By Atanu Biswas
As Covid-19 emerged, with tons of easily available data, academic journals and the media were flooded with lots of contradictory predictions of Covid-19 numbers and the onset times of different waves. Most of these proved utterly wrong eventually. There is a new trend now—estimating excess death from Covid, which is primarily non-verifiable, and thus even the weird estimates can hardly be proved wrong. Funny indeed!
‘Excess death’ is not an alien concept though. When Covid ravaged Italy in 2020, one of my Italian acquaintances wrote me an e-mail: “Many people are dying, who have never been checked for Covid-19, so they do not enter the official counts.” Well, is it only Italy’s story? Possibly not.
There’s reason to believe that many Covid-related deaths were not recorded as ‘Covid’ elsewhere—not a fault of the concerned authority though. No country was prepared for the pandemic . Because of the initial scarcity of test-kits, overwhelmed healthcare system, and, of course, social stigma, such under-reporting is inevitable—especially if the concerned disease is highly contagious in nature.
Defining ‘excess deaths’ is a tricky task though. Leading magazines and top medical journals argued that those who had Covid but might have died on a similar timeframe from other ailments should be excluded from the count. However, it is advocated that people who have died of preventable causes during the pandemic—because the overwhelmed health infrastructure could not treat them—should be attributed to Covid. By the same token, quarantine measures might have reduced deaths from accidents and occupational injuries, and also social distancing led to a decrease in deaths from flu-like diseases. These should also be kept in mind while estimating the Covid number.
The World Health Organization’s recent estimate of more than 4.7 million Covid deaths in India—nearly 10 times higher than official records suggest—sparked controversy. There are other estimates though. A University of Chicago study even estimated 6.3 million excess deaths in India during the pandemic through August 2021. In fact, a March 2022 Lancet article estimated that 18·2 million deaths worldwide. The dynamic estimate made by The Economist magazine shows about 14.7-25.1 million global excess deaths, which is 2.3-4 times the official toll so far. The US Centers for Disease Control and Prevention estimated more than 1 million excess deaths in America also.
Such widely varying estimates are based on various government and private data, and different types of models. Understandably, Covid data from different sources contain errors and biases of different magnitude and directions. And estimating anything, in general, based on possible biased data of unknown nature is subject to error of an unknown amount. Partial data is another serious problem. For example, if Covid mortality in Maharashtra or Kerala is used to draw a pan-India picture, that would provide a highly biased estimate.
Data-based conclusions are not unique, we know. Interestingly, the above-mentioned Lancet article used six models to estimate expected mortality; final estimates were based on an ensemble of these models. Well, couldn’t other models give different estimates? Nobody really knows how appropriate a model is, in general. ‘Wisdom’, certainly, plays an important role in choosing an ‘appropriate’ model.
Although it may sound surprising, to estimate Covid’s excess death for any country, Covid-data may not be needed at all. All-cause mortality data during the last few years may suffice, if it is available and credible. One may build a ‘suitable’ time series model for the total number of deaths up to 2019, keeping the trend and the possible disasters in the country during these years in mind. Had there been no Covid, the expected number of deaths in 2020 and 2021 could be obtained from the fitted model. If we substract this from the total all-cause death figure of 2020-21, that would give the estimated Covid number. In fact, using Covid-data for this purpose might even induce an error of unknown magnitude. However, the all-cause deaths may not be fully recorded or released in a timely manner, or even be questionable in a country. With so many contradictory and widely varying estimates, finding Covid’s exact death toll may be like chasing an unknown in an unknown direction. And that would possibly remain elusive, unless a credible large-scale survey is conducted all over the country.
Yes, one may recall a seven-decade-old history. During the Bengal famine in the 1940s, Prasanta Chandra Mahalanobis, India’s ‘Plan Man’, conducted a large-scale sample survey of famine-ravaged villages, for causal analysis, and to assess the extent of the disaster and an estimate of the number of people affected. The survey provided very useful findings such as one-fourth of the number of families (1.5 million people) who had owned rice land before the famine had either sold in full or in part such land or had mortgaged it, and the economic position of nearly 4 million people deteriorated during the famine. Well, only a credible survey like that might bring out the actual Covid story clearly. It is not easy though—Mahalanobis’s shoes are yet unfilled. Contradictory pictures of the Covid-disaster would continue to create buzz.
The author is Professor of statistics, Indian Statistical Institute, Kolkata.