“Never try to walk across a river just because published data informs you that it has an average depth of four feet.”

—Martin Friedman

I have just gone through this learned study titled “Three New Estimates of India’s All-Cause Excess Mortality during theCOVID-19 Pandemic” by USA-based Abhishek Anand, Justin Sandefur, and Arvind Subramanian. (It is a free download at Centre for Global Development: https://www.cgdev.org/publication/three-new-estimates-indias-all-cause-excess-mortality-during-covid-19-pandemic )

I extend my fervent thanks to the authors for this entertaining study. I do believe it is hilarious enough to make a coronavirus cackle in delight.

I say this in all seriousness and sobriety, and with all my authority as a science scholar of international disrepute from the prestigious North Eastern Hill University, who has plumbed unique and unparalleled depths of non-achievement in the most obscure and abstruse disciplines.

The Introduction to the study begins with a most earnest declaration: “We want to emphasize that we are not estimating Covid-caused deaths as CPHS has no information on cause of death. Rather, we focus on all-cause mortality, and estimate excess mortality from the onset of the pandemic relative to a pre-pandemic baseline, adjusting for seasonality.” [emphasis mine]

Alas, the study proceeds to do exactly what it declares it does not aim to do. It estimates Covid-caused deaths in India.

In fact, it concludes that while India’s official Covid death count as of end-June 2021 was 400,000, the actual death toll ( ‘excess deaths’) in India are between 3.4 million and 4.9 million.

To put it plainly, the study concludes that India is hiding dead bodies. Millions of dead bodies.

How does the study arrive at this conclusion?

Well…

For its first estimate, the study blithely extrapolates death data from just seven Indian states to the whole of India to estimate under-counted deaths or what it calls ‘excess deaths’.  In other words, the study decides that the averages of death data from Andhra Pradesh, Bihar, Chhattisgarh, Karnataka, Kerala, Madhya Pradesh, Tamil Nadu and Uttar Pradesh must be applied to the whole of India— comprising 28 states and 8 Union Territories—to figure the actual ‘excess deaths’ in India. And it arrives at the estimate of 3.4 million excess deaths.

For the second estimate, the study applies “international estimates of age-specific infection fatality rates (IFR)” to India.  To translate this gobbledygook into English:  the study assumes that because the infection fatality rate (IFR) is incredibly high in USA, EU countries and so on, then that incredibly high fatality rate must be the norm that India must obey too. And so, it blithely extrapolates IFR from USA, EU and other high-Covid-fatality nations to India to estimate around 4 million excess deaths.

For the third estimate, the study analyses data from the Consumer Pyramid Household Survey (CPHS). With admirable candour, the study admits that “There is reason for caution when relying on the CPHS for mortality estimates though. While CPHS has become a critical source of timely information on labour market and consumption trends, especially in the absence of timely and reliable official data, its representativeness has recently been questioned.” But that admission has not prevented the authors from relying on CPHS data to accuse India of hiding an  estimated 4.9 million excess deaths.

 In a nutshell – a very old and shriveled peanut shell – this study rests on the three very shaky pillars of incoherence, irrelevancy, and plain immaturity , reinforced with a strong foundation of meaninglessness.  

The kindest thing I can say about this study is that what it lacks in scientific rigor, it more than compensates with deep-set rigor mortis.

In passing: I wonder why the learned authors did not apply their IFR-based logic to the People’s Republic of China where the Covid-19 virus was actually born (or created), and whose Covid-related data is rather questionable to put it mildly? 

Especially when China, with a population of 1401,000,000 (give or take a few million Uighurs and Tibetans), officially reports a mere 4636 deaths out of a minuscule 92,364 cases – thus finding itself placed at 103^rd  position in the Worldometer’s country list —below countries such as Montenegro at position 100 (pop. 628,150; total cases 100,755; deaths 1623) and Cyprus at position 102 (pop. 1,216,583; total cases 93,247; deaths 391)?

I place below a simple table, with an accompanying graphic, that I hope will inspire Abhishek Anand, Justin Sandefur, and Arvind Subramanian to take a long and very hard look at China’s Covid-19 data and assess the People’s Republic’s ‘excess deaths’ with the rigorous rigor mortis they’ve applied to India.  Please note that I have renamed the ‘serial number’ column as ‘Rank of Shame’ to mirror the spirit of this Olympics season and the spirit of Abhisekh et al.’s study: ((all data from Worldometer as on 22 July 2021, 1000 hours IST) :

Rank of Shame	Country	Total cases	Total deaths	Population
1	USA	35,146,476	625,808	333,045,503
2	India	31,256,839	419,021	1,394,272,063
3	Brazil	19,474,489	545,690	214,149,270
..and many many countries later…
100	Montenegro	100,802	1624	628,150
102	Cyprus	94,261	394	1,216,607
103	China	92,414	4636	1,439,323,776

In conclusion, I am grateful to the authors of this study for underlining two extraordinary and enduring Laws of Credibility that are adopted instinctively by vast swathes of Indian academia and Indian media.

Law 1. Scholars, especially Indian-born scholars, are far more capable of discerning ‘facts’ and analysing ‘data’ on India when they are sitting 11,000 kilometers away from India, than if they based themselves in India and did hard data gathering and field work in India.

Law 2. The credibility and worth of any academic research focused on India is directly proportional to the distance of the researchers from India; and the credibility and worth of the research increases exponentially if the researchers are located in a generally Westward direction from India.

Jai Hind. Hail Comrade Xi.