A CSH overview on the COVID-19 pandemic

UPDATED ON OCTOBER 20th 2020

The COVID-19 epidemic:

The current COVID epidemic will undoubtedly become a significant landmark in the history of the human kind. The reason for this does not lie so much in the health consequences of the epidemic itself, however impressive these may seem. COVID-19 is estimated indeed to have killed a little more than 1 million people worldwide since its beginning 10 months ago. Of course, this if far from being a final assessment as the epidemic is still spreading. Yet, for the moment, this dead toll is somewhat modest when compared to those of past epidemics, be they relatively recent, or more ancient. The Hong Kong flu epidemic of 1968-1969 for instance is estimated to have killed from 1 to 4 million people worldwide, for a world population of 3 billion (as compared to the 7 billion people who live on Earth nowadays). Hence, the Hong Kong flu epidemic has been at least twice as deadly as COVID-19 so far. The 1957-1958 H2N2 pandemic is also estimated to have killed around 1.1 million people worldwide, for a world population of 2.5 billion. And these figures are obviously very modest when compared to the 20-50 millions of death caused by the Spanish Flu in 1918-1923 (over a population of 1.5 billions), or to the third of the European population that was apparently killed by the Black death of 1346-1353.  The health consequence of COVID-19 are also modest when compared to those of other major causes of death – many of them avoidable – that the human kind is currently facing. According to the World Health Organization, in 2017, 1.3 million people worldwide died from road accidents, 15.2 millions died from cardiovascular diseases, and 9.6 millions died from cancers.

The main reason why COVID-19 will become an episode of significant importance in the human history is the astonishing fear it has caused, and which is responsible for the adoption of policy measures of unprecedented stringency to prevent its spreading. While the actual impact of those measures on mitigating the health consequences of COVID-19 are far from clear, their dire economic and social consequences are amazing, and unprecedented in the last century. The following table, showing some estimations of the economic growth rate for 2020 in some of the largest economies, is amazingly clear from that perspective.

Country name Estimated GDP growth rate 2020 (%)
(source: The Economist, October 4th 2020)
Australia -4.5
South Africa -8.0
Brazil -5.2
Canada -5.8
China 1.7
France -10.2
Germany -5.9
India -8.5
Italy -10.4
Japan -6.4
Pakistan -2.8
Russia -5.7
South Korea -1.8
UK -9.5
US -5.3

If we except China, all countries of the table are therefore expected to experience severe reduction in their national income. India is expected to see its GDP drop by more than 8%, something that has never happened in the country since its independence. France, whose GDP is expected to fall by more 10% this year, will also witness the most severe recession of its history. Behind these (spectacularly) negative numbers lie the dire realities of 150 millions of people worldwide who have been plunged into the hell of extreme poverty (defined by an income of less than 1.9 US dollar per day in purchasing power parity) according to the most recent estimate of the World Bank. It is the first time in the last 20 years that the world is experiencing an increase in the rate of extreme poverty.  India, which concentrates one third of the world’s population of extremely poor people is obviously heavily concerned by this descent into hell.

COVID-19 is hurting us. The policies that we have put into place to prevent its adverse health consequences are hurting us even more. This is what makes the COVID-19 epidemic so exceptional.

 The next table provides numbers that describing the current state (as of October 2020)  of the COVID epidemic in East Asian countries as well as in a few others that are “salient” in terms of their population size, severity of the COVID, or their geographical localization. Countries in this table are ranked decreasingly in terms of the lethality rate of the COVID-19. As can be seen, countries are extremely diverse on this matter. In the world’s most severely affected country so far, Peru, more than a thousand people per million have died from COVID-19. This number is quite impressive, especially considering the severity of the lockdown implemented in Peru and the relatively young age of its population (half of which being below 28). Latin American countries are all on a par from that point of view. All of them have paid an extremely severe dead toll to COVID-19, despite the fact of having a rather young population. At the other extreme, one finds East Asian countries such as Bhutan, Cambodia, Laos, Sri Lanka, Taiwan, Thailand and Vietnam who have seen less than 1 person out of a million dying from COVID. If one excepts Hong Kong, Myanmar, Japan and the countries of the Indian subcontinent, the overall lethality of COVID in East Asia is everywhere below 10 deaths per million, which is extremely low (in average a hundred time lower than what is observed in Latin American countries). India is certainly the East Asian country that has paid the highest dead toll to COVID-19 (84 deaths per million) so far, even though this dead toll, is 8 to 10 times lower than what is find in the most affected European and Latin American countries.    

