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)
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|
|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.
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.
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.
Regarding the CSH scientific production linked to the topic:
- Olivier Telle‘s contribution to the CNRS podcast series on COVID-19 (in French): http://www.csh-delhi.com/news/olivier-telle-part-of-the-cnrs-podcast-series-on-covid-19-in-french/
- Olivier Telle and Samuel Benkimoun (co-written with Eric Denis from CNRS), article on The Conversation on mapping the lockdown effects in India: https://theconversation.com/mapping-the-lockdown-effects-in-india-how-geographers-can-contribute-to-tackle-covid-19-diffusion-136323
- Same approach was applied to France: https://theconversation.com/evolution-des-mobilites-et-diffusion-du-covid-19-en-france-ce-que-les-donnees-facebook-devoilent-137846
- Some CSH researchers want to understand from a more qualitative perspective the impact of the Indian lockdown on the underprivileged segments of the Indian population, and more generally on the Indian economy. A brief description of the impact of the Indian lockdown on the migrant workers and Indian agriculture written by Bruno Dorin can be found here: https://www.cirad.fr/en/news/all-news-items/articles/2020/science/covid-19-and-food-security-india-and-its-jobs-crisis
- An interview of Bina Agarwal, an associate member of the CSH, suggesting alternative ways to fight the epidemic can be listened to here: https://www.facebook.com/brutindia/videos/836890173484449/
- Bina Agarwal in The Indian Express about labour during the lockdown period: https://indianexpress.com/article/explained/expert-explains-working-with-lockdown-create-green-worker-pools-not-green-zones-6376360/
- A crossed study of Kerala and Uttar Pradesh response to the COVID threat, co-authored by CSH member Anmol Seghal: https://www.counterview.net/2020/04/uttar-pradesh-and-kerala-preparedness.html
- An excellent description of the social impact of the Indian lockdown, co-authored by the associate member of the CSH Marine Al-Dahdah and former CSHer Mathieu Ferry, can be found here: https://booksandideas.net/The-Covid-19-Crisis-in-India.html
- Marine Al-Dahdah did also write on the tracing apps used to monitor Covid-19 progression: https://booksandideas.net/Tracing-Apps-to-Fight-Covid-19.html
- Here is a recent political analysis of the Indian national lockdown by CSH member Jean-Thomas Martelli, and Christophe Jaffrelot: https://indianexpress.com/article/opinion/columns/india-covid-19-coronavirus-lockdown-narendra-modi-6383721/
- A paper co-written by CSH member Rémi de Bercegol and colleagues from the IFP, about the marginalized populations of Indian cities during the lockdown: https://journals.openedition.org/echogeo/19357
- Another excellent and detailed analysis can be found here: https://cepr.org/sites/default/files/policy_insights/PolicyInsight102.pdf