How the US States and Territories Compare on Covid Death Rates

I haven’t seen this sort of simple analysis done anywhere else, so I tallied the total number of reported deaths, and divided this by the population, and moved the decimal point six places so we get the death rates per million. The table below shows the results, in order from highest to lowest fatalities per million inhabitants.

What is their secret?

Looking at the ECDC figures on the current corona virus, I am struck by one thing: Some countries have tiny numbers of people dead from this disease, and some have enormous death tolls.

A lot of the nations with low COVID-19 mortality totals are not exactly famous for having wonderful medical systems> On the other hand, some of these nations are known for being relatively advanced and prosperous, and have well-equipped social networks.

So, what’s their secret?

I just made a list of all the nations with at least a half-million population that have so far had fewer than a hundred people who have died from COVID-19. After each one I list the number dead through today, June 20, 2020, and their population in millions. From that I derived the number of fatalities per million, or fpm. I have arranged them by continent, and then alphabetically by country name.

In ONLY ONE of these countries is the number of deaths per million population anywhere near what it is in the USA, namely about 354 dead per million to date. (That exception is El Salvador.) Many of the countries I listed have fewer than 1 fatality per million, which I denoted as “<1 fpm”.

In Africa:

Angola, 8 dead, pop 32 Million people, <1 fpm

Botswana, 1 dead, pop 2 M, <1 fpm

Benin, 11 dead, pop 12 M, 1 fpm

Burkina, Faso 53 dead, pop 20 M, 3 fpm

Burundi, 1 dead, pop 12 M, <1 fpm

Cape Verde, 8 dead, pop 0.5 M, 16 fpm

Central African Republic, 19 dead, pop 5 M, 4 fpm

Chad, 74 dead, pop 16 M, 5 fpm

Congo, 27 dead, pop 5 M, 5 fpm

Cote d’Ivoire, 49 dead, pop 26 M, 2 fpm

Djibouti, 45 dead, pop 1 M, 45 fpm

Equatorial Guinea, 32 dead, pop 1.4 M, 23 fpm

Eritrea, 0 dead, pop 3 M, 0 fpm

Eswatini (was Swaziland), 4 dead, pop 1 M, 4 fpm

Ethiopia, 72 dead, pop 112 M ,<1 fpm

Gabon, 34 dead, pop 2 M, 17 fpm

Gambia, 1 dead, pop 2 M, <1 fpm

Ghana, 70 dead, pop 30 M, 2 fpm

Guinea, 27 dead, pop 13 M, 2 fpm

Guinea Bissau, 15 dead, pop 2 M, 8 fpm

Lesotho, 0 dead, pop 2 M, 0 fpm

Liberia, 33 dead, pop 5 M, 7 fpm

Libya, 10 dead, pop 7 M, 1 fpm

Madagascar, 13 dead, pop 30 M, <1 fpm

Malawi, 8 dead, pop 19 M, <1 fpm

Mauretania, 95 dead, pop 5 M, 19 fpm

Mozambique, 4 dead, pop 30 M, <1 fpm

Namibia, 0 dead, pop 2 M, 0 fpm

Niger, 67 dead, pop 23 M, 3 fpm

Rwanda, 2 dead, pop 13 M, <1 fpm

Senegal, 79 dead, pop 16 M, 5 fpm

Sierra Leone, 53 dead, pop 8 M, 7 fpm

Somalia, 88 dead, pop 15 M, 6 fpm

South Sudan, 31 dead, pop 15 M, 2 fpm

Togo, 13 dead, pop 8 M, 2 fpm

Tunisia, 50 dead, 12 M, 4 fpm

Uganda, 0 dead, 44 M, 0 fpm

Tanzania, 21 dead, 58 M, <1 fpm

Western Sahara, 1 dead, pop 0.6 M, 2 fpm

Zambia, 11 dead, pop 17 M, <1 fpm

Zimbabwe, 4 dead, pop 15 M, <1 fpm

In the Americas:

Costa Rica, 12 dead, pop 5 M, 2fpm

Cuba, 85 dead, pop 11 M, 7 fpm

El Salvador, 93 dead, pop 0.6 M, 155 fpm

Guyana, 12 dead, pop 0.8 M, 15 fpm

Haiti, 87 dead, pop 11 M, 7 fpm

Jamaica, 10 dead, pop 3M, 3 fpm

Nicaragua, 64 dead, pop 7 M, 9 fpm

Paraguay, 13 dead, pop 7 M, 2 fpm

Suriname, 8 dead, pop 0.6 M, 13 fpm

Trinidad & Tobago, 8 dead, pop 1 M, 8 fpm

Uruguay, 24 dead, pop 3 M, 8 fpm

Venezuela, 30 dead, pop 29 M, 1 fpm

In Asia:

Bahrain, 57 dead, pop 2 M, 28 fpm

Bhutan, 0 dead, pop 0.8 M, 0 fpm

Cambodia, 0 dead, pop 16 M, 0 fpm

Jordan, 9 dead, pop 10 M, 1 fpm

Kyrgyzstan, 35 dead, pop 6 M, 6 fpm

Laos, 0 dead, pop 7 M, 0 fpm

Lebanon, 32 dead, pop 7 M, 5 fpm

Maldives, 8 dead, pop 0.5 M, 16 fpm

Mongolia, 0 dead, pop 3 M, 0 fpm

Myanmar, 6 dead, pop 54 M, <1 fpm

Nepal, 22 dead, pop 29 M, <1 fpm

Palestine, 5 dead, pop 5 M, 1 fpm

Qatar, 93 dead, pop 3 M, 31 fpm

Singapore, 26 dead, pop 6 M, 5 fpm

Sri Lanka, 11 dead, pop 21 M, <1 fpm

Syria, 7 dead, pop 17 M, <1 fpm

Taiwan, 7 dead, pop 24 M, <1 fpm

Tajikistan, 51 dead, pop 9 M, 6 fpm

Thailand, 58 dead, pop 70 M, <1 fpm

Uzbekistan, 19 dead, pop 33 M, <1 fpm

Vietnam, 0 dead, pop 96 M, 0 FPM

In Europe:

Albania, 42 dead, pop 3 M, 14 fpm

Cyprus, 19 dead, pop 0.9 M, 21 fpm

Estonia, 69 dead, pop 1.3 M, 53 fpm

Georgia, 14 dead, pop 4 M, 4.5 fpm

Kosovo, 33 dead, pop 2 M, 17 fpm

Latvia, 30 dead, pop 2 M, 15 fpm

Lithuania, 76 dead, pop 3 M, 25 fpm

Malta, 9 dead, pop 0.5 M, 18 fpm

Montenegro, 9 dead, pop 0.6 M, 15 fpm

Slovakia, 28 dead, pop 5 M, 6 fpm

Elsewhere:

New Zealand, 22 dead, pop 5 M, 4 fpm

Papua New Guinea, 0 dead, pop 9 M, 0 fpm

Once again, I would very much like the secret of what those countries (apparently) did right, and what the US, Brazil, Mexico, France, Spain, Italy, Belgium and a lot of other countries obviously did wrong.

Ideas?

USA: Highest COVID Death toll in the entire world; One of the highest infection rates per capita; and highest number of cases

Making America Great Again – was it really intended to make the USA have the highest Covid-19 death toll in the entire world, PLUS the highest infection rate? What a record!!

Please look at this table, which I compiled from data I found here and here. I have sorted it by the total number of reported Covid-19 deaths and left off almost all of the nations with less than three thousand cases, except for Taiwan and Vietnam.

If you look, you will see that the US (with 105 thousand deaths) is way ahead of every other country — in fact, it’s about the same as the next three or four nations combined (UK, Italy, Brazil, and France).

The US also has the highest number of reported cases in the entire world, with about 1.8 million; that’s roughly the same amount as the next seven nations combined (Brazil, Russia, UK, Spain, Italy, Germany, and India).

No Herd Immunity

People have been talking about herd immunity and low fatality rates. My calculations tell me that we are a long, long way from herd immunity anywhere, and that the fatality rates are rather high.

To get herd immunity, you need to have 70% to 90% of the population that has antibodies – either from a vaccine or from having contracted the disease and recovered by their own body producing the necessary antibodies. I simply divided the total number of reported cases (which is probably too low in every case, but I have no idea by what factor) by the population of each country. What I find is that not a single nation has reached even 1% of their population having been infected and recovered. The highest such rates are in the small nations of Bahrain, Kuwait, and Luxembourg, which have about 7 people diagnosed as having been positive per THOUSAND, that’s 0.7%. The US has about 0.55% positive.

