COVID-19 Special Update: Is this a disease of the Rich?

I’ve seen various references recently to COVID-19 as a disease of the rich — a reference to the fact that it is most active in very developed countries. Typically, the foil compared against COVID-19 is Ebola, which has appeared primarily in the most undeveloped countries. But is this true? Does the wealth of these countries have anything to do with the outbreaks? Lets look at some data. In the image above, one could imagine how a media outlet, hungry for a story, might look at the data and surmise that only rich countries are being affected. This diagram is trying to show the state of the world at this moment and as you can tell by a relatively blank Asia, history is not being captured. So, the recency bias is in favor of the rich country narrative.

The Latitude Effect

I have put great energy into measuring what is going on with COVID-19 by latitude, because I’m aware that in general, latitudes have similar temperatures. You can see the state of the latitude effect in the table below. In general, it’s unclear why 40 to 60 degrees latitude has been so heavily impacted by cases and deaths (particularly 40 to 50 north latitude), but one can tell that many of the countries in these latitudes are wealthier countries. This might be one of the drivers of the over-distribution of COVID-19 to rich countries, but can’t explain it all. If it did, we’d see the effect across all longitudes. So there must be more.

Other Factors

Another subject of a handful of my analyses is the correlation of other factors with outbreak severity. The World Bank and the World Health Organization have made available data about numerous factors measured across countries. This includes medical factors (Body Mass Index, Diabetes prevalence, etc.) as well as environmental factors (average temperature, pollution, etc.). I have blended these data with my Instantaneous Rate of Change (IROC) numbers for each country to see if I can correlate these many factors with the severity of the outbreak as measured by the IROC.

Correlated Factors with the Severity of Case Growth

In the table above, you can see on the factor on the left and the correlation with the IROC of confirmed case growth on the right. Let me discuss a few of these.

  1. Female Smoking Rate: This factor has been the most highly correlated factor with IROC of Case Growth ever since day one. Why? All I can figure is that from looking at the world bank data, Male Smoking Rates have little variance across countries whereas Female Smoking Rates do. For instance, Iran has nearly zero rate of Female Smoking whereas in Italy, the numbers of Female Smokers equals the Male numbers. So therefore, if Smoking is correlated with case growth (as this would indicate), high female smoking rates would naturally drive even more cases than would low female smoking rates. Also, in many countries, there may be a non-linear effect on case growth if females who might have been in caregiver roles became sick.
  2. Body Mass Index Standard Deviations (STD): We know from reports that doctors are seeing extreme COVID-19 cases in folks with high BMI’s. The data above confirms this and indicates that high male mean BMI results in higher numbers of COVID-19 cases. I have no idea why the male BMI is more highly correlated with case growth than female mean BMI. However, the more interesting thing is that the Standard Deviation (in general English, the STD is a number that explains how data is spread out from the mean. For BMI, a large STD means that BMI numbers are all over the board, not just clustered around the country’s average) of Female BMI is the second-highest factor correlated with COVID-19 case growth. Perhaps this is a similar discussion to female smoking above. It may point to a likelihood for extremely high or low BMI’s, which might be a health factor that could lead one to contract the COVID-19 virus.
  3. Nurses per 1000 people: The only reason I could figure that this might be correlated with COVID-19 case growth is that first, circular reasoning would dictate that countries that we know are currently affected are wealthier and have more nurses… the correlations are not causations, remember. Second, however, and more palatable, having more nurses could equal more confirmations because nurses can give tests.
  4. Population Density: It seems obvious that population density would be positively correlated with case growth, and it is. But not so much so as the above factors.
  5. Income Group: The first negative correlation is Income Group, and this is artificial by the way my data is represented. In my data, the World Bank’s high income group is represented with a 1 and the Lowest Income group is represented with a five. Therefore, when the income group numbers go down (to 1), the case growth increases. This confirms what we already see, that the wealthier countries are where the case growth is higher.
  6. Temperature: This factor is the average March temperature in each country. This factor is also negatively correlated. As Temperature goes up, the case growth goes down. This is also pretty obvious, but it’s good to see there are numbers to support the obvious.
  7. Other Diseases: Tuberculosis Rates, High Blood Pressure, Diabetes. All of these show negative correlations with case growth. Why? Aren’t these co-Morbidity factors? Well, all I can figure, is that first, things like TB are higher in poorer countries and maybe this is just biased by the wealthier countries that have high case growth. The second thing I can guess is that possibly, these factors lead to deaths for other reasons where they are high and possibly there are fewer people at high risk to contract COVID-19? More to think about.

