COVID-19 Update: Expanded Correlation Study

I have added some factors and am exploring the correlation between these factors and overall numbers of cases and deaths (previously, I have been attempting to correlate factors with the severity of rates of cases and deaths. There is enough data now to try to tease out factors that share some dependence with the numbers of cases and deaths.

Correlations between various factors and total number of Cases per 1000 persons in a country

Things I find Interesting – Case number correlation:

  • Average rates of Tuberculosis have the strongest negative correlation with the total number of COVID-19 cases in a country. There may be many reasons for this, but it is an interesting datapoint. Perhaps more so because of the studies into the potential that a TB vaccine might be providing some kind of protection in the regions that still get it (which may well be regions that still have a high TB rate).
  • Countries with large numbers of deaths due to Zinc and Iron deficiency have experienced less COVID-19 cases. This might be simply that these deaths removed some potentially susceptible people from the pool of potential cases. Factors that might show this same effect are HIV percentage and even TB percentage (though the latter are more strongly negatively correlated with cases).
  • A country with a high Female Smoking Rate is more likely to have larger numbers of COVID-19 cases. This has been pretty consistent. Mean BMI number is also highly correlated with cases. My suspicion is that these individuals present with symptoms more quickly than others and therefore are formally counted as a case whereas others might not be counted. This concept also applies to people over 65. The same theory applies to the number of nurses per 1000. In countries with low numbers of nurses, perhaps many people never get diagnosed, and therefore recorded.
  • Population density is less correlated than the above factors, but is still positively correlated with the number of cases.

Deaths Per 1000 person – Correlations

Things I find interesting – Death number correlation:

Here we find the co-morbitities for COVID-19 as well as factors that may have a dependency with the number of deaths per 1000.

  • Note that having a high number of citizens over 65 is the highest factor that is positively correlated with deaths due to COVID-19. This is not surprising, but confirms what we are hearing.
  • Female Smoking rate is equally correlated with cases and deaths in a country. This would make me suppose that in this case, countries with a high female smoking rate are seeing more females die due to COVID-19 than other countries.
  • Countries with a high number of deaths due to eating red meat (how is this measured??) also have a higher number of COVID-19 deaths, but I suspect there’s a lot of similarity between this measure and Mean BMI.
  • Countries with a high number of deaths due to lack of calcium also see a high number of COVID-19 deaths. Not sure if this is just correlated (i.e., how drowning deaths are correlated with ice cream sales every year), but it’s one of the few factors related to nutrition that is postitively correlated. Other nutrition-related factors (deficiencies in Iron, Vitamin-A, Zinc) are negatively correlated and the rest of the nutrition factors here have no correlation at all.
  • Temperature is very negatively correlated with deaths (as well as with cases). My intuition is that this is causal, but I can’t support that exactly.
  • Finally, the Income Group and the Growth Rate are both highly negatively correlated with deaths. I see these as measuring similar things. Countries with a high Growth Rate are also generally growing in weath and moving up Income charts. This weath and what it brings the society (ability to shelter in place? Better health care?) makes them less susceptible to COVID-19 deaths (and cases).

Leave a Reply

Your email address will not be published. Required fields are marked *