Country number of cases number of deaths Country population Median age number of tests per million people number of deaths per million people
Peru 870 876 33 820 32 510 453 28 19 394.41 1 040.28
Belgium 230 480 10 443 11 539 328 41.4 355 886.06 904.99
Brazil 5 251 127 154 226 211 049 527 32.6 30 426.23 730.76
Spain 1 015 795 33 992 46 736 776 42.7 248 974.38 727.31
US 8 456 653 225 222 329 064 917 38.1 404 809.54 684.43
Mexico 854 926 86 338 127 575 529 28.3 14 527.36 676.76
UK 741 212 43 726 67 530 172 40.5 366 606.09 647.50
Italy 423 578 36 616 60 550 075 45.5 135 420.87 604.72
Argentina 1 002 662 26 716 44 780 677 31.7 45 490.76 596.60
Sweden 103 200 5 918 10 036 379 41.2 124 412.70 589.65
Colombia 965 883 29 102 50 339 443 30 72 729.05 578.12
France 910 277 33 623 65 129 728 41.4 242 219.19 516.25
Iran 534 631 30 712 82 913 906 30.3 54 761.08 370.41
South Africa 705 254 18 492 58 558 270 27.1 77 691.47 315.79
Canada 201 437 9 778 37 411 047 42.2 232 516.99 261.37
Russia 1 415 316 24 366 145 872 256 39.6 372 244.93 167.04
Saoudi Arabia 342 583 5 201 34 268 528 27.5 214 603.21 151.77
Germany 373 731 9 899 83 517 045 47.1 230 809.26 118.53
Turkey 349 519 9 371 83 429 615 30.9 148 727.91 112.32
Poland 183 248 3 614 37 887 768 40.7 100 683.26 95.39
India 7 597 063 115 236 1 366 417 754 28.1 68 957.09 84.33
Maldives 11 232 37 530 953 28.2 337 451.71 69.69
Philippines 359 169 6 675 108 116 615 23.5 38 021.45 61.74
Egypt 105 547 6 130 100 388 073 23.9 N.A 61.06
Indonesia 365 240 12 617 270 625 568 30.2 9 435.62 46.62
Australia 27 405 905 25 203 198 38.7 328 372.45 35.91
Bangladesh 390 206 5 681 163 046 161 26.7 13 287.84 34.84
Pakistan 323 452 6 659 216 565 318 23.8 18 937.08 30.75
Nepal 136 036 757 28 608 710 24.1 45 472.79 26.46
Myanmar 37 205 914 54 045 420 28.2 5 879.59 16.91
Hong Kong 5 257 105 7 436 154 44.4 173 932.52 14.12
Japan 93 127 1 674 126 860 301 47.3 22 021.24 13.20
South Korea 25 333 447 51 225 308 41.8 48 071.22 8.73
Malaysia 21 363 190 31 949 777 28.5 59 025.29 5.95
New Zealand 1 887 25 4 783 063 37.9 215 737.91 5.23
Singapore 57 915 28 5 804 337 34.6 566 867.67 4.82
China 85 704 4 634 1 433 783 686 37.4 N.A 3.23
Thailand 3 700 59 69 037 513 37.7 15 794.02 0.85
Sri Lanka 5 625 13 21 323 733 32.8 18 339.19 0.61
Vietnam 1 140 35 96 462 106 30.5 N.A 0.36
Taiwan 540 7 23 773 876 40.7 4 166.38 0.29
Bhutan 299 0 763 092 27.6 N.A 0.00
Cambodia 280 0 16 486 542 25.3 N.A 0.00
Laos 23 0 7 169 455 23 N.A 0.00

A common explanation put forth to explain the diversity of the countries in terms of their COVID death toll is their age structure. COVID typically hit very severely old people, and it was recently recalled by the French president Emmanuel Macron that 90% of the French people who die from COVID are above 65. Hence, one could expect countries with a young population to pay a lower dead toll to COVID than countries with an older age structure. Yet the Figure 1 below shows that this impression is not really corroborated by the existing data. For one thing, Latin America countries have a very young population structure and yet they pay an impressive dead toll to COVID. At the other extreme, countries like Taiwan and Japan have a rather aged population structure and do not suffer much from COVID mortality. All in all, the correlation between median age and mortality due to COVID, while positive, is extremely low.

Figure 1: Number of COVID related deaths/million against countries’ median age

Another common explanation invoked to explain the amazingly different performances of the countries in avoiding COVID-19 mortality is their differing testing capacities. Yet, here again, there does not seem to be a significant correlation between a country’s testing capacity – measured by the number of tests performed per million people – and its performance in avoiding COVID-19 test. As shown on Figure 2 below, while countries who pay the largest dead toll to COVID tend to be those who test the least, the negative relationship is rather weak. One reason for this may lie in the fact that the COVID epidemic and the testing policy are simultaneously determined. On the one hand, an wide testing policy may reduce the death toll from COVID by easing the tracking of people, and preventing therefore the spreading of the disease. On the other hand, when COVID is very active and people get infected and develop symptoms, they want to be tested. This induces a positive relationship between the intensity of COVID and the number of tests done. The balance between the two effects is what is captured on Figure 2.

Figure 2: COVID death toll by countries testing capacity

But all in all, the main causal explanation for the cross-country differences in COVID mortality remains far from clear.

Nicolas Gravel, Director of the CSH.

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