No herd immunity there.

High Fatality Rates

If we divide the number of coronavirus deaths by the total number of cases, we get rather large percentages. For the world as a whole, it’s about 6%, and for the very worst-off nations like France, Belgium, Italy, the UK, Netherlands, Sweden, Spain, and Mexico, your chances of dying if diagnosed positive [EDIT] are over 10%.*

Scary.

Total Reported Cases Total Reported Deaths Calculated fatality rate Population, millions Infection rate so far
World 6,104,980 370,078 6.06% 7594 0.080%
United States 1,811,016 105,295 5.81% 327 0.554%
United Kingdom 274,762 38,489 14.01% 66 0.416%
Italy 233,019 33,415 14.34% 60 0.388%
Brazil 501,985 28,872 5.75% 209 0.240%
France 151,496 28,771 18.99% 67 0.226%
Spain 239,429 27,127 11.33% 46 0.520%
Mexico 87,512 9,779 11.17% 126 0.069%
Belgium 58,381 9,467 16.22% 11 0.531%
Germany 183,411 8,602 4.69% 83 0.221%
Iran 151,466 7,797 5.15% 82 0.185%
Canada 90,516 7,092 7.84% 37 0.245%
Netherlands 46,442 5,956 12.82% 17 0.273%
India 182,143 5,164 2.84% 10 1.821%
Russia 405,843 4,693 1.16% 144 0.282%
China 83,001 4,634 5.58% 1393 0.006%
Turkey 163,103 4,515 2.77% 82 0.199%
Sweden 37,542 4,395 11.71% 10 0.375%
Peru 155,671 4,371 2.81% 32 0.486%
Ecuador 38,571 3,334 8.64% 17 0.227%
Switzerland 30,862 1,657 5.37% 9 0.343%
Ireland 24,990 1,652 6.61% 5 0.500%
Indonesia 26,473 1,613 6.09% 268 0.010%
Pakistan 70,868 1,519 2.14% 212 0.033%
Chile 94,858 997 1.05% 19 0.499%
Philippines 18,086 957 5.29% 107 0.017%
Egypt 23,449 913 3.89% 98 0.024%
Colombia 28,236 890 3.15% 50 0.056%
Japan 16,804 886 5.27% 127 0.013%
Ukraine 23,672 708 2.99% 46 0.051%
Austria 16,731 668 3.99% 9 0.186%
Algeria 9,394 653 6.95% 42 0.022%
Bangladesh 47,153 650 1.38% 161 0.029%
South Africa 30,967 643 2.08% 58 0.053%
Denmark 11,633 571 4.91% 6 0.194%
Argentina 16,201 528 3.26% 44 0.037%
Hungary 3,876 526 13.57% 10 0.039%
Saudi Arabia 85,261 503 0.59% 34 0.251%
Dominican Republic 16,908 498 2.95% 11 0.154%
Panama 13,018 330 2.53% 4 0.325%
Finland 6,859 320 4.67% 5.5 0.125%
Czech Republic 9,233 319 3.45% 11 0.084%
Bolivia 9,592 310 3.23% 11 0.087%
Moldova 8,251 295 3.58% 3.5 0.236%
Israel 17,024 284 1.67% 9 0.189%
Nigeria 9,855 273 2.77% 196 0.005%
South Korea 11,468 270 2.35% 52 0.022%
Sudan 4,800 262 5.46% 42 0.011%
United Arab Emirates 33,896 262 0.77% 10 0.339%
Afghanistan 15,205 257 1.69% 37 0.041%
Serbia 11,381 242 2.13% 7 0.163%
Norway 8,437 236 2.80% 5 0.169%
Belarus 42,556 235 0.55% 9.5 0.448%
Kuwait 27,043 212 0.78% 4 0.676%
Morocco 7,783 204 2.62% 36 0.022%
Honduras 5,094 201 3.95% 9.6 0.053%
Iraq 6,179 195 3.16% 38 0.016%
Cameroon 5,904 191 3.24% 25 0.024%
Bosnia & Herzegovina 2,510 153 6.10% 3 0.084%
Bulgaria 2,453 140 5.71% 7 0.035%
North Macedonia 2,226 133 5.97% 2 0.111%
Armenia 9,282 131 1.41% 3 0.309%
Malaysia 7,819 115 1.47% 32 0.024%
Luxembourg 4,016 110 2.74% 0.6 0.669%
Croatia 2,246 103 4.59% 4 0.056%
Australia 7,193 103 1.43% 25 0.029%
Guatemala 4,739 102 2.15% 17 0.028%
Cuba 2,025 83 4.10% 11 0.018%
DR Congo 3,046 72 2.36% 84 0.004%
Azerbaijan 5,494 63 1.15% 10 0.055%
Thailand 3,081 57 1.85% 69 0.004%
Tajikistan 3,807 47 1.23% 9 0.042%
Oman 11,437 46 0.40% 5 0.229%
Senegal 3,535 41 1.16% 16 0.022%
Kazakhstan 10,858 40 0.37% 18 0.060%
Ghana 7,881 36 0.46% 30 0.026%
Ivory Coast 2,799 33 1.18% 25 0.011%
Guinea 3,706 23 0.62% 12 0.031%
Singapore 34,884 23 0.07% 5.6 0.623%
Djibouti 3,194 22 0.69% 1 0.319%
Bahrain 10,793 18 0.17% 1.5 0.720%
Uzbekistan 3,554 14 0.39% 33 0.011%
Taiwan 442 7 1.58% 24 0.002%
Vietnam 328 0 0.00% 96 0.000%

