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?

How do we fix the CV19 testing problem? By re-testing everybody who tested positive!

I guess I’ve re-discovered a form of Bayes’ Theorem  regarding the problem that is posed by the high numbers of false negatives and false positives when testing for the feared coronavirus.  What I found is that it doesn’t really even matter whether our tests are super-accurate or not. The solution is to assume that all those who test negative, really are negative, and then to give a second test to all those who tested positive the first time. Out of this group, a larger fraction will test positive. You can again forget about those who test negative. But re-test again, and if you like, test again. By the end of this process, where each time you are testing fewer people, then you will be over 99% certain that all those who test positive, really have been exposed.

Let me show you why.

Have no fear, what I’m gonna do is just spreadsheets. No fancy math, just percents. And it won’t really matter what the starting assumptions are! The results converge to almost perfect accuracy, if repeated!

To start my explanation, let’s start by assuming that 3% of a population (say of the US) has antibodies to CV19, which means that they have definitely been exposed. How they got exposed is not important for this discussion. Whether they felt anything from their exposure or not is not important in this discussion. Whether they got sick and died or recovered, is not going to be covered here. I will also assume that this test has a 7% false positive rate and a 10% false negative rate, and I’m going to assume that we give tests AT RANDOM to a hundred thousand people (not people who we already think are sick!) I’m also assuming that once you have the antibodies, you keep them for the duration.

This table represents that situation:

math of CV19 testing

If you do the simple arithmetic, using those assumptions, then of the 100,000 people we tested, 3%, or three thousand, actually do have those antibodies, but 97%, or ninety-seven thousand, do not (white boxes, first column with data in it).

Of the 3,000 folks who really do have the antibodies – first line of data – we have a false  negative rate of 10%, so three hundred of these poor folks are given the false good tidings that they have never been exposed (that’s the upper orange box). The other 90% of them, or two thousand seven hundred, are told, correctly, that they have been exposed (that’s the upper green box).

Now of the 97,000 people who really do NOT have any antibodies – the second line of data – we have a false positive rate of 7%, so you multiply 0.07 times 97000 to get six thousand, seven hundred ninety of them who would be told, incorrectly, that they DID test positive for Covid-19 – in the lower orange box. (Remember, positive is bad here, and negative is good.) However, 90,210 would be told, correctly, that they did not have those antibodies. (That’s in the lower green box.)

Now let’s add up the folks who got the positive test results, which is the third data column. We had 2,700 who correctly tested positive and 6,790 who wrongly tested positive. That’s a total of 9,490 people with a positive CV19 antibody test, which means that of that group of people, only 28.5% were correctly so informed!! That’s between a third and a fourth! Unacceptable!

However, if we look at the last column, notice that almost every single person who was told that they were negative, really was negative. (Donno about you, but I think that 99.7% accuracy is pretty darned good!)

However, that 28.5% accuracy among the ‘positives’ (in the left-hand blue box) is really worrisome. What to do?

Simple! Test those folks again! Right away! Let’s do it, and then let’s look at the results:

math of CV19 testing - round 2

Wowser! We took the 9490 people who tested positive and gave them another round of tests, using the exact same equipment and protocols and error rates as the first one. The spreadsheet is set up the same; the only thing I changed is the bottom two numbers in the first data column. I’m not going to go through all the steps, but feel free to check my arithmetic. Actually, check my logic. Excel doesn’t really make arithmetic errors, but if I set up the spreadsheet incorrectly, it will spit out incorrect results.

Notice that our error rate (in blue) is much lower in terms of those who tested positive. In fact, of those who test positive, 83.7% really ARE positive this time around, and of those who test negative, 95.9% really ARE negative.

But 84% isn’t accurate enough for me (it’s either a B or a C in most American schools). So what do we do? Test again – all of the nearly three thousand who tested positive the first time. Ignore the rest.

Let’s do it:

math of CV19 testing - round 3

At this point, we have much higher confidence, 98.5% (in blue), that the people who tested ‘positive’, really are ‘positive’. Unfortunately, at this point, of the people who tested negative, only about 64% of the time is that correct. 243 people who really have the antibodies tested negative. So perhaps one should test that subgroup again.

The beautiful thing about this method is that it doesn’t even require a terribly exact test! But it does require that you do it repeatedly, and quickly.

Let me assure you that the exact level of accuracy, and the exact number of exposed people, doesn’t matter: If you test and re-test, you can find those who are infected with almost 100% accuracy. With that information you can then discover what the best approaches are to solving this pandemic, what the morbidity and mortality rates are, and eventually to stop it completely.

Why we don’t have enough tests to do this quickly and accurately and repeatedly is a question that I will leave to my readers.

Addendum:

Note that I made some starting assumptions. Let us change them and see what happens. Let’s suppose that the correct percentage of people with COVID-19 antibodies is not 3%, but 8%. Or maybe only 1%. Let’s also assume a 7% false positive and a 10% false negative rate. How would these results change? With a spreadsheet, that’s easy. First, let me start with an 8% infection rate and keep testing repeatedly. Here are the final results:

Round Positive accuracy rating Negative accuracy rating
1 52.8% 99.1%
2 93.5% 89.3%
3 99.5% 39.3%

So after 3 rounds, we have 99.5% accuracy.

Let’s start over with a population where only 1% has the antibodies, and the false positive rate is 7% and the false negative rate is 10%.

Round Positive accuracy rating Negative accuracy rating
1 11.5% 99.9%
2 62.6% 98.6%
3 95.6% 84.7%
4 99.6% 30.0%

This time, it took four rounds, but we still got to over 99.6% accuracy at distinguishing those who really had been exposed to this virus. Yes, towards the end our false negative rate rises, but I submit that doesn’t matter that much.

So Parson Tommy Bayes was right.

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