A CSH overview on the COVID-19 pandemic

UPDATED ON JANUARY 6th 2021

The COVID-19 epidemic:

The year 2020 will be forever associated to the COVID-19 epidemic, and will for this reason become a landmark in the history of the human kind. Surprisingly enough, it is not so much the health consequences of the epidemic itself – however severe these may seem – that will make 2020 famous in the history. COVID-19 is estimated indeed to have killed a little less than 1.9 million people worldwide since its beginning a year ago. While this is certainly not the final health assessment of the epidemic, there is now some hope that the vaccination process that has started in the beginning of December and that is gaining momentum will reduce the spread of the disease and bring many regions of the world to a herd immunity, at least for a while. Indeed, the current graph seems to be suggesting that the world is entering in a peak of the daily number of COVID-19 death.  Yet this peak, which is associated to the third wave of the epidemic, has seen a much larger number of daily death than any of the two preceding ones.

Figure 1: Dynamic of the daily COVID-19 related deaths

While the death toll of COVID-19 may thus seem to be impressive, it is quite 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). 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 low 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 consequences 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 facing. According to the World Health Organization, in 2017, 1.3 million people worldwide died from road accidents, 15.2 million people died from cardiovascular diseases, and 9.6 million 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 is far from clear, their dire economic and social consequences are amazing, and unprecedented in the last century. The following table, showing some recent 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, December 5th 2020)
Australia -4.1
South Africa -7.7
Brazil -5.2
Canada -5.8
China 1.8
France -9.5
Germany -5.8
India -9.8
Italy -9.1
Japan -6.4
Pakistan -2.8
Russia -4.4
South Korea -1.2
UK -11.3
US -3.8

If we except China, all countries on the table are therefore expected to experience a severe reduction in their national income. India for example is expected to see its GDP drop by almost 10%, something that has never happened in the country since its independence. France, whose GDP is also expected to fall by more than 9% this year, will also witness the most severe recession of its history. Behind these (spectacularly) negative numbers lie the dire realities of 150 million people worldwide who have been plunged into the hell of extreme poverty (defined by an income of less than 1.9 US dollars 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.  

The 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 describing the current state (as of January 4th 2021) 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, Belgium, more than 1700 people per million have died from COVID-19. Italy and Peru are two other countries that have paid a heavy death toll of above a thousand deaths per million people to COVID-19. The Peruvian death toll is all the more impressive as it is observed in a country that has implemented one of the toughest lockdown in the world, and whose inhabited by a relatively young population (half of the Peruvians are below 28). Latin American countries have all paid a surprisingly severe death 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-19. If one excepts Hong Kong, Myanmar, Japan and the countries of the Indian subcontinent, the overall lethality of COVID-19 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 one of the Asian countries who has paid the highest death toll to COVID-19 (a little more than 100 deaths per million) so far, even though this death toll is 10 times lower than what is find in the most affected European and Latin American countries.

Country Number of cases Number of deaths Population Median age Number of tests / million people Number of deaths / million people
Belgium 650011 19701 11539328 41,4 600734,8088 1707,291794
Italy 2155446 75332 60550075 45,5 439282,8085 1244,127278
Peru 1019475 37830 32510453 28 43892,89808 1163,625742
UK 2654779 75024 67530172 40,5 770888,7073 1110,970071
US 21113528 360078 329064917 38,1 727092,3658 1094,246094
Spain 1936718 50837 46736776 42,7 467725,8012 1087,730142
France 265728 65037 65129728 41,4 532667,6015 998,5762569
Mexico 1448755 127213 127575529 28,3 25247,69464 997,1583187
Argentina 1640718 43482 44780677 31,7 87664,23965 970,9991656
Brazil 7733746 196018 211049527 32,6 30426,22787 928,7772533
Colombia 1675820 43965 50339443 30 120528,9657 873,3708079
Sweden 437379 8727 10036379 41,2 388300,5016 869,5367124
Poland 1322947 29161 37887768 40,7 185955,7417 769,6679308
Iran 1249507 55650 82913906 30,3 93801,97334 671,1781254
South Africa 1100748 29577 58558270 27,1 115147,7494 505,0866428
Canada 607476 15926 37411047 42,2 375328,9236 425,7031352
Germany 1787728 35325 83517045 47,1 416700,4831 422,9675511
Russia 3260138 58988 145872256 39,6 629674,2473 404,3812142
Turkey 2255607 21685 83429615 30,9 299064,415 259,919694
Saoudi Arabia 363155 6256 34268528 27,5 324930,1225 182,558177
India 10348930 149838 1366417754 28,1 127998,7635 109,657533
Maldives 13867 48 530953 28,2 607827,8115 90,40348204
Philippines 478761 9263 108116615 23,5 59468,75973 85,67600826
Indonesia 772103 22911 270625568 30,2 18447,27029 84,65940661
Egypt 142187 7805 100388073 23,9 N.A 77,74827992
Nepal 262262 1885 28608710 24,1 68108,69836 65,88902471
Myanmar 126935 2744 54045420 28,2 34556,65623 50,77210983
Pakistan 488529 10350 216565318 23,8 31490,26383 47,79158591
Bangladesh 516019 7626 163046161 26,7 19976,78437 46,77203041
Australia 28504 909 25203198 38,7 453463,4851 36,06685152
Japan 243847 3599 126860301 47,3 35793,90845 28,36978922
Hong Kong 9018 151 7436154 44,4 746187,4781 20,30619592
South Korea 64264 981 51225308 41,8 81099,87352 19,15069012
Malaysia 120818 501 31949777 28,5 106867,0996 15,68086062
Sri Lanka 44964 213 21323733 32,8 60428,53754 9,988870148
New Zealand 2181 25 4783063 37,9 295714,6916 5,226776231
Singapore 58721 29 5804337 34,6 934878,695 4,996264001
China 87150 4634 1433783686 37,4 N.A 3,23200776
Thailand 8439 65 69037513 37,7 21833,43424 0,941517114
Vietnam 1497 35 96462106 30,5 N.A 0,362836781
Taiwan 815 7 23773876 40,7 5367,572372 0,294440839
Bhutan 433 0 763092 27,6 N.A 0
Cambodia 356 0 16486542 25,3 N.A 0
Laos 41 0 7169455 23 N.A 0

