings in another term, Case Fatality Rate (CFR).

The proportion of deaths from a certain disease compared to the total number of people diagnosed with the disease for a certain period of time. (source)

According to that cited Wikipedia article:

The IFR differs from the CFR in that it aims to estimate the fatality rate in all those with infection: the detected disease (cases) and those with an undetected disease (asymptomatic and not tested group).

Not being good at math has always been a downfall for me. I may not be good at it, but I am good enough to do back-of-the-envelope calculations.

When I look at the numbers for the US and elsewhere, the number of reported deaths to the number of reported cases is far greater than 0.26%, far greater than 0.67%, but not as high as 20% either.

So what is it actually, what number should we be looking at when we try to make a determination on how much worry we should have, or for our government officials, what policies and prohibitions should be put in place?

Enter the rabbit hole of deaths from COVID-19

On to Part 2