* EDIT: The divisor here is the number of people who have been formally and medically diagnosed as positive. The number of people who have actually been exposed to COVID-19 is probably considerably higher than the number of people who have tested positive, since no country is testing every single citizen, and the technicians are not testing people randomly.

By what factor is the reported positive rate in the various nation’s populations too low? I cannot say, and I’m positive it varies a lot from nation to nation and even within any country or state or region.

CDC gives a much lower fatality rate than I do – they estimate it to be under 1%, which would mean that every single reported positive case represents about 10 to 60 people who got the infection and fought it off unknowingly. That’s the only way you can lower a 6% fatality rate to 0.6% or 0.1%. Does that sound reasonable to you? It would be nice if that were true, but I rather doubt it.

There is NO Herd Immunity in the US but we have a High Fatality Rate

covid cases reported each day, USA

Notice from this pink graph that in the USA, technicians are still detecting twenty to 25 THOUSAND new cases of COVID-19 per day. These folks didn’t all get sick; they just all tested positive for antigens and/or antibodies. Some did get sick, some less so, and some more so, and some died.

One of the key questions is, what is the fatality rate? We now have some idea, which we can get by comparing the total number of cases reported so far with the total number of deaths. This yellow graph shows the cumulative ECDC-reported number of cases in the USA. Right now it’s a bit over 1.7 million people – roughly one half of one percent of the population, which is roughly 330 million.

One half of one percent of the population is nothing like herd immunity! You need 70 to 90% or more of the people to have been exposed to reach that level according to JHU.

total covid cases to date, may 30

Now let’s compare that to the total deaths each day and cumulative.

covid deaths per day

As you can see from the white graph above, the US is recording something like 1000 to 1500 deaths from COVID every day. (My guess as to why it’s going down has to do with the fact that the vast majority of the population is engaging in social distancing.)

Total, cumulative deaths can be seen below:

TOTAL COVID DEATHS TO DATE, MAY 30

The above graph shows that at present, a bit over a hundred thousand people have been killed in the United States so far by this virus at this writing. Now let’s compare that total number of deaths, namely 102,836, with the total number of detected cases, which is 1,747,087. Get out your favorite calculator and divide. If you divide the big one (~1.7 million) by the smaller one (~103 thousand), you get roughly 17 — which means that about ONE OUT OF EVERY 17 PEOPLE IN THE USA WHO HAS TESTED POSITIVE, HAS DIED.

Let that sink in.

If you are infected, it looks like you have a one-in-seventeen chance of dying.

And there is neither a vaccine, nor a cure, nor herd immunity, nor any contact tracing to speak of. Testing is still rationed tightly, or else you have to pay a LOT for it. Will that ratio continue to hold in the future? I don’t know, but it’s alarming all the same.