A common explanation put forth to explain the diversity of the countries in terms of their COVID-19 death toll is their age structure. COVID tend to hit 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. Figure 2 below shows that this impression is corroborated by the existing data, even though the relation between the countries’ age structure – measured by median age – and the lethality of COVID is far from perfect. For one thing, Latin America countries have a very young population structure and yet they pay an impressive death toll to COVID-19. At the other extreme, countries like Taiwan and Japan have a rather aged population structure and do not suffer much from the direst consequences of COVID mortality.

Figure 2: Number of COVID related deaths/million against countries’ median age

Another frequently heard explanation for the amazingly different performances of the countries in avoiding COVID-19 mortality is their differing testing capacities. Yet, 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 3 below, countries who pay the largest dead toll to COVID tend to be those who test the most, even though the relationship is extremely weak. The reason for this lies in the simultaneous determination of COVID epidemic and the testing policy. On the one hand, a wide and generous testing policy may reduce the death toll from COVID by easing the tracking of people, and preventing therefore the spreading of the disease.  This direction of causality would suggest a negative impact of testing capacity on COVID lethality. On the other hand, when COVID is very active and people get infected and develop symptoms, they want to be tested. This demand-induced testing goes in the direction of 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 3. 

Figure 3: COVID death toll by countries’ testing capacity;
Source: Our World Data (Oxford University)
https://ourworldindata.org/coronavirus-testing

Are lockdown policies effective? a cross-comparison of France and India

A widely discussed issue pertaining to the management of COVID-19 is the impact of lockdown policies implemented in various countries to limit the spread of the virus. France for instance is currently finishing a second lockdown, that it has alleviated a bit on November 28th and, then on December 15th. Moreover, the French government does not rule out the possibility of imposing a third lockdown if the number of daily contamination does not go down more significantly.  India is still in the process of eliminating the last restrictions of the very severe lockdown it has implemented on March 22 2020.

How successful have been these lockdowns in reducing COVID mortality? Answering a question like this is not easy, because we don’t know the counterfactual situation that would have prevailed in absence of those lockdowns. However, the historical juxtaposition of the timing at which the lockdown policies have been implemented and the daily number of COVID death provides some useful insights on those matters. Figure 4 below shows the daily number of COVID deaths observed in India since March 1st 2020, along with the various steps of the lockdown policies implemented by the Modi government, starting from the first stringent lockdown initiated on March 22nd to the current phase 5 and 6 of the “un-lockdown”. 

Figure4: number of daily COVID deaths in India during the different lockdown phases; Source: Worldometers (https://www.worldometers.info/coronavirus/)

This figure is suggestive of a failure of the Indian lockdown episodes to affect the lethality of the COVID. Any policy restricting the contact between individuals and the circulation of the virus at time t will see its effect on COVID lethality some 20 to 25 days later. Indeed, it takes between 5 to 7 days to be contaminated by the virus and it takes another 10 days after the beginning of the symptoms for the disease to evolve into the dangerous and potentially lethal phase. Hence, this graph suggests that the first phase of the lockdown has slowed down a bit the daily lethality of the COVID (one can see a little drop in the number of death roughly in the first two weeks of April). However, the progressive alleviation of the lockdown initiated on April 14th and after seem to have generated a continuous upsurge of the daily deaths that has peaked in mid-September without any clear connection to the severity of the lockdown (which has regularly decreased over the period).

Hence, except perhaps at the very beginning of the epidemic, lockdown policies in India seem to have had no visible effect on the dynamic of the daily number of daily deaths. To the contrary, it looks as if the reduction in the severity of the lockdown initiated on August 1st with the Unlock 3.0 has contributed to decrease the rate of growth of those daily deaths, thus leading to the mid-September peak. Again, one must be careful in over-interpreting this juxtaposition, because we do not know what would have been the dynamic of daily Covid deaths in India had the lockdown policies be different. 

The French situation, described in the following figure, is more suggestive of a relative success of the first lockdown in reducing the number of daily COVID death.  Indeed, 21st day or so after the implementation of the first lockdown on March 16th, the number of daily COVID death reached a peak (a bit before April 15th) and start to decrease. Following that decrease, the French government has announced an alleviation of the lockdown whose effect have all been eliminated on June 11th.

Figure4: number of daily Covid deaths in France during the different lockdown phases; Source: Worldometers (https://www.worldometers.info/coronavirus/)

However, the second lockdown implemented on October 28th to fight the second wave of the COVID does not seem to have had much effect on the number of daily deaths. Indeed, the peak in the number of those deaths observed around November 15th has taken place much too early to be explainable by the lockdown. It seems therefore that the dynamics of the second wave of COVID in France has been largely independent from the implementation of the second lockdown.

Nicolas Gravel, Director of the CSH.

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