A CSH overview on the COVID-19 pandemic
UPDATED ON April 6th 2021
The COVID-19 epidemic:
The years 2020 and 2021 will be forever associated to the COVID-19 epidemic. They will for this reason become a landmark in the history of the humankind. Surprisingly enough, it is not so much the health consequences of the epidemic itself – however severe these may seem – that will make 2020 and 2021 famous in the history. COVID-19 is estimated indeed to have killed a little less than 2.9 million people worldwide since its beginning 16 months ago. While this is certainly not the final assessment of the epidemic, there is now some hope that the vaccination process that is gaining momentum will reduce the severity of the disease and bring many regions of the world to herd immunity, at least for a while. Yet, as the graph below clearly indicates, we are not quite there yet, and the humankind is currently hitting a fourth wave of the epidemic.
The not less spectacular current resurging of the Indian epidemic, depicted on the figure below, suggests that this global fourth wave has not yet reached its peak.
While the death toll of COVID-19 may seem to be impressive, it remains 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 billion), 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 humankind 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 clear from that perspective.
|Country name|| Estimated GDP growth rate 2020 (%), |
(source: the Economist, January 31st 2021)
If we except China, all countries of the table are therefore estimated to have experienced severe reductions in their national income in 2020. The drops of GDP by 8% and 9% observed in India and France respectively are unprecedented in the history of these two countries. 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 has hurt us. The policies that we have put into place to prevent its adverse health consequences have hurt us even more. This is what makes the COVID-19 epidemic so exceptional.
The next table provides numbers that describe the current state (as of April 5th, 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, Czechia, more than 2500 people per million have died from COVID-19. Italy, UK and the US are other countries that have paid a heavy death toll of above 1500 deaths per million people to COVID-19. Latin American countries have all paid a surprisingly severe dead toll to COVID-19 given their young population. At the other extreme, one finds East Asian countries such as Cambodia, Laos, Taiwan and Vietnam who have seen less than 1 person out of a million dying from COVID.
|Country||Number of cases||Number of deaths||Country population||Median age||Number of tests per million||Number of deaths per million people||Fraction of population fully vaccinated (%)|
|Czechia||1 555 245||27 169||10 689 209||42,10||1 124 762,08||2 541,72||5,46|
|UK||4 362 150||126 862||67 530 172||40,50||1 838 396,00||1 878,60||8,04|
|Italy||3 678 944||111 326||60 550 075||45,50||830 036,00||1 838,58||5,75|
|US||31 496 976||569 282||329 064 917||38,10||1 142 600,64||1 730,00||18,96|
|Peru||1 590 209||53 138||32 510 453||28,00||139 632,94||1 634,49||0,98|
|Spain||3 311 325||75 783||46 736 776||42,70||776 702,76||1 621,49||6,10|
|Mexico||2 251 705||204 399||127 575 529||28,30||44 447,86||1 602,18||0,92|
|Brazil||13 023 189||333 153||211 049 527||32,60||30 426,23||1 578,55||2,29|
|France||4 833 263||96 875||65 129 728||41,40||990 191,55||1 487,42||4,77|
|Colombia||2 456 409||64 293||50 339 443||30,00||253 332,40||1 277,19||0,78|
|Germany||2 903 036||77 653||83 517 045||47,10||602 631,39||929,79||5,19|
|South Africa||1 552 416||52 995||58 558 270||27,10||167 420,78||905,00||0,46|
|Iran||1 963 394||63 506||82 913 906||30,30||142 853,34||765,93||N.A.|
|Israel||834 222||6 253||8 519 377||29,90||1 762 862,71||733,97||57,06|
|Russia||4 597 868||101 106||145 872 256||39,60||829 284,63||693,11||3,18|
|Canada||1 014 374||23 118||37 411 047||42,20||742 294,52||617,95||1,92|
|Turkey||3 529 601||32 456||83 429 615||30,90||465 323,91||389,02||8,63|
|Saoudi Arabia||393 377||6 704||34 268 528||27,50||443 097,85||195,63||N.A.|
|Indonesia||1 542 516||41 977||270 625 568||30,20||31 577,32||155,11||1,48|
|Philippines||812 760||13 817||108 116 615||23,50||89 174,22||127,80||0,03|
|Maldives||25 053||67||530 953||28,20||1 218 680,37||126,19||N.A.|
|Egypt||205 732||12 210||100 388 073||23,90||N.A||121,63||N.A.|
|India||12 686 049||165 577||1 366 417 754||28,10||179 968,82||121,18||0,79|
|Nepal||278 210||3 036||28 608 710||24,10||79 540,11||106,12||N.A.|
