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