If you divide the little one by the big one, you will get about 0.05886. That means 5.886% chance of dying – nearly 6% fatality rate!

That is one hell of a lot more lethal than the flu.

If we open up again without contact tracing and effective and humane quarantine and/or medical care of those who test positive, I am really afraid of what will happen.

5.886% of the population of the USA is over 19 million people.

I’ve checked about a dozen other countries, and their fatality rates range from about 2% (Taiwan) up to 19% (France).

 

The Pandemic Is Far From Over

While the rate of increase per day in the number of deaths is generally down, the COVID-19 pandemic is far from over. In general, more people are still dying each day in the US from this disease than the day before, as you can see from this data, which is taken from the CDC. The very tall bar on day 27 is when New York City finally added thousands of poor souls who had in fact died from this virus. (Day 27 means April 9, and Day 41 means April 30, which is today.)

Opening up the economy and encouraging everybody to go back to work, play, and school will mean a rebirth of exponential growth in deaths and in diagnosed cases after about 2 weeks, since this disease takes about that long to be noticed in those who have been exposed. And once everybody is back on the streets and in the stores and schools, the disease WILL spread exponentially. Opening wide right now, when we still can’t test or follow those who may be infected, would be a huge mistake.

us covid deaths per day

Only somebody as clueless as our current Grifter-In-Chief and his brainless acolytes could be recommending something so irresponsible, against the advice of every medical expert. Maybe they think that only the poor, the black, and the brown will get this disease. Wrong.

COVID-19 Numbers in the US do not seem to be growing exponentially

Looking at the past month of CDC-reported infections and deaths from the new corona virus, I conclude that there has been some good news: the total number of infections and deaths are no longer following an exponential growth curve.

The numbers are indeed growing, by either a quadratic (that is, x^2) or a quartic (x^4) curve, which is not good, and there is no sign of numbers decreasing.

BUT it looks as though the physical-social distancing and self-quarantining that I see going on around me is actually having an effect.

Yippee!

Here is my evidence: the actual numbers of infected people are in blue, and the best-fit exponential-growth equation is in red. You can see that they do not match well at all. 

total cases US not looking exponential

If they did match, and if this were in fact exponential growth, we would have just about the entire US population infected by the end of just this month of April – over 300 million! That no longer seems likely. Take a look at the next graph instead, which uses the same data, but polynomial growth:

total cases US looking second power

Just by eyeballing this, you can see that the red dots and blue dots match really, really well. When I extend the graph until the end of April, I get a predicted number of ‘only’ 1.5 million infected. Not good, but a whole lot better than the entire US population!

Also, let’s look at total cumulative reported deaths so far. Here are the CDC-reported numbers plotted against a best-fit exponential curve:

deaths do not seem to be exponential

Up until just a few days ago, this graph was conforming pretty well to exponential growth. However, since about April 8, that seems to be no longer the case. If the total numbers of deaths were in fact growing at the same percentage rate each day, which is the definition of exponential growth, then by the end of April we would have 1.5 million DEAD. That’s THIS MONTH. Continued exponential growth would have 1.2 BILLION dead in this country alone by the end of May.

Fortunately, that is of course impossible.

Unfortunately all that means is that the virus would run out of people to infect and kill, and we would get logistic growth (which is the very last graph, at the bottom).

death seem to be 4th power polynomial

This fourth-degree mathematical model seems to me to work much better at describing the numbers of deaths so far, and has a fairly good chance of predicting what may be coming up in the near future. It’s still not a good situation, but it shows to me that the social and physical distancing we are doing is having a positive effect.

But let’s not get complacent: if this model correctly predicts the next month or two, then by the end of April, we would have about 60 thousand dead, and by the end of May we would have 180 thousand dead.

But both of those grim numbers are much, much lower than we would have if we were not doing this self-isolation, and if the numbers continued to grow exponentially.

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FYI, a logistic curve is shown below. Bacteria or fungi growing in a broth will grow exponentially at first, but after a while, they not only run out of fresh broth to eat, but they also start fouling their own environment with their own wastes. WE DO NOT WANT THIS SITUATION TO HAPPEN WITH US, NAMELY, THAT WE ALL GET INFECTED!!!

logistic curve again

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