Correlation with the Severity of Death Growth

Since I have made it clear that I don’t value the quality of Confirmed Case Data, this correlation is more interesting to me. See below.

You’ll notice that some of the factors correlated with Case Growth are also correlated with the Death Rate. Female Smoking and BMI STD are two. This is unsurprising, as there is clearly a relationship between Case Growth and Death Rates. Interesting takeaways are as follows:

  1. While Percent of Population over Age 65 is highly correlated with Case Growth, it is a few points more correlated with deaths. This may indicate that people in this age group are more likely to get the disease but even more likely to die from it. That’s probably no surprise, but the numbers align with the media narratives at least.
  2. Nurses per 1000 people: This factor was highly correlated with case growth but is much less correlated with Death Rate. The fact that it is positively correlated with Death Rate at all might be confirmation that this is just an effect of the fact that nations with more nurses have already had lots of Cases and Deaths. BUT the fact that this factor is a full 14 points lower on the Death Rate correlation strongly indicates that a large population of nurses is shrinking death rates.
  3. Density: Population density is around 11 points lower on the Death Rate correlation table. One reasonable explanation is that high density tends to cause the disease to spread, but high density is also a proxy for the better health services available in dense cities, which tend to keep the death rate down.
  4. Temperature: Temperature is much less negatively correlated with Death Rate than it was Case Rate. Apparently, temperature is a contributor to the spread of the disease, but has less effect on the Death rate.

There are more things that could be discussed (feel free to weigh in on the comments) about these correlations, but this is probably a good start.


Is COVID-19 a disease of the rich? I’m pretty sure I haven’t answered this completely, but I’ll reiterate a couple of points that indicate to me that it is not. First, the latitudes the rich countries are in seem to have had a March Temperature in a range that led to quicker spread of the virus. Second, as is probably obvious, richer countries have more resources to test their population. As I have shared in other articles, countries like Iceland have taken a very scientific approach to testing so they can understand the outbreak better. Third, richer/more open countries are being more honest and consistent with data collection. I have zero confidence in the numbers coming out of China and Russia, because they have something to gain from artificially low number. Fourth, there are factors associated with richer countries that might be increasing case and death rate growth: things like numbers of nurses (only correlated with case growth), higher BMI numbers, aging populations, and maybe overall smoking rates.

And of course, the data is still young. Give this another year and we’ll understand these correlations much better.

4 Replies to “COVID-19 Special Update: Is this a disease of the Rich?”

  1. Hey Tod, another thing that might correlate with affluence is mobility. Poor cannot afford to fly from Wuhan to Milan, for example. I think I heard Newt Gingrich say that there about 100K Chinese living in Northern Italy and that there are (or were) daily flights between Wuhan and Italy. Maybe worth checking out.

    1. Nice! Agree. Mobility has to be part of it. Yes, Milan is the largest outpost of ethnic Chinese (per capita?) outside China I read…

  2. Interesting data, but Correlation is not causation. The variables are not independent.

    Wealthy people are some of the most densely concentrated populations across the world. Look at places like London and Manhattan. “Rich” people are packed in tightly. They also tend to be highly networked and socialize extensively. The highest rate in the Dallas Fort Worth metro area is the zip code 75225. Again, high wealth and highest density. This really looks like a exposure issue, with a health care system unprepared to treat it, and bad data from the biggest players.

    This really looks

    1. Hi Bruce, yes, agree one has to be careful when working with lots of correlated factors. However, correlations, when treated as such, allow us to have good conversations. And there are varying degrees of dependence between the variables. As with machine learning, dependence helps us learn about potential causalities. There’s a lot of buzz about one potential causality right now regarding the BCG vaccine… seems like people vaccinated with BCG have a 10-100x lower COVID-19 incidence. Rich countries stopped getting this vaccine (for TB) years ago. Interesting, but like you say, could be just correlated with some other factors.

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