COVID19 - a quick look at the numbers as of April 27, 2020

It is day 40 in California as the lockdown was declared on March 19.

Where are we now?

Here is a screen capture of the death per 1 million over at Real Clear Politics.

The first screen grab is a partial list of nations. Italy was hit hard very early and then Spain followed. Since Belgium is a rather small country it hasn't made the news, but relative to population, it has been hit very hard. The USA number though terrible indicate it could have been much worse. There undoubtedly will be many studies as to why some countries got struck so severely while others fared better. From media reports, the sense is that Italy was the "perfect storm" of the high density of the cities in northern Italy, the age of the population, and the slow initial response. Whether this set of circumstances will account for some of the other hard hit countries remains to be seen.

Meanwhile, in the USA, the states in the northeast had death rates comparable to Italy/Spain/Belgium with New York exceeding the worst of Europe. There will be many studies and second guessing as to what resulted in the calamitous march of COVID19 in the northeast USA in particular New York and New York City. Most pundits have cited the high density of New York City and the large numbers of international passenger travel.

The fatality rate of COVID19 remains a moving target. The number often cited for flu is 0.1% of people who get the flu die from the flu. Thus, in the USA, a nation of about 330 million, if 30 million catch the flu during a typical season, 30,000 may die of it. If you look at the CDC estimates, from 2010-2017, the range was 12,000 to 51,000. Flu viruses are constantly mutating and some years it is more dangerous than others. Additionally, in some years the mixture of strains put into the flu vaccine design is more effective and sometimes less so.

But what is the fatality rate for COVID19?

We don't know.

According to John Hopkins COVID19 dashboard as of April 27, there were 3 million cases worldwide with about 211 thousand deaths. That is a 7% fatality rate. However, it is generally acknowledged that testing is not robust enough at this time to really get a fix on the fatality rate. What happens is that the tests are generally given to people who have some symptoms or are at risk (medical personnel). Thus, the figure is very likely to be overstated.

Also, likely to lower the fatality rate is the hypothesis that a certain percentage (currently unknown) of the population got COVID19 but they either had no symptoms or the symptoms were mild such that they never got tested. This could explain how the disease spread so rapidly as people unwittingly passed it onto others.

As a government official setting public health policy, the fatality rate estimate - really a guess back in March - drove the decision making. If you need to decide for Los Angeles County, a region with 10 million people, what do you do?

If you think 10% of the population is going to catch COVID19; then, 1 million people could get sick. If you think the fatality rate is 1% (10x worse than the flu), then 10,000 people will die. If you are working with numbers in this range, you are going to lock everything down and hope for the best.

But if your public health team thinks that only 4% will get COVID19 as this very small study shows (1). Thus, 400,000 gets COVID19. And what if the fatality rate in your projection is only 0.2%, worse than the flu but not astronomical? This scenario yields 800 dead. If you think these are the "facts" on the ground back in March, you probably don't go for a total lock down (2)?

As of this writing, the LA County reports 942 deaths attributed to COVID19. LA County death per day is declining so the number will go up but not dramatically so. Thus, perhaps COVID19 infection rate might be a bit higher than 4% and the fatality rate might be a bit higher than 0.2% yielding a situation that is akin to a bad flu. But the government officials didn't know this back in March when the orders to lock down were issued. How much worse it would have been if there was no lock down is unknowable.

But what about New York City, I don't think anyone imaged it was going to get that bad!

New York City is currently estimating death at 17,000. New York City population is about 8.4 million. What happened there? How do you get that many fatalities? The population infection rate must be much higher in NYC? Perhaps, for some reason the fatality rate is higher?

Some scenarios:
40% infection + 0.4% fatality = 13,440 deaths
30% infection + 0.6% fatality = 15,120 deaths
50% infection + 0.4% fatality = 16,800 deaths
60% infection + 0.3% fatality = 15,120 deaths

It will take many more months of investigating to piece together what happened in New York City and places where the COVID19 swept in with such deadly force.

Given the limited information at hand, if I had to guess the fatality rate of SARS-Cov2/COVID19 will turn out to be higher than the typical flu, perhaps around 0.3 to 0.6% (3), and what will turn out to be the driver of fatalities is a high infection rate due to unique features of the locality in question.

UPDATES:
Decided to add a few things after thinking more about what I posted last evening.
(1) Some scientists have called into question the antibody kit used in the study as not fully validated and await better characterized kits and larger numbers in the test population. Will better test kits reveal higher or low percent infection in the general population?
(2) The "what if" we don't know is what the infection rate would have been without the total lock down? In Los Angeles County, if the number rises to 8% infection with 0.2% fatality, then you have 1600 deaths. If you are on the board of supervisors and that is what your public health people tell you, what do you do? It all comes down to the "best guess" at the time about infection rates and fatality rates and how many people is it "tolerable" to lose to the virus. Scale the infection percent and fatality percent to higher numbers and the impetus to lock down becomes very strong.
(3) One could also suspect specific vulnerable populations will be looked at. The fatality rate might turn out to be quite "low" for the population as a whole. But what is the fatality rate when it hits a nursing home? Or an apartment building with a higher percentage of senior citizens? And there will definitely be studies on health disparities in poorer communities that may have a much higher fatality rate. Populations aren't homogeneous. There will be more to the data than just simple density of people in a community.