|Japan||485 325||9 231||126 860 301||47,30||73 196,41||72,77||0,19|
|Pakistan||696 184||14 924||216 565 318||23,80||47 549,37||68,91||N.A.|
|Myanmar||142 511||3 206||54 045 420||28,20||45 929,70||59,32||N.A.|
|Bangladesh||644 439||9 318||163 046 161||26,70||28 816,01||57,15||N.A.|
|Malaysia||353 329||1 300||31 949 777||28,50||241 358,49||40,69||0,87|
|Australia||29 377||909||25 203 198||38,70||619 493,61||36,07||N.A.|
|South Korea||106 230||1 752||51 225 308||41,80||150 658,79||34,20||0,05|
|Hong Kong||11 532||205||7 436 154||44,40||1 505 304,09||27,57||1,21|
|Sri Lanka||93 595||581||21 323 733||32,80||114 159,47||27,25||N.A.|
|New Zealand||2 531||26||4 783 063||37,90||397 844,44||5,44||0,34|
|Singapore||60 519||30||5 804 337||34,60||1 472 407,44||5,17||8,06|
|China||90 341||4 636||1 433 783 686||37,40||N.A||3,23||N.A.|
|Thailand||29 571||95||69 037 513||37,70||45 091,41||1,38||0,07|
|Cambodia||2 915||22||16 486 542||25,30||N.A.||1,33||N.A.|
|Bhutan||896||1||763 092||27,60||768 579,67||1,31||N.A.|
|Taiwan||1 050||10||23 773 876||40,70||8 035,80||0,42||N.A.|
|Vietnam||2 658||35||96 462 106||30,50||25 733,44||0,36||N.A.|
|Laos||49||0||7 169 455||23,00||N.A.||0,00||N.A.|
The following map shows how unequal the lethality of COVID has been across countries.
A common explanation put forth to explain the diversity of the countries in terms of their Covid death toll is their age structure (COVID tends to hit more severely old people). Hence, one could expect countries with a young population to pay a lower death toll to COVID than countries with an older age structure. Figure 3 below shows that this impression is corroborated by the existing data, even though the relationship 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 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 the direst consequences of COVID mortality.
Another commonly 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 in Figure 4 below, countries that pay the largest death 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 2.
What about vaccination?
Vaccination has started in December 2020, albeit at a various pace in the different countries in the world. As of now, almost 147 million people have been completely vaccinated worldwide (which means, for most vaccines, receiving two shots). The following picture, which shows the evolution of this number of “completely vaccinated” vaccinated people suggests that the vaccination effort has not yet been sufficient to affect the dynamic of the epidemic. Indeed, the steep increase in the total number of fully vaccinated people worldwide observed at the beginning of January does not seem to have impacted the decreases in the daily number of cases already observed before the beginning of the vaccination.
However, the Israel experience may make one somewhat optimistic about the impact of vaccination if performed at a very large scale. Indeed, Israel is by far the country in the world that has vaccinated the largest fraction of its population (above 50%). The following picture, showing the evolution of the total number of totally vaccinated people (in thousands) and the daily number of new covid cases, is suggestive of a clear impact of the former on the latter. What is unclear however is the duration of the protection that vaccines are likely to provide against COVID-19.
Nicolas Gravel, Director of the CSH.
Regarding the CSH scientific production linked to the topic:
- Nicolas Gravel in conversation with Ms. Abantika Ghosh “InResearch” on the topic Why does the lethality of COVID-19 differ so much across countries? The recording of the interview is available at https://fb.watch/4Q3SSw6A8S/
- Olivier Telle‘s contribution to the CNRS podcast series on COVID-19 (in French): https://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
- The 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
- Bina Agarwal in a Mint podcast talking about the impact of the pandemic on inequalities in India: https://www.htsmartcast.com/single-episode/business/covid-19-widens-indias-inequality-divide–7722867/
- Another excellent and detailed analysis can be found here: https://cepr.org/sites/default/files/policy_insights/PolicyInsight102.pdf
- Nicolas Gravel also gave an interview about the application of economical science tools to help decision making in that pandemic era, in the French newspaper Le Monde (in French): https://www.lemonde.fr/idees/article/2020/09/04/on-peut-s-interroger-sur-l-adoption-de-politiques-de-confinement-qui-paralysent-les-economies_6050976_3232.html